Growthbook Release Notes

Highlights

This is our biggest release yet! Over 400 PRs closed.

• Cmd+K command pallets for universal search
• Product Analytics explorers and AI Data Analyst (beta)
• Multi-environment feature rules
• Overhaul of feature approval flows and revisions
• Feature flag ramp schedules
• Bandits without sticky bucketing
• Pre-Exposure bias checks
• Huge REST API refactor with tons of new endpoints for metric groups, teams, experiments, features, namespaces, and more

Other Changes

• Holdout scheduling
• Feature revision comparison tool
• Attribute/Identifier type mapping support
• Lookback override option for experiment analysis
• Improved warehouse metadata and tagging for SQL query cost attribution
• Improved stale feature algorithm and UX
• Support for BigQuery reservations
• API keys with arbitrary roles (not just admin or readonly)
• New Saved Group approval flow
• Option to include additional metadata in SDK payloads
• Support for incremental refresh of quantile metrics using KLL sketches
• Ability to disable API keys and track last usage date
• Huge SQL generation refactor (internal, no user-facing changes)
• Better filtering on Insight dashboards
• Namespaces overhaul
• Updated Presentations tool
• Plus tons of UX improvements, bug fixes, dependency updates, security patches, and performance improvements.

Thanks to all of the existing contributors: @Auz, @Kevin-Chant, @ahdriel, @bryce-fitzsimons, @fsarachu, @gazzdingo, @itsgrimetime, @jdorn, @jrnold, @lukebrawleysmith, @lukesonnet, @madhuchavva, @mknowlton89, @msamper, @natasha-growthbook, @nhat-growthbook, @nodirnasirov, @oelshaikh, @royalfig, @tzjames and a big thanks to all of the first time contributors:

• @estrattonbailey made their first contribution in #5283
• @teresayung made their first contribution in #5251
• @nadapzy made their first contribution in #5396
• @dannylin-ant made their first contribution in #5504
• @HampusPoppius made their first contribution in #5572
• @saurabhkashyap-ui made their first contribution in #5564
• @olu-an made their first contribution in #5604
• @csbailey5t made their first contribution in #5614
• @discorev made their first contribution in #5600
• @aiSynergy37 made their first contribution in #5639
• @lillialjackson made their first contribution in #5721
• @nielskaspers made their first contribution in #5629
• @johnham-ant made their first contribution in #5793
• @adittya-upstart made their first contribution in #5778
• @jakemainwaring22 made their first contribution in #5698
• @anna-yn made their first contribution in #5920

Original source

The safety net for rapid AI development

AI has empowered developers to build new features faster than ever. What used to take a week can now be shipped in a day. The agentic era has transformed the way engineers work, but shipping fast without the proper guardrails can quickly lead to incidents. GrowthBook Feature Flags set your whole team up for the pace and volume of today’s development lifecycle.

Feature flags allow modern teams to ship fast while maintaining control. The best practice is straightforward: wrap every feature behind a flag so you can ship at scale and roll back the moment something breaks. But as teams scale and AI agents become more deeply embedded in workflows, ad-hoc flag management breaks down.

Without the right metrics monitored on every release, something that breaks in production could go undetected for weeks or even months before you realize the problem. By that point, dozens of other features may have shipped, making it difficult to identify which change is actually the culprit. Smaller failures compound the problem. Someone forgets to increment a rollout. A flag that should have been deleted six months ago is still sitting in production. Each rollout follows a different process depending on who is running it.

Consistency and guardrails are what enable teams to safely keep pace with the compressed development cycles that come with AI coding. Feature flags should be baked into the agent's coding workflow for every new feature, creating a safety net where guardrail metrics monitor performance and auto roll back if something degrades.

With that net in place, teams can move quickly, confident that bad releases will get caught early and rolled back. Humans can then focus on where judgment is actually needed, such as deciding which guardrail metrics matter, reviewing whether the rollout plan aligns to the risk, and approving changes that warrant human review.

Modern teams need enterprise-class flag management with the controls and governance to ship safely at AI speed. That’s where GrowthBook comes in.

GrowthBook 4.4 extends our feature flag platform with 3 major new capabilities: release plans with automated ramp schedules, configurable approval workflows, and enhanced stale feature flag detection through expanded REST API and MCP server endpoints. 4.4 also includes SDK cache improvements, metadata in SDK payloads, a namespace overhaul, and more. Together, these controls turn feature flag management into a repeatable, scalable practice that lets you move quickly while de-risking every release.

Release plans with ramp schedules: Standardize and automate your rollout

In 4.4, we’re introducing release plans with ramp schedules: automated, staged rollout plans attached to a feature flag. Release plans make it fast and easy to define a standardized schedule and rollout process into a reusable template that everyone on your team can follow, with guardrails built in so safety is a standard part of how features are released.

You define the stages, set the percentages, time intervals, and guardrails, and GrowthBook executes the plan automatically. Choose from preset templates or build your own to target specific user groups and attributes. You can also gate individual stages of your release plan by prerequisite features or by the specific feature value a user is currently assigned.

With manual feature rollouts, you can run into two types of problems. Either someone forgets to increment the percentage, and a feature sits at a given stage indefinitely. Or someone moves too fast, and a problem that should have been caught at 10% instead hits 50% of users. Release plans keep rollouts from stalling at an early stage or accelerating past the point where a problem could have been caught. Build in approval requirements at specific stages based on your risk tolerance, requiring the rollout to pause for review and manual approval before advancing. You can also attach guardrail metrics so the feature auto-rolls back if any of them degrade, catching problems automatically between approval gates.

Best practices for designing a release plan

Guardrails help your team feel confident about shipping, and combining them with human approval gates ensures there are no gaps.

1. Start simple and don't over-engineer your first release plan.
2. Build out your process as you go, learning what works and what doesn't with each release.
3. Choose guardrail metrics your team is aligned on
4. Pair guardrail metrics with approval gates where it makes sense.

Once you have a process that works, standardize it as a default template with versions for different risk profiles or product needs (high risk, low risk, internal-only, etc.) Treat these templates as living artifacts and evolve them as your team learns what works and what areas need improvement.

Sample release plans

Rollout processes vary by team, product, and risk tolerance. Release plans are flexible enough to fit whatever process your team uses and ensure every rollout follows that process consistently, no matter who is running it. Below are two of the most common patterns we see, and how to structure a release plan for each.

Example 1: Simple percentage rollout

A simple percentage rollout is where you expose a small percentage of users before gradually going wider. Set approval gates at key checkpoints to enforce a metrics review and manual approval before committing to broader rollout.

Example 2: Segmented rollout

A segmented rollout lets you control who gets access first, reducing risk to your highest-value users. For instance, you may opt to roll out a feature to free users before paid. Free users are valuable, but the risk of churn or revenue impact is lower than with paying customers. By the time you're ramping up paid users, you've already caught the obvious issues.

You may segment by any attribute available in GrowthBook, including location, device type, browser, user group, and more. Start simple or build in as much complexity as you need. You can also apply guardrails to specific stages; for example, you might be okay rolling the feature out to free users, but want it to auto-rollback if metrics degrade when it hits paid users.

The release plan with ramp schedules feature is available in the GrowthBook Pro and Enterprise plans.

Flag revisions, approvals, audit logs: maintain control and visibility

Feature flag revisions

When someone changes a feature flag, a draft revision can be submitted for review to approve or request changes. On approval, the change is published to the SDK. Nothing goes out unreviewed unless you want it to.

