LaunchDarkly vs GrowthBook: which fits a team that runs experiments
If your team pays for LaunchDarkly Experimentation and Data Export mainly so experiments can read event data, the decision is about where that data lives and who runs the pipeline. LaunchDarkly fits when you want a managed platform and are content to let it own experiment analysis and the event path, with its Data Export add-on feeding your warehouse or other destinations. Self-hosted GrowthBook fits when experiment event data must stay in your warehouse, because it runs read-only SELECT queries against sources such as Snowflake, BigQuery and Postgres and transmits only aggregated statistics. That model also means you accept running GrowthBook yourself: its front end, Express API and Python statistics engine ship in one Docker image, with metadata in MongoDB or a compatible database.
Start by checking your warehouse maturity. GrowthBook assumes you have trusted exposure and metric tables, clean user identifiers and a team that can write the SQL behind each metric. If those tables do not exist yet, the work of building them lands on your data team whichever product you pick, and the cost of that work is the largest unknown in this comparison. Next, list the statistical features you rely on. GrowthBook's free self-hosted Open Source plan has one project and unlimited users. Multi-armed bandits, holdouts, sticky bucketing, safe rollouts, ramp schedules, SSO and SCIM, advanced permissioning and approval workflows are in the self-hosted Enterprise tier, so confirm with GrowthBook's pricing page which of these you need before you budget.
Finally, decide whether you need GrowthBook's Enterprise features at all. If you only need feature flags plus fixed-horizon A/B tests on your own warehouse, the free self-hosted plan may cover it. If governance, approvals or holdouts are required by your change process, price the Enterprise seats and compare them with the LaunchDarkly contract you have today. Ask LaunchDarkly what its Data Export add-on delivers, how it is billed and whether historical data can be backfilled, and ask your data team how long it would take to model experiment metrics directly in the warehouse. Those answers, not feature lists alone, will tell you which platform fits the team that runs your experiments.
Which option fits which team setup
Teams that already land product events in Snowflake, BigQuery, Redshift, Databricks or another supported warehouse usually want experiments computed where the data already sits. GrowthBook fits this setup because it analyzes experiments against the customer's own data source, runs only SELECT queries and transmits only aggregated statistics. The condition that makes it true is that your analysts already define metrics in SQL and treat the warehouse as the system of record for experiment results.
- Data source coverage: GrowthBook lists BigQuery, Snowflake, Redshift, Databricks, ClickHouse, Postgres, MySQL, SQL Server, Athena, Presto or Trino and Vertica as supported sources. Confirm your exact warehouse version and network path before planning.
- Metric ownership: Metrics are defined against your tables, so analysts review the SQL rather than trusting a vendor's event pipeline.
- Read-only access: Create a dedicated warehouse role limited to SELECT on the modeled tables used for experiments.
- Statistics review: Have a data scientist validate the statistics engine output against a known analysis before relying on it for decisions.
- Tier check: Bandits, holdouts and sticky bucketing sit in the paid self-hosted Enterprise tier, so decide early whether you need them.
Before you commit, ask your warehouse team who will own the query load, cost and access reviews for experiment tables.
Product teams that run experiments but have no warehouse or data engineering capacity often rely on a vendor-hosted event store. For this setup, GrowthBook still works, but you must first decide where events will be stored, because GrowthBook's experiment analysis depends on a connected data source. Without a warehouse, the practical path is to stand up one of the supported SQL databases such as Postgres or MySQL, or to choose a platform whose analytics is bundled in the same product.
- Analysis dependency: GrowthBook analyzes experiments from your data source, so a team without any SQL store must add one before experiments produce results.
- Release-first start: Teams can begin with flags in a self-hosted tool and add experiment analysis once an event store exists.
- Operational load: A database, the GrowthBook image and its metadata store each need backup and upgrades, which is ongoing work for whoever runs them.
- Event instrumentation: Someone must send exposure and conversion events consistently, which is a development task, not a configuration setting.
- Alternative fit: If you want product analytics and flags together, compare PostHog on its own page rather than assuming GrowthBook covers that need.
Ask for a written scope that lists the event store, instrumentation work and who will maintain both before choosing this path.
Banks, healthcare providers, public sector bodies and critical infrastructure operators often need every component, including experiment analysis, to run inside their own infrastructure. GrowthBook can be self-hosted from a single Docker image, and it offers an official Helm chart for Kubernetes. The condition that makes it true is that your security and compliance teams accept GrowthBook's self-hosted components, its MongoDB or compatible metadata store and its file storage options as part of their control scope.
- Deployment model: The Next.js front end, Express API and Python statistics engine ship together, so the whole analysis path can sit inside your network.