Flag revisions provide a complete audit trail, capturing who changed a flag, what they changed, and when they changed it. When something breaks, you get instant visibility into what recently changed. As multiple people manage flags over time, revisions also preserve the intent and context behind each change.

GrowthBook maintains a version history of all previous revisions of a feature flag over time, so you always maintain a clear picture of how a flag has evolved over time.

The revision feature is available for feature flags in all GrowthBook plans.

Feature flag approval workflows

Approval requirements are configurable per project and per environment, giving you the flexibility to require review gates where they make sense. For example, you can require approvals for production while letting staging updates flow freely.

GrowthBook 4.4 expands the scope of approval requirements. Previously, approval workflows applied only to changes to rules and values. Now, you can also require approvals for environment kill switches, pre-requisites, saved groups, and metadata changes. This gives more granular control over what can happen without a review.

These approval workflows help teams move fast with the right safety checks at the right time, making governance tunable to your team’s needs. This level of control is especially important when agents are acting on your behalf. Agents can create drafts for all these change types and require human approval. The separation is clear and enforced, so your governance is applied to the full scope of what an agent can touch.

The feature flag approval workflow feature is available in the GrowthBook Enterprise plan.

Audit logs

For teams with compliance requirements, the audit log provides the paper trail without any extra process. They provide a timestamped record of every feature flag event, including changes, approvals, publishes, creation, and more.

You may also expand for a more detailed view of the specific changes made for each event.

The audit log feature is available in all GrowthBook plans.

Stale feature flag cleanup with agents

GrowthBook categorizes a feature flag as stale if it hasn't been updated in 2 weeks and is not active in any environment, or if there is a one-sided rule that sends 100% of traffic to a single variation.

GrowthBook 4.4 also introduces support for teams using AI coding tools like Claude Code or Cursor to detect and remove stale flags that have outlived their purpose. Using GrowthBook's MCP server or REST API, you can prompt an agent to surface every stale flag in your environment, returning it as a reviewable table with additional context on why the flag is being surfaced as stale. Once you've decided which flags to remove, write a follow-up prompt asking the agent to locate and remove those flag references from your codebase to eliminate technical debt in one pass.

The same endpoints can also surface ambiguous flags that don't definitively meet the stale definition, but show signs they may no longer be in use. Examples include: flags with no rules defined, abandoned drafts, or disabled environments. Results come back as a table you can review to decide which flags genuinely need cleanup and which are still doing real work.

Stale flags create real risk: technical debt, performance overhead, and accidental production changes. Stale and ambiguous feature flag detection together help simplify flag hygiene by surfacing the flags worth reviewing, with enough context to tell what's truly orphaned and what's still needed in production.

The stale feature flag cleanup feature is available in all GrowthBook plans.

REST API and MCP server endpoints

In 4.4, we expanded our REST API and MCP server endpoints across the feature flagging surface. Anything your team can do in the GrowthBook app, AI agents can now do through the API or MCP server: create flags, manage revisions, configure release plans, set targeting rules, locate stale flags, and more.

The same permissions, approval workflows, and audit logs apply to every API- or MCP-prompted action. Whether a human triggers a change from the UI or an agent runs it from your editor, it goes through the same review gate and shows up in the same audit log. Agents get the same platform and the same accountability as your team, with the scoping and guardrails calibrated for how they work.

The REST API & MCP Server endpoints are available in all GrowthBook plans.

Built for the way modern teams ship

The pace of development has fundamentally changed with AI, and the controls around how teams ship need to evolve with it. Teams need to move fast, test everything, ship safely, and roll back at the first sign of trouble.

GrowthBook 4.4 gives engineering and product teams the building blocks to do exactly that: a repeatable rollout process accessible through the app, REST API, or the MCP server. Whether the change comes from an engineer, a product manager, or an AI agent, the process holds.

Original source

Experimentation: An API-first lifecycle

We got a little carried away. 417 pull requests and 376,798 lines later, GrowthBook 4.4 delivers a rebuilt API with broad coverage to support programmatic use of GrowthBook, a conversational AI Data Analyst for self-serve product analytics, and ramp schedules that make targeted, time-based rollouts repeatable and safe.

Writing software with AI agents is the new standard, and this release fully positions teams to take advantage of new approaches to developing and shipping products with AI. We’ve rebuilt the foundations of our API and extended its coverage so that teams can work with GrowthBook however they choose, whether through the UI or agentic tools. We work with the leading AI companies, and we’re excited to keep building out features that make it easier to ship faster, more confidently.

Version 4.4 is available immediately to both our cloud and self-hosted users.

Experimentation has assumed a human is in the loop at every step:

• Writing the hypothesis
• Configuring the test
• Reading the results
• Deciding what to ship.

That assumption is breaking down.

Teams are pushing experimentation into agent-assisted and automated workflows. With GrowthBook 4.4, you can work programmatically at any stage of the lifecycle you choose to, from setup and operation to decision.

In 4.4, the REST API surface was rebuilt on Zod-driven endpoints. Zod is a schema library: define the shape of an API once, and the TypeScript types, runtime validation, and OpenAPI docs all generate from the same definition. They can't drift apart. That makes the experiment lifecycle a real programmable workflow instead of a collection of endpoints with a spec that lags behind reality.

Set up and launch experiments from a template

Templates encode your organization's standards: statistical methods, metric selection, guardrails, and the defaults a practitioner would otherwise select manually. Agents create experiments against those templates over REST. You get consistent experiment design at agent speed, with your rigor baked in.

In 4.4, an agent can pull the pre-launch checklist, see what passes and fails, and mark manual items complete. That covers automated checks for metrics defined, variations configured, and targeting set, plus any manual checks your team has added.

Observe experiments at the scale of your program

Instead of clicking through the UI to view every running experiment, your agents make a single endpoint round-trip. That call returns estimated completion times, metric direction, and any guardrail issues for every experiment at once. Snapshots, status, and results work the same way: one call across the program, not one per experiment.

Analyze, decide, and ship

Reports are a new first-class API resource in 4.4. An agent can build a persistent analysis from an experiment, customizing metric overrides, analysis settings, or dimension breakdowns. The report stays put, and doesn't get overwritten by the next snapshot. An agent can hand a report URL to a stakeholder, refresh it on demand, and link back to it from a write-up.

From the report, the agent reads results against your team's decision criteria and concludes the experiment programmatically, setting the winner, results, and analysis summary in one call.

Bandits without sticky bucketing

Multi-armed bandits fit when you want continuous learning and traffic reallocation without the time cost of a full A/B test. That profile shows up frequently in AI application work: model selection, prompt comparison, response ranking. Until now, adopting bandits in GrowthBook meant setting up sticky bucketing first. Now, bandits are easier to set up, and we expect that teams building AI-powered products will use them extensively.

Product Analytics: Structured exploration with an AI-native interface‍

Analytics has three different bottlenecks, and traditional tools built around SQL and a dashboarding UI don't address any of them well.

• Non-data teams need to self-serve analysis on product metrics without queuing behind the data team.
• Repetitive review workflows run the same explorations over and over.
• Agents in the loop need programmatic access to metric data data, fact tables, and experiment results.

All three converge at the same place: the data team becomes a bottleneck for work that structured, repeatable tooling should handle. GrowthBook 4.4 introduces a new AI Data Analyst to improve self-serve analysis, and makes it explore data without SQL.

AI Data Analyst (beta)

The AI Data Analyst is a conversational AI assistant for Product Analytics. Ask a question in plain language, like “What’s our DAU trend by country” or “How is our model playground engagement rate compared to API key creation,” and get back charts and insights built with tooling specific to GrowthBook’s metrics, fact tables, and data sources. Product managers, marketers, and others can self-serve their questions without writing SQL or filing tickets with the data team.

It's in beta, and we want to hear how you're using it. Ask it real questions, try out your actual workflows, and let us know what we can improve.

Explorations

Suppose you want to understand the change in daily active users of a new search feature, broken out by plan tier. GrowthBook’s Explorer lets you create charts (Explorations) from your warehouse data through a visual interface. Select your metric, dimensions, and date range to generate a chart that you can share or save. Or run the same exploration over the API and get the chart data back, plus a deep link to open it in GrowthBook.

Product Analytics over MCP and API

Every exploration is now addressable through the MCP server and REST API. Queries run the same way whether they come from a person clicking through the UI or an automated workflow calling the endpoint.

Feature flagging: Safer flags, for whoever's shipping them

AI-assisted software development has rapidly increased the amount of code written and features deployed. Teams need flag infrastructure and governance that scale with the automation, so that problems like premature rollouts and flag sprawl don’t scale right alongside feature development.

GrowthBook 4.4 includes safety and governance features to prevent these failure types, and automated stale flag cleanup to mitigate flag sprawl in existing codebases.

Ramp schedules

Targeted releases now include automated, time-based rollouts with fine-grained controls and optional approval steps. Each step supports its own targeting in addition to a traffic percentage, so a canonical rollout might look like:

1. Internal employees for 24 hours
2. Free tier at 25%
3. Free tier at 100%
4. Paid tier at 25%
5. Paid tier at 100%,

This is particularly useful for AI model and prompt deployments, where you want time between each exposure before widening the blast radius. With monitored ramp schedules, you can attach guardrail metrics and automatically hold, advance, or roll back releases. With reusable templates, teams codify standard rollout shapes they want to apply to new features quickly.

Revisions and approval controls

Approval flows are built directly into the flag change lifecycle, with REST coverage (in beta) for revisions, reviews, and approvals. When a change comes in, it goes through the same review gate regardless of origin. The same guardrails apply whether the change comes from a teammate or an automated process.

Fully automated stale flag cleanup

Engineering teams often find their codebases bloated with quietly accumulated stale flags, which drive significant technical debt. Cleanup is handled in occasional tech debt sprints or just perpetually deferred.

This release adds an endpoint for stale flag detection, which lets an AI agent run an entire cleanup process. An agent or tool can pull stale flags by API, provide a reviewable list, then find and clean up flag references in your codebase as you decide what to remove. Automating the cleanup process removes a major source of compounding debt, especially for large enterprise teams.

Read more on what's changed, including feature flag change comparisons, audit logs, and approval workflows in our feature flag deep dive.

Ship, test, measure, and decide at a new pace

GrowthBook was built for teams that ship product with discipline: engineering teams with release processes, data teams defining metrics and guardrails, and product teams trying to move fast without losing rigor. Our 4.4 release extends the same principles to the agents now working alongside all of them.

The changes across experimentation, feature flags, and product analytics connect. A programmable experiment lifecycle, analytics you can query by agent, and flag infrastructure with real approval controls form one continuous workflow: ship, test, measure, decide. Whether a product team runs it manually or an automated pipeline runs it continuously, the underlying system works the same way.

Faster iteration. Same rigor and safety.

What's next

• See the full 4.4 release notes →
• Read the feature flagging deep dive →
• Talk to our team →

Original source

Release Highlights

Experiments

• Post-Stratification - uses dimensions to reduce variance and get to statistical significance faster.
• Result Filters - quickly narrow which metrics are visible by tag, group, or slice.
• Result Drilldowns - view additional info by clicking on any row in the results table
• Daily Participation Metrics - a new metric type that's analogous to Daily Active Users (DAU)
• Fact Table Filtering - more flexible filtering UI when defining metrics
• Query Performance - increased number of metrics that can be combined in a single SQL query, reducing warehouse costs.

Feature Flags

• Nested Saved Groups - more powerful and composable targeting logic
• Case Insensitive Targeting - match things like email addresses where case doesn't matter
• OR Targeting Conditions - build complex targeting rules without using advanced mode
• Feature Diagnostics - warehouse native tool to debug real world flag usage in your application
• New Rust and Roku SDKs - use GrowthBook inside performance critical back-ends and on millions of TVs
• Performance and UI Improvements - faster load times and snappier UX in the GrowthBook app when there are thousands of feature flags and large JSON values.

Other

• Fix broken browser back button on many pages in the app
• New API endpoints to manage experiment dashboards and custom fields
• New Project Admin role to make it easier to manage a large distributed team
• New Custom Hook option to only validate incremental changes
• Kerberos auth support for Trino/Presto
• Support additional AI models from Anthropic, Gemini, xAI, and Mistral
• Option to auto-update metric slice values
• UI improvements to data source page
• Incremental refresh bug fixes and improvements
• Product Analytics fixes and improvements
• Plus many other bug fixes and performance improvements
  Note: Internally, we migrated from yarn to pnpm. We included a small shim script for self-hosted users, so any Docker commands (e.g. yarn start) should continue working without modification unless you are doing something very custom and bespoke.

New Contributors

• @siarheipashkevich made their first contribution in #5008
• @vovapyc made their first contribution in #5050
• @hq6 made their first contribution in #5074
• @oelshaikh made their first contribution in #5031
• @fsarachu made their first contribution in #5166

Plus a ton of returning contributors:
@jdorn @ahdriel @royalfig @Kevin-Chant @mknowlton89 @lukesonnet @tzjames @madhuchavva @bryce-fitzsimons @msamper @itsgrimetime @august-growthbook @Hephaest @lukebrawleysmith @Auz @natasha-growthbook

Full Changelog: v4.2.0...v4.3.0

Original source

GrowthBook 4.3 Release: Run experiments with 20% more traffic using post-stratification, metric drilldowns for deeper insights, and feature evaluation diagnostics. Plus Rust & Roku SDKs.

At GrowthBook, we're focused on helping you learn faster and ship with confidence. GrowthBook 4.3 delivers on both fronts, with post-stratification to reach statistical significance sooner, metric drilldowns to understand results more deeply, and feature evaluation diagnostics to verify your flags are working correctly in production.

GrowthBook 4.3 is now available to all cloud and self-hosted users.

GrowthBook 4.3 Live Demo with Q&A
Faster Experiments, Deeper Insights

Experiment Analysis

Post-Stratification (Enterprise only)

Experiment analysis now supports post-stratification, a powerful variance reduction technique that produces more precise results.

Here's the idea: if you know revenue varies by country, post-stratification uses that information to isolate the treatment effect from between-group noise. The result is tighter confidence intervals from your existing traffic. In the right conditions, CUPED + post-stratification can be equivalent to running your experiment with 20%+ more traffic!

Configure post-stratification at the organization level under Settings → General, or override it at the metric or experiment level. To enable it, you'll need to have pre-computed dimensions configured in your experiment assignment query.

Post-stratification is available to Enterprise customers. CUPED (without post-stratification) is available to Pro and Enterprise customers.