- Metadata store: MongoDB or a compatible database holds metadata, and the docs suggest a managed service such as MongoDB Atlas or AWS DocumentDB for production. Decide whether a managed service is allowed in your boundary.
- File storage: Local storage, Amazon S3 or Google Cloud Storage are supported for uploads, so choose one that matches your data residency rules.
- Licensing: Code outside the enterprise directories is MIT licensed, while the enterprise directories fall under the GrowthBook Enterprise License. Have legal review which features you will run.
- Identity: SSO and SCIM require the paid self-hosted Enterprise tier, so budget for it if access must follow your directory.
DeployOpen prepares systems and evidence for assessments, but independent auditors decide whether your controls meet the standard that applies to you.
Some teams use LaunchDarkly mostly for release toggles, targeting and gradual rollouts, and run few experiments. For them, GrowthBook is a heavier fit than necessary, since its strength is experiment analysis. Self-hosted Unleash or Flagsmith may match the need more directly, and you can still use GrowthBook later if experimentation grows. The condition that makes this true is that experiments are occasional and most of the value comes from safe releases and targeting.
- Flag-first need: If your main use is kill switches and percentage rollouts, a release-focused platform keeps the operating model simpler.
- Free tier limits: GrowthBook's self-hosted Open Source plan allows 1 project with unlimited users, and Unleash Open Source allows 1 project and 2 environments. Count the projects and environments you need today.
- Licensing check: Unleash moved its server to AGPLv3 with v8.0.0, while Flagsmith is mostly BSD 3-Clause with closed-source Enterprise features.
- Edge components: Unleash Edge's open-source version reaches end of life on December 31, 2026, so plan a replacement if you rely on it for scale.
- Decoupling: Wrapping calls in OpenFeature lets you switch providers later without rewriting application code.
Ask each shortlisted vendor for its current free-tier limits in writing, because these change between releases.
Scenarios where each option is the better fit
This table maps common situations to the option that fits best, with the condition under which each choice holds. Read each row against your own event data location, warehouse maturity and governance needs, since the right answer usually depends on one or two constraints rather than overall preference. The main takeaway is that GrowthBook fits teams whose experiments must read from their own warehouse, while LaunchDarkly fits teams that want a hosted platform with no infrastructure to run.
| Scenario | Better fit | Condition and reason |
|---|---|---|
| Event data must stay in the warehouse | GrowthBook | Holds when your experiment events already land in Snowflake, BigQuery or another supported source, because GrowthBook runs read-only SELECT queries and sends back only aggregated statistics. |
| No warehouse in place yet | LaunchDarkly Experimentation | Holds when you have no governed event pipeline, since GrowthBook's analysis depends on a source you operate, while LaunchDarkly's hosted analysis needs no warehouse build-out. |
| Bandits, holdouts or approval workflows required | GrowthBook Enterprise | Holds when you need multi-armed bandits, holdouts, approval workflows or sticky bucketing. Confirm these are in the self-hosted Enterprise tier, since the free self-hosted plan excludes them. |
| Self-hosting required for data control | GrowthBook or Unleash | Holds when policy keeps flag and experiment data in your own infrastructure. GrowthBook's MIT-licensed core covers the open-source plan, but enterprise directories use a separate license. |
| Flags are the main need, experiments occasional | Unleash or Flagsmith | Holds when release flags dominate and experiments are rare. A flag-first tool avoids paying for or operating an analysis stack you use only a few times a year. |
| Experiments run daily across product teams | GrowthBook | Holds when many teams need self-service analysis tied to shared metrics, since GrowthBook keeps metric definitions and results close to the warehouse data they depend on. |
| Single hosted vendor preferred with minimal operations | LaunchDarkly | Holds when your team has no capacity to run MongoDB, the GrowthBook services and upgrades, and prefers a managed SaaS with vendor-run infrastructure. |
| Migration from LaunchDarkly Data Export planned | GrowthBook | Holds when you want experiment history rebuilt from your own warehouse data rather than exported event streams. Confirm which historical results can be recomputed in your environment before cutover. |
Where experiment event data lives and who queries it
The first decision in this comparison is where experiment event data is analyzed. With LaunchDarkly, evaluation and custom events can be streamed out through the Data Export add-on, so the analysis path runs through a vendor-side pipeline that you configure, monitor and pay for alongside the platform. With GrowthBook, the analysis runs against your own data source: it issues read-only SELECT queries against systems such as Snowflake, BigQuery or Postgres and receives only aggregated statistics back. Confirm with LaunchDarkly exactly which event types and fields the Data Export add-on delivers, and whether it is included in your contract, before you assume your warehouse holds the same data as the experiment dashboard.