Experiment Metric Drilldowns (All editions)

Understanding experiment results just got a lot easier. Click any metric row to open a Metric Drilldown, a focused view with everything you need to interpret that metric without jumping between pages:

• The Overview tab shows metric details, time series, and a results table with analysis controls.
• The Slices tab lets you see how your metric breaks down across different values.
• The Debug tab reveals how CUPED, post-stratification, capping, and priors are affecting your numbers.

Metric slices are an Enterprise feature. See Metric Slices for configuration details.

Experiment Result Filters (All editions)

Experiments with dozens or hundreds of metrics can be overwhelming to review. You can now filter results by tag, slice, or metric group to focus on what matters.

Once you find a view you like, use Add to Dashboard to save it for later and share with your team. We also cleaned up the results UI to reduce clutter and keep the focus on your data.

Daily Participation Metrics (All editions)

We added a brand new metric type: Daily Participation. For each user, this measures the fraction of days they were active while enrolled in the experiment (active days ÷ days exposed), then averages that value across users in each variation.

Think of it as DAU normalized per user and exposure window, but more stable for experiments than raw daily active user counts.

This is a really valuable metric for any website or app that is trying to grow daily usage.

Better Fact Table Filters (All editions)

Metrics are built on Fact Tables, and often you only need a subset of rows. This release adds a powerful filtering UI to define exactly which rows to include, without writing SQL.

Feature Flags

Feature Evaluation Diagnostics (All editions)

When a flag isn't behaving as expected, debugging can be frustrating: you're left guessing whether the issue is in your targeting rules, SDK configuration, or something else entirely.

Feature evaluation diagnostics solves this by querying SDK evaluation events stored in your data warehouse. See exactly what evaluated in production, not just what the rules say should happen. Troubleshoot targeting conditions, rollouts, and experiment rules with real data instead of guesswork.

Nested Saved Groups (All editions)

Saved Groups now support nesting, letting you define groups in terms of other groups. Build complex targeting logic while keeping base definitions centralized and reusable.

For example, combine "Beta Users" AND "Enterprise Plan" to create "Beta Enterprise Users." Update the base group, and nested groups update automatically.

This makes it faster and easier to create targeting rules for feature flags.

Case-Insensitive Regex Targeting (All editions)

New targeting options for case-insensitive regex and "in list" matches—useful for matching email addresses and other values where case shouldn't matter.

Available now in the latest JavaScript, React, Node, and Python SDKs. More SDKs coming soon.

Rust and Roku SDKs (All editions)

We're excited to announce two new official SDKs: Rust and Roku.

Rust is the language of choice for modern performance-critical applications. Special shout out to the community, who authored the initial version of this SDK.

GrowthBook, now on your TV? That’s right, the next time you watch your favorite show, GrowthBook might be working behind the scenes with the launch of our official Roku SDK, a leading smart TV platform that powers millions of streaming devices and TVs worldwide.

With these additions, GrowthBook now offers 23 SDKs spanning client-side, server-side, mobile, and edge.

Quality-of-Life Improvements

Big thanks to all of our users who reported bugs, shared feedback, and contributed ideas to this release on GitHub or Slack.

Many small improvements add up to a big boost in usability:

• Improved query performance for fact metrics
• Cleaner experiment results UI with fewer distractions
• OR targeting conditions
• Updated SDK support
• New API endpoints to manage experiment dashboards and custom fields
• New Project Admin role to make it easier to manage a large distributed team
• New Custom Hook option to only validate incremental changes
• Kerberos auth support for Trino/Presto
• Option to auto-update metric slice values
• Support for additional AI models from Anthropic, Mistral, xAI, and Gemini

Plus dozens of smaller fixes and performance improvements.

Join our webinar on February 19 at 10:00 AM PT for a live demo of 4.3 features and a Q&A session.

Original source

Release Highlights

Product Analytics (preview)

Turn your warehouse data and metrics into actionable product insights. Explore user behavior, share dashboards, and make smarter decisions about what to build next. This release is just the beginning and we have much more planned for Product Analytics in the coming months.

Metric Slices

Automatically break down metrics into key “slices”. This can drastically reduce the number of metrics you need to create and can help organize experiment results. For example, if you have an Orders fact table, you can enable “Auto Slices” on the product_category and device_type columns. Now, when you add a Revenue metric to an experiment, it will also include breakdowns for each of these, so you can easily compare mobile vs desktop revenue or revenue from different product categories. You no longer need to maintain 10+ separate metrics that are all slight variants of each other!

Incremental Refresh

We revamped our Data Pipeline Mode to drastically reduce data warehouse costs and increase query speeds for long-running experiments and high-traffic apps. We do this by storing intermediate results back into the data warehouse and incrementally refreshing it throughout the experiment. We’re starting with BigQuery, where we’ve seen some users save up to 85% in query costs.

Statsig Importer and Code Migration Tool

OpenAI recently acquired Statsig and the future of the platform is uncertain. Everyone is asking “will Statsig shut down?” We built an importer and migration tool so teams can be ready to switch to GrowthBook on a moment’s notice. Our Importer lets you instantly copy over all feature gates, dynamic configs, segments, and more. Our Code Migration tool (built on Claude Code) can seamlessly migrate your app from using the Statsig SDK to the GrowthBook SDK.

Official Metrics

Many companies have a core set of metrics that have been validated and battle tested over time. GrowthBook lets you mark these metrics as “Official”, which gives them a special badge in the UI, elevates them in the app, and locks down editing. We’ve had Official metrics for a while, but they had to be managed entirely through the API. In this release, we now let admins manage these metrics directly from the UI, making them more accessible and easy to use.

Custom Validation Hooks

GrowthBook has always been extremely customizable, but this release brings it to a new level. Self-hosted users can now write fully custom javascript code snippets that hook into our validation logic. Require feature flags to have tags, block targeting rules that contain PII, enforce custom naming conventions - the possibilities are endless. The validation code runs in a sandbox based on V8 isolates for ultimate security and performance. We only have a couple hooks to start, but we’d love to hear your ideas and use cases for additional ones!

New SQL Template Variables

You can now access custom field values and phase data within your metric and experiment SQL! This unlocks 3 important use cases:

1. Add highly custom query optimization by taking advantage of non-date partition keys.
2. Re-use the same SQL definitions when only minor tweaks are needed per-experiment.
3. More accurately join experiment exposure events to experiment phases.

Edge Remote Eval

Remote Eval lets client-side SDKs offload feature flag evaluation to a back-end server, which prevents leaking targeting logic to your users. Previously, this required running your own GrowthBook Proxy servers (and dealing with all of the associated infrastructure and maintenance). We now offer a simpler option based on Cloudflare Workers. Deploy a blazing fast and cheap Remote Eval server on Cloudflare’s global infrastructure with zero maintenance required.

Quality-of-Life Improvements

Big thanks to all of our users who reported bugs, provided feedback, and worked with us to make GrowthBook better. This release includes many quality-of-life improvements that, while small on their own, add up to make a big impact. Here are a few highlights:

• Brand new search algorithm for features, metrics, and experiments. Results are now much more relevant and search is faster overall, even when there are thousands of potential matches.
• Ability to create feature rules in multiple environments at once
• Better column-type detection for Fact Tables when using BigQuery
• You can now add metric row filters based on boolean columns in your Fact Tables
• We stopped sending feature.updated webhooks for unpublished draft changes (reducing noise).
• Slack and Discord notifications now include more details about the change in GrowthBook that triggered the notification.
• Custom Pre-launch experiment checklist items can now be scoped to specific projects.
• Much faster database schema browser when there are hundreds or thousands of tables
• New settings option to disallow creating legacy metrics. This can help ease the transition to using fact tables. There is more planned here so stay tuned!
• The experiment results table is now sortable, so you can quickly see the best (or worst) performing metrics based on either significance or lift.
• Complete revamp of the included sample data and experiments to better showcase the GrowthBook platform and all that it can do.
• Plus dozens of bug fixes and smaller improvements.