The trade-offs fall into four areas. Data freshness depends on the pipeline: an export path adds a hop and a schedule, while a warehouse query reads whatever your own ingestion has loaded, so the lag is set by your team's pipelines. Duplicate pipelines are the usual hidden cost, since a vendor export means a second copy of events that must be reconciled with the warehouse copy your finance and product teams already use. Access control moves with the data: in GrowthBook, the people who can read experiment results are constrained by the database roles you grant to the GrowthBook connection, so review that service account's grants with your data platform owners. Metric ownership also changes. In a warehouse-native setup, metric definitions live as SQL that your analysts can review, version and reuse, which suits teams that already define revenue in the warehouse.
This matters most when the warehouse already holds the authoritative product and revenue data. If orders, subscriptions and sessions are reconciled there, computing experiment metrics in the same place avoids two sets of numbers for one business outcome. If the warehouse is not yet authoritative, or event modeling is weak (inconsistent user identifiers, missing exposure events, or the same action logged under different names), either option becomes harder. A vendor export will carry the bad modeling into a second system, and warehouse queries will expose it in SQL that someone must fix. Before choosing, have the data team check exposure logging, identifier stitching and the timestamps used for attribution, and ask DeployOpen or your own engineers to document those checks as part of the migration scope.
What GrowthBook's free plan leaves out compared with Enterprise
GrowthBook's self-hosted Open Source plan covers the core experimentation workflow, but several controls that regulated teams often need sit behind the Enterprise tier. The cards below list the gaps to price, the license boundary to review, and the questions to put to LaunchDarkly before comparing the two.
Statistics engine and warehouse computation
GrowthBook runs experiment analysis against your own data source and sends only aggregated statistics back to the application. The queries are read-only SELECT statements, so the event data stays in the warehouse your team already governs. Before you compare, confirm which statistical methods your analysts need, check that your warehouse (for example Snowflake, BigQuery or Postgres) is on the supported list, and ask LaunchDarkly how its Experimentation results are computed and where raw events are stored.
One project on the Open Source plan
The self-hosted Open Source plan allows 1 project with unlimited users and unlimited API requests. Teams that run several products, business units or environments with separate governance usually outgrow that single project quickly. Count your projects before you commit, and ask LaunchDarkly how its experimentation entitlements scale with projects, environments and seats in the tier you are considering.
Identity and access controls
Self-hosted Enterprise adds SSO and SCIM provisioning, advanced permissioning and approval workflows. These features decide who can launch an experiment, who can change a production rollout and how joiners and leavers are removed automatically. If your access reviews depend on directory-driven provisioning or on separation of duties for production changes, treat these controls as required rather than optional, and confirm them against your own identity provider.
Advanced experiment methods
Multi-armed bandits, sticky bucketing, holdouts, safe rollouts and ramp schedules are listed as Enterprise features on the self-hosted tier. Holdouts measure long-term impact against users kept out of a change, and sticky bucketing keeps users in the same variant across sessions, which matters for rollout analysis. Map which of these methods your current LaunchDarkly experiments use, then confirm the equivalent in each product.
License boundary inside the codebase
GrowthBook's LICENSE file places code outside its enterprise directories under the MIT Expat license, while the packages/back-end/src/enterprise, packages/front-end/enterprise and packages/shared/src/enterprise directories fall under the GrowthBook Enterprise License. Legal and procurement teams should read that file directly, confirm how their paid Enterprise features map to those directories, and record which code they will modify or redistribute.
Questions to ask LaunchDarkly
LaunchDarkly's experimentation capabilities and Data Export are sold across plan levels, so the features you rely on may sit in a higher tier than the one you quote. Ask the vendor in writing which experimentation methods, approval controls, SSO and SCIM, and warehouse or export options are included in each tier, whether any are add-ons, and how the price changes when you add seats or environments. Compare those answers with your written GrowthBook scope.
How a move from LaunchDarkly Experimentation to GrowthBook proceeds
The sequence moves experiment analysis onto your own warehouse first, then brings flags and SDK calls across, so each experiment can be checked before the old platform is switched off. The output is a GrowthBook deployment that analyzes experiments from your data without relying on LaunchDarkly's event pipeline.
Confirm the warehouse source and event schema
Your data engineering and analytics leads identify which warehouse holds the exposure and conversion events that LaunchDarkly currently sends through Data Export, and document the table names, user identifier columns, timestamps and event properties. GrowthBook supports a broad list of warehouses and databases, including BigQuery, Snowflake and Postgres, so confirm your engine is on the supported list in GrowthBook's data source documentation. This step is done when a written schema map exists and someone outside the analytics team has reviewed it for gaps, such as events that were only ever sent to LaunchDarkly and never written to the warehouse.