New Contributors

• @kosmoz made their first contribution in #4011
• @aalfalah made their first contribution in #4520

Plus a ton of returning contributors:
@ahdriel @gazzdingo @mknowlton89 @bryce-fitzsimons @Kevin-Chant @madhuchavva @royalfig @kosmoz @tzjames @lukesonnet @aalfalah @msamper @Auz @jdorn @nodirnasirov @Bohdan-Kim @lukebrawleysmith @natasha-growthbook

Full Changelog: v4.1.0...v4.2.0

Original source

GrowthBook 4.2 release

GrowthBook 4.2 introduces Product Analytics (beta), a Statsig migration kit, and enterprise-scale enhancements. One platform for features, experiments, and analytics.

At GrowthBook, our mission is to provide the insights you need to build better products that grow your business faster. With GrowthBook 4.2, we’ve added a beta version of GrowthBook Product Analytics. Now our users will have a single integrated platform for feature management, experimentation, and product analytics.

In addition, we’ve continued to enhance the developer experience, making experimentation at scale and integration into any stack easier than ever. Finally, for companies seeking an alternative to Statsig, our Statsig to GrowthBook Migration Kit automates importing feature gates and dynamic configs while replacing Statsig SDKs with GrowthBook SDKs.

Release 4.2 is available immediately to both our cloud and self-hosted users. Visit our Pricing page for details about Starter, Pro, and Enterprise options.

Join us for a GrowthBook 4.2 Live Demo with Q&A
Thursday, December 4 at 10AM PT / 1PM ET

GrowthBook Product Analytics (Beta)

Adding Product Analytics to the GrowthBook platform closes the loop for development. Now, you can go from feature management to experimentation to product analytics in a single tool. While in beta, Product Analytics will be available to all users.
Turn your warehouse data and metrics into actionable product insights. Explore user behavior, share dashboards, and make smarter decisions about what to build next. With Product Analytics, you will be able to:

• Build and share dashboards that combine graphs, pivot tables, and text
• Create custom charts and tables from any data in your warehouse
• Use GrowthBook SQL Explorer with our AI-powered text-to-SQL capabilities to query, aggregate, and group data
• Access any metric defined in GrowthBook and track its performance over time

This Product Analytics beta provides a glimpse of what’s to come as GrowthBook develops more self-service tools for building, analyzing, and exploring all of your product data. Let us know what you think in our Slack community!

Statsig to GrowthBook Migration Kit

With the OpenAI acquisition of Statsig, we saw a spike in interest in GrowthBook. Product teams looking for alternatives expressed concern about what would happen to their data. Others worried that the product might be discontinued or deprioritized. To make the transition from the acquired platform to an open-source alternative as effortless as possible, we created the Statsig to GrowthBook Migration Kit, free for all users.

• Statsig Importer instantly copies over feature gates, dynamic configs, and segments.
• Statsig Code Migration Tool (powered by Claude Code) automatically replaces Statsig SDKs with GrowthBook SDKs.

Enterprise Enhancements

The 4.2 features below continue our investment in the developer experience that makes GrowthBook a top choice for product development teams with high volume apps and advanced experimentation programs.

Metric Slices: Simplify Experiment Design

When users create experiments, they often want to look at a number of metrics across common dimensions like product categories or device types. This can lead to the need to manage a number of metrics. Metric slices solves this problem. Enable auto slices on a Fact Metric once, and GrowthBook automatically generates drill-down analyses for each dimension value across all experiments using that metric.

Instead of creating separate “Orders” metrics for each product category or device type, you can enable Auto Slices on those columns with a single metric which means fewer redundant metrics, faster setup, and cleaner reporting.

Incremental Refresh

We revamped our Data Pipeline Mode to lower query costs and improve performance for long-running experiments and high-traffic apps. By storing intermediate results and incrementally refreshing them, we’ve seen users save up to 85% in query costs. This first version is available on BigQuery, Presto, and Trino. We’ll be adding support for more data warehouses based on customer demand.

Official Metrics

Many organizations rely on a trusted set of “official” metrics. GrowthBook now makes these easier to manage by letting admins mark and edit official metrics directly from the UI (previously API-only). This helps standardize measurement, reduce confusion, and promote consistency across teams.

New SQL Template Variables

You can now access custom field values and phase data directly in your metric and experiment SQL, unlocking several use cases:

• Fine-tuned query optimization using non-date partition keys
• Reuse of SQL definitions with minor tweaks per experiment
• More accurate joins between experiment exposure and phase data

Custom Validation Hooks

GrowthBook has always been flexible — and now it’s even more so. Self-hosted enterprise users can write custom JavaScript validation hooks that run in secure V8 isolates. Use them to:

• Require tags on feature flags
• Prevent targeting rules containing PII
• Enforce naming conventions or internal policies
  These hooks let teams automate governance without slowing down development.

Edge Remote Eval

Edge Remote Eval lets client-side SDKs offload feature flag evaluation to a backend server, preventing targeting logic from leaking to users. Previously, this required managing your own GrowthBook proxy servers. Now, you can deploy a Cloudflare Workers–based Remote Eval server — a fast, low-cost, zero-maintenance alternative built on Cloudflare’s global infrastructure.

Quality-of-Life Improvements

Big thanks to all of our users who reported bugs, shared feedback, and contributed ideas to this release on GitHub or Slack.
Many small improvements add up to a big boost in usability:

• Faster and more relevant search algorithm for features, metrics, and experiments
• Create feature rules in multiple environments at once
• Better column-type detection for BigQuery Fact Tables
• Add metric row filters based on Boolean columns
• Reduced webhook noise (no more notifications for unpublished drafts)
• Slack and Discord notifications now include more detailed change info
• Custom pre-launch checklist items can be scoped to specific projects
• Faster database schema browsing, even with hundreds of tables
• New setting to disable legacy metrics for smoother transition to Fact Tables
• Sortable experiment results tables — quickly see top or bottom performers
  Plus dozens of smaller fixes and performance improvements.

2025: A Year of Rapid Innovation

The 4.2 release is GrowthBook’s sixth major update in 2025, capping off what has easily been the biggest year of innovation in our company’s history. GrowthBook launched over 45 new features across four major themes in 2025:

• Experimentation at Scale: New metrics, templates, dashboards, and analytics
• Feature Management: Safe rollouts and feature analytics
• Artificial Intelligence: A new MCP server and embedded AI capabilities
• Developer Experience: Managed data warehouse, native Vercel integration, 13 updated SDKs, enhanced server-side rendering, and support for new CMSs and FerretDB

Whether you’re on the Starter plan ready for more advanced experimentation and analytics or a Pro user building a culture of experimentation, we’re ready to help you grow. We’re excited to see what you build — and how you use these new tools to learn faster.