Define metrics in the warehouse
Analysts translate the metrics your experiments depend on into SQL definitions against the warehouse tables, covering conversion, revenue, guardrail and latency measures. Metrics defined here carry over regardless of which flag platform you use, so the work is also a useful audit of definitions that may currently live only inside LaunchDarkly's Experimentation interface. This step is complete when each metric has a named owner, a tested query that returns the expected totals on a sample date range, and agreement from the product owners who read the results.
Deploy GrowthBook in your environment
Platform engineering installs GrowthBook inside your own cloud account or data center, using the official Helm chart for Kubernetes or the Docker image for simpler setups. Plan the metadata database, which GrowthBook stores in MongoDB or a compatible database, and decide whether to use a managed service for production, as the GrowthBook self-hosting documentation suggests. Confirm your edition: the self-hosted Open Source plan does not include SSO and SCIM, approval workflows, sticky bucketing, holdouts or multi-armed bandits, so decide early which Enterprise features your experiment program needs. The step is done when the instance serves its login page over your internal TLS endpoint and backups of the metadata store are tested.
Connect data sources with read-only access
A database administrator creates a dedicated warehouse role that can run SELECT queries only against the approved schemas, then GrowthBook is configured to use that credential. GrowthBook transmits aggregated statistics rather than raw rows, but the least-privilege role still limits what the platform can read if the credential is exposed. Security reviews the role grants and network path, and the team runs a test experiment query. The step is complete when the query returns results, the role is confirmed unable to write or create objects, and the credential is stored in your existing secrets manager.
Import flags with the two-step LaunchDarkly importer
An engineer creates a LaunchDarkly REST API access token with the narrowest role that can read projects, environments and flags, then runs GrowthBook's importer in two stages. The first stage fetches projects, environments and feature flags into a preview; the second imports the pending items, at about one second per flag according to GrowthBook's documentation. The importer aims to carry over fallback values, targeting rules, rollouts and prerequisites, but its documentation warns that advanced setups may include scenarios it cannot handle. Review every preview line against the LaunchDarkly console, list any flags the importer skipped or altered, and rebuild those by hand. The step is complete when the imported flag set matches a signed-off inventory.
Replace the LaunchDarkly SDK in each application
Each service and front end that calls the LaunchDarkly SDK is switched to a GrowthBook SDK, because the importer moves the configuration but does not change application code. Teams can use the OpenFeature route if they want a provider layer between the code and the flag vendor, which keeps future vendor changes contained. Tests should confirm that the same user receives the same variation before and after the change, because a change in the bucketing key or hashing attribute can re-assign users in running experiments. The step is done when each application passes its test suite in staging and a sample of production users shows consistent assignments.
Run parallel analysis
For a defined set of live or recent experiments, the analytics team runs GrowthBook's analysis against the warehouse while LaunchDarkly Experimentation continues to report on the same tests. The two outputs are compared on exposure counts, conversion rates and confidence intervals, and any difference is traced to a cause such as a metric definition, a date boundary or a missing event. Differences that cannot be explained block the cutover. The step is complete when the results agree within the tolerance agreed by the analytics owner, and that tolerance is written down.
Cut over experiments
The team moves new experiments to GrowthBook, closes or freezes running LaunchDarkly experiments at a planned point, and confirms that holdout or sticky bucketing settings you rely on are configured in the edition you deployed. Export the final LaunchDarkly results and keep them under your records policy, because the historical analysis is not automatically recreated in GrowthBook. Remove the LaunchDarkly SDK and token from production once no application depends on them. The step is done when no experiment reads from LaunchDarkly, the exported results are archived and the access token is revoked.
Questions buyers ask before choosing GrowthBook over LaunchDarkly
How much effort does moving from LaunchDarkly to GrowthBook take?
The effort depends mainly on how many flags, environments, segments and experiments you have, and how many applications call the LaunchDarkly SDK. GrowthBook's LaunchDarkly importer fetches projects, environments and flags into a preview, then imports pending items, but the documentation warns that advanced setups can include scenarios it cannot handle. Application work is separate: each service must switch from the LaunchDarkly SDK to a GrowthBook SDK after the import. Ask for a written inventory of flags, experiments and SDK call sites before agreeing on scope, since that inventory drives most of the work. Consider GrowthBook's migration guide when you plan the two-step import and any re-import behavior.
Can our own team migrate from LaunchDarkly to GrowthBook without outside help?