Live Demo with Q&A

Thursday, December 4 at 10AM PT / 1PM ET
Register Now

Original source

GrowthBook version 4.1 includes Holdouts, Dashboards, AI features, and More!

This release continues the momentum of GrowthBook 4.0 by adding two of our most requested Enterprise features - Holdouts and Experiment Dashboards. Plus, we’ve made several significant enhancements to our integrations with AI coding tools and our MCP server capabilities. If you’re not yet using GrowthBook with your AI coding tools, we highly recommend it!

Read on to learn more about these features and everything else we’ve been working on these past 2 months.

Holdouts

Holdout experiments measure the long-term impact of features by maintaining a control group that doesn't receive new functionality. While most users experience your latest features and improvements, a small percentage remains on the original version, providing a baseline to measure cumulative effects over time. Read more about holdouts.

Experiment Dashboards

Experiment Dashboards let you create tailored views of an experiment. Highlight key insights, add context, and share a clear story with your team. For example, highlight the key goal metric results, show an interesting dimension breakdown, and link to supporting external documents, all in a single view. Dashboards are available for all Enterprise customers. We have a lot planned for this in the future, so stay tuned!

AI Features

This release deeply integrates AI to help accelerate your workflows within GrowthBook. Auto-summarize experiment results, get help writing SQL, improve hypotheses, detect similar past experiments, and more. These features are available even if you’re self-hosted, just supply an OpenAI API key. See the AI features in action or read detailed information on how these features work.

MCP Updates

We've updated our MCP Server to allow creating experiments directly from your AI coding tool of choice without needing to context switch to GrowthBook. This change unlocks a bunch of new, exciting workflows, and we can't wait to see how you use it!

Vercel Native Integration

We're excited to announce that GrowthBook is now available as a native integration in the Experimentation category on the Vercel Marketplace! This integration makes it easier than ever to add feature flagging and A/B testing to your Vercel projects, with streamlined setup, unified billing, and ultra-low latency performance. Read more on our announcement post.

Pre-computed Dimensions

You can now pick a set of key experiment dimensions and pre-compute them along with the main experiment results. This allows for more efficient database queries and instant dimension breakdowns in the UI. Read more in our docs.

FerretDB Support

FerretDB is a MongoDB-compatible database that is open source and free to use. It serves as a drop-in replacement for MongoDB, converting MongoDB wire protocol queries to SQL and using PostgreSQL as its backend storage engine. We're pleased to support FerretDB officially!

Sanity CMS Integration

Sanity is a real-time content backend for all your text and assets. You can now use GrowthBook feature flags to seamlessly test different content variations within Sanity. Check out our announcement video and tutorial or our docs.

The 4.1 release includes over 150 commits, way more than we can quickly summarize here. View the release details on GitHub for a more comprehensive list. As always, we love feedback - good and bad. Let us know what you think of the new features and what you want to see as part of 4.2!

Original source

Release Highlights

Holdouts

Holdout experiments measure the long-term impact of features by maintaining a control group that doesn't receive new functionality. While most users experience your latest features and improvements, a small percentage remains on the original version, providing a baseline to measure cumulative effects over time.

Experiment Dashboards

Experiment Dashboards let you create tailored views of an experiment. Highlight key insights, add context, and share a clear story with your team. Dashboards are available for all Enterprise customers. We have a lot planned for this in the future, so stay tuned!

Vercel Native Integration

We're excited to announce that GrowthBook is now available as a native integration in the Experimentation category on the Vercel Marketplace! This integration makes it easier than ever to add feature flagging and A/B testing to your Vercel projects, with streamlined setup, unified billing, and ultra-low latency performance.

AI Features

This release deeply integrates AI to help accelerate your workflows within GrowthBook. Auto-summarize experiment results, get help writing SQL, improve hypotheses, and detect similar past experiments, and more. These features are available to everyone, even self-hosted (just supply an OpenAI key).

Pre-computed Dimensions

You can now pick a set of key experiment dimensions and pre-compute them along with the main experiment results. This allows for more efficient database queries, and instant dimension breakdowns in the UI.

FerretDB Support

FerretDB is a MongoDB-compatible database that is open source and free to use. It serves as a drop-in replacement for MongoDB, converting MongoDB wire protocol queries to SQL and using PostgreSQL as its backend storage engine.

Sanity CMS Integration

Sanity is a real-time content backend for all your text and assets. You can now use GrowthBook feature flags to seamlessly test different content variations within Sanity.

MCP Updates

We've updated our MCP Server to allow creating experiments directly from your AI tool of choice without needing to context switch to GrowthBook. This change unlocks a bunch of new exciting workflows and we can't wait to see how you use it!

New Experiment Results Tooltip

We've completely redesigned the tooltip when mousing over experiment results. The new design provides more context and insights at a glance, making it easier to understand the impact of your experiments.

SQL Auto-Complete

We've added context-aware SQL auto-complete suggestions for table and column names to help you write queries faster and with fewer errors.

Official Segments and Dimensions

You can now mark specific segments and user dimensions as Official via the GrowthBook API. This will give them a little badge in the UI and lock down editing to prevent unwanted changes. Use this to differentiate important segments and dimensions from ad-hoc or exploratory ones.

Self-Hosting Improvements

For anyone self-hosting GrowthBook, we've greatly improved observability and customization. We now support full monitoring and tracing of all background jobs, plus distributed tracing within the Python stats engine. We also added a number of new env vars to more easily split the GrowthBook monolith into microservices for those that need extra scalability.

SDK Updates

• React - Full React 19 Typescript support
• Golang - SSE streaming fixes
• PHP - sticky bucketing fixes
• Java - new caching and refresh options, memory optimizations
• Flutter - many bug fixes and updates
• C sharp - SSE streaming, attribute targeting fixes
• Swift - sticky bucket fixes, support for bearer auth, logging improvements, and memory optimization
• Kotlin - async-safe sticky bucketing, new streamingHost setting, suspend entry points to avoid blocking
• Flutter - TTL-based fetch option, cache improvements, attribute targeting fixes
• Edge - many new options for advanced integrations

REST API Updates

• New GET /settings endpoint to fetch all of your organization's current settings
• Fix PUT /metric/:id endpoint that was ignoring the archived setting
• New GET /feature/:id/revisions endpoint to fetch all revisions for a specific feature with pagination
• New endpoints to create, update, and delete dimensions and segments
• Ability to archive fact tables and fact metrics
  Plus a lot more
• New is:watched filter on experiment search
• Enable encryption option for back-end SDKs
• Add user-agent header to all HTTP requests (webhooks, etc.)
• Various date and timezone fixes
• Various SQL editing and schema browser fixes
• Properly close MySQL connections
• Ability to remove LIMIT 5 when testing SQL queries
• Use Webhook Secrets by default for new webhooks
• ClickHouse - add support for new JSON data types
• Wrap user-supplied SQL in parentheses to avoid edge cases
• Fix divide-by-zero bug in stats engine
• Jira integration update to support a custom field
• Fix stale feature detection logic
• New Big Number visualization type in the SQL Explorer
• Enable the schema browser for self-hosted orgs that use config.yml
• Improve fact metric query performance by filtering rows earlier
• Fix broken SRM check when one variation has 0 users
• Fix bugs with quantile and percentile capped metric settings
• Audit log entries now show the full date time instead of "X days ago"
• Power calculator fixes
• Allow manual seed selection for feature rollout rules
• Skip feature.updated webhooks until draft changes are actually published
• Improve handling of unit quantiles when "ignore zero" is set
• Persist last viewed tab on the experiment list

New Contributors

• @nodirnasirov made their first contribution in #4296
• @kweic made their first contribution in #4315
• @denisov-vlad made their first contribution in #4351
• @Khrol made their first contribution in #4339
• @benruehl made their first contribution in #4254

Plus a ton of returning contributors:
@jdorn @royalfig @tzjames @ahdriel @mknowlton89 @madhuchavva @bryce-fitzsimons @natasha-growthbook @Auz @lukesonnet @Kevin-Chant @lukebrawleysmith @Hephaest @gazzdingo @ari-party @msamper @itsgrimetime

Full Changelog: v4.0.0...v4.1.0

Original source

GrowthBook 4.0 includes a huge number of new features and updates. Continue reading for a full list of changes.

We shipped so many new features in our June Launch Month that we decided that it deserved a major version increase. Version 4.0 brings a huge array of new features. Here’s a quick summary of everything it includes.

GrowthBook MCP Server

AI tools like Cursor can now interact with GrowthBook via our new MCP server. Create feature flags, check the status of running experiments, clean up stale code, and more.

Safer Rollouts

Building upon our Safe Rollouts release last version, we added gradual traffic ramp up, auto rollback, a smart update schedule, and a time series view of results. All of these combine to add even more safety around your feature releases.

Decision Criteria

You can now customize the shipping recommendation logic for experiments. Choose from a “Clear Signals” model, a “Do No Harm” model, or define your own from scratch.

Search Filters

We’ve revamped the search experience within GrowthBook to make it easier to find feature flags, metrics, and experiments. Easily filter by project, owner, tag, type, and more.

Insights Section

We added a brand new left nav section called “Insights” with a bunch of tools to help you learn from your past experiments.

• The Dashboard shows velocity, win rate, and scaled metric impact by project.
• Learnings is a searchable knowledge base of all of your completed experiments.
• The Experiment Timeline shows when experiments were running and how they overlapped with each other.
• Metric Effects lists the experiments that had the biggest impact on a specific metric.
• Metric Correlations let you see how two metrics move in relation to each other.

SQL Explorer

We launched a lightweight SQL console and BI tool to explore and visualize your data directly within GrowthBook, without needing to switch to another platform like Looker.

Managed Warehouse

GrowthBook Cloud now offers a fully managed ClickHouse database that is deeply integrated with the product. It’s the fastest way to start collecting data and running experiments on GrowthBook. You still get raw SQL access and all the benefits of a warehouse native product, without the setup and maintenance cost.

Feature Flag Usage

See analytics about how your feature flags are being evaluated in your app in real time. This is built on top of the new Managed Warehouse on GrowthBook Cloud and is a game changer for debugging and QA.

Vercel Flags SDK

GrowthBook now has an official provider for the Vercel Flags SDK. This is now the easiest way to add server-side feature flags to any Next.js project. We have an even deeper Vercel integration coming soon to make this experience even more seamless.

Official Framer Plugin

You can now easily run GrowthBook experiments inside of your Framer projects. Assign visitors to different versions of your design (like layouts, headlines, or calls to action), track results, and confidently choose the best experience for your audience.

Personalized Landing Page

There’s a new landing page when you first log into GrowthBook. Quickly see any features or experiments that need your attention, pick up where you left off, and learn about advanced GrowthBook functionality to get the most out of the platform.

New Experimentation Left Nav

There’s a new “Experimentation” section in the left nav. Experiments and Bandits now live within this section, along with our Power Calculator, Experiment Templates, and Namespaces. We’ll be expanding this section soon with Holdouts and more, so stay tuned!

REST API Updates

• Filter the listFeatures endpoint by clientKey
• Support partial rule updates in the putFeature endpoint
• New Queries endpoint to retrieve raw SQL queries and results from an experiment
• Added Custom Field support to feature and experiment endpoints
• New endpoints for getting feature code refs
• New endpoint to revert a feature to a specific revision

Performance Improvements

We’ve drastically improved the CPU and memory usage when self-hosting GrowthBook at scale. On GrowthBook Cloud, we’ve seen a roughly 50% reduction during peak load, leading to lower latency and virtually eliminating container failures in production.

Original source

Release Highlights

GrowthBook MCP Server

AI tools like Cursor can now interact with GrowthBook via our new MCP server. Create feature flags, check the status of running experiments, clean up stale code, and more.

Safer Rollouts

Building upon our Safe Rollouts release last version, we added gradual traffic ramp up, auto rollback, a smart update schedule, and a time series view of results. All of these combine to add even more safety around your feature releases.

Decision Criteria

You can now customize the shipping recommendation logic for experiments. Choose from a “Clear Signals” model, a “Do No Harm” model, or define your own from scratch.

Search Filters

We’ve revamped the search experience within GrowthBook to make it easier to find feature flags, metrics, and experiments. Easily filter by project, owner, tag, type, and more.

Insights Section

We added a brand new left nav section called “Insights” with a bunch of tools to help you learn from your past experiments.

• The Dashboard shows velocity, win rate, and scaled metric impact by project.
• Learnings is a searchable knowledge base of all of your completed experiments.
• The Experiment Timeline shows when experiments were running and how they overlapped with each other.
• Metric Effects lists the experiments that had the biggest impact on a specific metric.
• Metric Correlations let you see how two metrics move in relation to each other.

SQL Explorer

We launched a lightweight SQL console and BI tool to explore and visualize your data directly within GrowthBook, without needing to switch to another platform like Looker.

Managed Warehouse

GrowthBook Cloud now offers a fully managed ClickHouse database that is deeply integrated with the product. It’s the fastest way to start collecting data and running experiments on GrowthBook. You still get raw SQL access and all the benefits of a warehouse native product, without the setup and maintenance cost.

Feature Flag Usage

See analytics about how your feature flags are being evaluated in your app in real time. This is built on top of the new Managed Warehouse on GrowthBook Cloud and is a game changer for debugging and QA.

Vercel Flags SDK

GrowthBook now has an official provider for the Vercel Flags SDK. This is now the easiest way to add server-side feature flags to any Next.js project. We have an even deeper Vercel integration coming soon to make this experience even more seamless.

Official Framer Plugin

You can now easily run GrowthBook experiments inside of your Framer projects. Assign visitors to different versions of your design (like layouts, headlines, or calls to action), track results, and confidently choose the best experience for your audience.

Personalized Landing Page

There’s a new landing page when you first log into GrowthBook. Quickly see any features or experiments that need your attention, pick up where you left off, and learn about advanced GrowthBook functionality to get the most out of the platform.

Experimentation Left Nav

There’s a new “Experimentation” section in the left nav. Experiments and Bandits now live within this section, along with our Power Calculator, Experiment Templates, and Namespaces. We’ll be expanding this section soon with Holdouts and more, so stay tuned!

REST API Updates

• Filter the listFeatures endpoint by clientKey
• Support partial rule updates in the putFeature endpoint
• New Queries endpoint to retrieve raw SQL queries and results from an experiment
• Added Custom Field support to feature and experiment endpoints
• New endpoints for getting feature code refs
• New endpoint to revert a feature to a specific revision

Performance Improvements

We’ve drastically improved the CPU and memory usage when self-hosting GrowthBook at scale. On GrowthBook Cloud, we’ve seen a roughly 50% reduction during peak load, leading to lower latency and virtually eliminating container failures in production.