A team with experience running self-hosted services and a clear owner for the flag platform can often handle the import and the SDK changes itself. The harder parts are usually the ones the importer does not cover: rebuilding targeting that relies on advanced rules, deciding how running experiments are carried over, and proving that rollouts behaved as before. Outside help tends to pay off when many teams depend on flags, when experiments are in flight, or when auditors will ask for evidence of the change. Compare the time your engineers would spend on the migration against the fixed scope a partner would agree in writing before work starts.
What does GrowthBook need from our data warehouse and data team?
GrowthBook analyzes experiments against your own data source, so the warehouse must hold the exposure and metric events the experiments need. It supports sources such as BigQuery, Snowflake, Redshift, Databricks, Postgres and Trino, and runs only SELECT queries, transmitting only aggregated statistics. Your data team should confirm that exposure events are logged with the assignment key used for bucketing, that metric definitions match how the business already measures outcomes, and that query costs are acceptable for your warehouse plan. Check the GrowthBook data source documentation against your warehouse before committing.
How long does a LaunchDarkly to GrowthBook migration take?
No duration can be stated from the research available here, so confirm the timeline in writing against your own inventory. The main drivers are the number of flags and experiments, the number of applications that must change SDKs, whether running experiments must finish before cutover, how many environments need validation, and how much of the warehouse data pipeline needs work. Ask any provider for a schedule that separates the import, the SDK changes, parallel running and the final cutover, and for the criteria that mark each stage as complete.
Does GrowthBook's free self-hosted plan cover running experiments?
The free self-hosted Open Source plan includes experimentation against your warehouse, but it is limited to 1 project with unlimited users and unlimited API requests. Several features that teams often need for mature programs are only in the self-hosted Enterprise plan, including SSO and SCIM, advanced permissioning, approval workflows, multi-armed bandits, safe rollouts, ramp schedules, sticky bucketing and holdouts. Confirm with GrowthBook's pricing page which of your current LaunchDarkly experiment features you depend on, and map each one to a plan before you decide.
Which GrowthBook features need an Enterprise license?
The self-hosted Enterprise tier adds the governance and experimentation controls that the free plan leaves out. Based on the pricing page, these are SSO and SCIM provisioning, advanced permissioning, approval workflows, multi-armed bandits, safe rollouts, ramp schedules, sticky bucketing and holdouts. The cost is a custom Enterprise agreement rather than a published per-seat rate, so ask for a quote that lists each feature you need. Also check the license terms: the GrowthBook LICENSE states that code in its enterprise directories is under the GrowthBook Enterprise License, while the rest is MIT licensed.
What happens to historical experiment results after moving to GrowthBook?
Historical results stay where they were recorded. LaunchDarkly's experiment results are held in its platform, while GrowthBook calculates results from the event data in your warehouse, so past outcomes are only portable if the underlying events were exported and remain queryable. Before cutover, decide whether to keep LaunchDarkly available for read-only reporting, export results and decision logs, and confirm that the warehouse retains the exposure and conversion events needed to recalculate key metrics. Ask your provider what will be archived, in which format, and who will sign off that the archive is complete.
How DeployOpen helps evaluate, implement or migrate GrowthBook
DeployOpen is a Chainscore Labs company that plans, implements, integrates, hardens, backs up and documents self-hosted open-source platforms inside your own infrastructure. For LaunchDarkly vs GrowthBook, we start by settling where your event data lives and which statistics questions matter, then build and hand over a GrowthBook deployment that fits that answer.
How we scope
- Engagements are fixed-scope, with deliverables and timelines agreed in writing before work starts.
- We map which LaunchDarkly Experimentation and Data Export features your teams use today against GrowthBook's free and Enterprise capabilities.
- We confirm your warehouse, data sources and metric definitions, and whether GrowthBook's self-hosted Enterprise features are needed for your workflow.
How we build
- We deploy GrowthBook in your cloud account or data center using its Docker image or official Helm chart, with MongoDB or a compatible database for metadata.
- We configure read-only warehouse connections so experiment queries run against your own data source and only aggregated statistics leave it.
- We test flag evaluation, SDK integration and experiment results against a baseline from your current platform before any cutover.
How we hand over
- You receive documentation covering deployment, data source configuration, access roles, backup and upgrade procedures for your GrowthBook instance.
- We document the steps to replace the LaunchDarkly SDK in each application and the checks to run before and after switching.
- We walk your engineering and analytics teams through running experiments, reviewing results and handling metric changes.
How we support
- Ongoing support, upgrades and continuous testing are available as a retainer after handover.
- Upgrades are planned and tested against your warehouse connections and SDK versions before they reach production.
- Where you need an independent assessment of your controls, we prepare the systems and evidence, while auditors and certification bodies issue any report.