New Contributors

• @paigeruppel-upstart made their first contribution in #3992

• @jrnold made their first contribution in #4056

• @devcodes9 made their first contribution in #4109

• @kabam-blambert made their first contribution in #4070

• @matttenenbaum-qz made their first contribution in #4172

• @edisonmoy made their first contribution in #4266

• Plus a ton of returning contributors:
  @ahdriel @gazzdingo @Auz @lukesonnet @tzjames @mknowlton89 @lukebrawleysmith @jdorn @romain-growthbook @natasha-growthbook @Kevin-Chant @royalfig @august-growthbook @msamper @bryce-fitzsimons

Full Changelog: v3.6.0...v4.0.0

Original source

Managed Warehouse

This week we're launching our Managed Warehouse and Feature Flag Usage Analytics.

Launch month continues with our Managed Warehouse product and feature flag usage analytics.

We’ve talked to thousands of companies and have seen our fair share of data warehouse and event tracking setups. We’ve noticed 3 consistent problems:

• 1. They are a LOT of work to set up and maintain, especially for companies without dedicated data engineers. Google Analytics + BigQuery is the most common one we see and that takes over 40 steps to configure (yes, we counted).
• 1. Data is often refreshed on a schedule instead of in real-time. You don’t want to wait 24 hours to find out a new feature or experiment is killing your metrics.
• 1. Pricing and performance are optimized for batch workloads. Refreshing experiment results frequently or exploring your data can become slow and costly.

This week, we’re excited to launch our new Managed Warehouse product on GrowthBook Cloud. We set out to solve all of these issues and we’re super happy with the results:

• 1. One-click setup and zero maintenance. Doesn’t get much easier than that!
• 1. Data arrives within seconds, letting you quickly detect issues.
• 1. Queries are crazy fast and free (only pay for ingesting the data, not querying it)

Under the hood, this is powered by ClickHouse, an open source database optimized for fast analytics at scale. You still get raw SQL access and all the other benefits of a true Warehouse Native platform, just without the cost.
The first 2 million tracked events each month are free for Pro users and we have super affordable usage-based pricing beyond that.

Feature Usage Analytics

This week we’re also launching Feature Usage Analytics, which was built to take advantage of many of the benefits of the new Managed Warehouse.
For each feature flag, you can see how often it's been evaluated, which values are being served, which rules are being hit, and more. This is a game changer for feature flag management and makes debugging issues so much faster. As an extra benefit, this also helps you stay on top of tech debt by highlighting stale flags that are no longer being used.
So how does it work? The GrowthBook SDK sends an event to the Managed Warehouse every time a feature flag is evaluated in your app. This usage data is then aggregated and displayed within seconds.
For now, this is only available for the new Managed Warehouse on GrowthBook Cloud, but we want to eventually open this up to everyone. If you are interested in this, but are self-hosting (or already have a data warehouse), let us know and we can keep you in the loop!

Original source

For week 3 of our Launch Month, we're releasing SQL Explorer, which lets you query your data and visualize results.

For week 3 of our Launch Month, we’re excited to announce the SQL Explorer!

At GrowthBook, we love dogfooding our product (most of our launches this month started behind feature flags). Along the way, we kept running into a common frustration: answering simple data questions meant jumping into separate tools like Looker, Mode, or Tableau - just to write some quick SQL or generate a basic chart.

All of that context switching adds up, which is why we built SQL Explorer - a lightweight, built-in SQL editor that lets you query your data, create visualizations, and save results right inside of GrowthBook. It's perfect for quick analyses without the overhead of a full BI platform. Check it out and let us know what you think.

We’ll be adding more features and integrating the SQL Explorer more deeply in the product in the coming weeks, so keep an eye out!

Original source

This week we're launching Insights, which includes a new Executive Dashboard, searchable experiment learnings, timeline visualizations, and deep metric analysis.

This week is all about Insights, the brand new section in our sidebar. In there, you’ll find a revamped Executive Dashboard, new Learnings and Timeline pages, plus some powerful metric analyses to help you get the most out of your experimentation program. This section becomes more useful the more experiments you run, so if you needed an excuse to run more tests this is it!

Executive Dashboard

The brand new dashboard gives you a 10,000 foot view of your organization’s experimentation program. Quickly see your team’s velocity, win rate, and impact. Enterprise users can also select a metric and see the cumulative effect from all the experiments that were run. Everything can be filtered by project and date range.

Learnings

The Learnings page is a searchable knowledge base of every experiment your team has completed. For each experiment, see the winning variation, a summary of results, and other key details. This page is a great place for new team members to learn about what has been tried and what has and hasn’t worked.

Fun fact: This was the original reason we started GrowthBook and how we got our name. We envisioned a digital book of everything you’ve learned about growth.

Experiment Timeline

The Timeline page lets you visualize when experiments were running in relation to each other. Experiments are color-coded by status (running, won, lost, etc.) and split up by phases. This is a valuable tool for managing your experimentation workflow, identifying bottlenecks in your process, and planning future tests.

Metric Effects

Do a deep-dive for a given metric and see the range of effect sizes from all the experiments that included it. Use this to learn how easy/hard it is to move your metric in general and see which specific experiments had the biggest impact (both good and bad).

Metric Correlations

See how any 2 metrics tend to move in relation to each other within experiments. This is especially useful for identifying proxy metrics that are highly correlated with your long term goals, but can get you results much faster.

We hope you find these new pages useful and we look forward to hearing your feedback. See you again soon for our Week 3 launches!

Original source

This June is our Launch Month! Every week, we’ll announce major new features and changes to GrowthBook that you can try out early, before the final release at the end of the month.

We have so many exciting projects we’re working on, we decided to do something a little different for this next release. June will be our official Launch Month! Every week we’ll announce major new features and changes to GrowthBook that you can try out early, before the final release at the end of the month.

Let’s kick things off with the Week 1 launches:

GrowthBook MCP Server

We launched the first ever MCP server for feature flagging and experimentation! MCP (Model Context Protocol) allows AI tools to communicate and do actions directly with services like GrowthBook. Now you can use AI to create feature flags, check experiment results, clean up stale code, and more, directly within your IDE. Read our announcement blog post for more info and a demo.

Even Safer Rollouts

We made three big changes to Safe Rollouts to make them even safer:

• 1. Traffic now gradually ramps up from 10% to 100%
• 1. Results are checked more frequently at the start of a Safe Rollout (and less frequently the longer it’s running)
• 1. There’s a new setting to automatically roll back if any guardrails are failing or the data looks unhealthy

When combined, these changes help make your rollouts even safer by minimizing the user impact when things go wrong. As always, you can learn more about this in our docs.

Custom Decision Criteria

You can now customize the logic that powers our Experiment Decision Framework on a per-experiment basis.

• Clear Signals (the default) - Ship only with clear goal metric successes and no guardrail failures.
• Do No Harm - Ship so long as no guardrails and no goal metrics are failing. Useful if the costs of shipping are very low.
• Custom - Define your own fully custom decision criteria logic using our intuitive UI.

Check out our docs for more information.

Search Filters

We’ve revamped the search experience within GrowthBook to make it easier to find feature flags and metrics. Easily filter by project, owner, tag, type, and more. The best part is that all of your filters are encoded in the URL, so once you find a view you like you can easily get back to it or share it with your team. We’ll be rolling this out to other parts of the app soon.

Original source