Open-source Optimizely Web alternative

Is GrowthBook an open-source alternative to Optimizely Web Experimentation?

GrowthBook can replace Optimizely Web Experimentation for marketing and CRO tests only after your team confirms that the visual editor and script tag are included in the plan you intend to run. Check this on the GrowthBook pricing page before you commit to anything. The free self-hosted Open Source plan lists unlimited users, 1 project and unlimited API requests, but the visual editor is the feature that determines whether your marketers can build tests without developers. Your renewal probably followed a change in MAU billing, which counts every visitor that loads the Optimizely snippet. GrowthBook's paid cloud tiers bill per seat, so ask both vendors how each would count your traffic.

This page covers what the self-hosted tier includes, how visitor counting changes, where client-side-rendered apps create limits for script-based tests, and how to move your existing tests. Code-level flags fit some applications better. If your experiments change application logic in a React or other single-page app, rather than only page markup, a flag evaluated by the SDK in your code gives more control over timing, targeting and rendering than a script tag does. Sort your current experiments into two groups now: in-page marketing changes and changes that need the application to evaluate a flag.

Before you commit, confirm three points in writing with GrowthBook: whether the visual editor on the plan you choose supports the pages and frameworks you test, whether a script-tag integration works with your rendering model, and whether one project is enough for how your teams organize experiments. Then inventory your Optimizely tests, goals and audiences, because the migration path for web experiments is a manual rebuild and not a one-click import. Ask your implementation partner to scope that work separately from the cost of the GrowthBook servers and the people who will run them.

Which Optimizely Web Experimentation capabilities GrowthBook covers

This table maps each main Optimizely Web Experimentation capability to the GrowthBook feature or workaround that covers it, along with the plan where it sits. Where the GrowthBook documentation does not confirm a feature for the web use case, the coverage note says to verify it before you plan the migration.

Optimizely capability	GrowthBook feature or workaround	Plan to check	Coverage note
Visual editor	GrowthBook visual editor for page changes	Confirm on the pricing page for self-hosted plans	Test it on your real page templates, including single-page apps, before assuming parity
Script or snippet deployment	GrowthBook SDK loaded through a script tag on each page	Self-hosted Open Source, confirm	Swap the Optimizely snippet in your tag manager and run both during a parallel period
Audience targeting	Targeting rules based on attributes your site passes to the SDK	Confirm whether segments are on Open Source	Audiences must be rebuilt from the attributes you send, so audit what your site currently passes
Goals and metrics	Metrics defined as queries against your own data source	Self-hosted Open Source with a connected data source, confirm	Optimizely goals do not carry over automatically, so each metric needs a warehouse definition
Traffic allocation	Variation weights and percentage rollouts on experiments	Open Source for weights; Enterprise self-hosted for ramp schedules and sticky bucketing	Basic weights are covered, while ramp schedules and sticky bucketing sit on the Enterprise plan
Results reporting	Experiment results calculated by the GrowthBook statistics engine on your data	Self-hosted Open Source with a connected data source, confirm	Results are only as good as your event pipeline, so check them against the Optimizely export before cutover
Holdouts and safe rollouts	Holdouts and safe rollouts available as experiment options	Self-hosted Enterprise	Optimizely users who rely on long-running holdouts should budget for the Enterprise plan
Personalization	No direct equivalent confirmed for Optimizely personalization campaigns	Verify with GrowthBook documentation	Treat as a gap to scope, with targeted feature rules or custom code as the likely workaround

Which GrowthBook setup fits a web testing program

Each option trades cost, control and effort differently. The free self-hosted edition, the paid self-hosted and cloud tiers, and code-level flags through OpenFeature answer different questions. Match the setup to the kind of test you run and to who will build and launch the variants.

Self-hosted Open Source

The free self-hosted edition, run from GrowthBook's single Docker image with MongoDB behind it. It allows one project with unlimited users and unlimited API requests, so one web testing program with a small team can start without a license cost, according to the GrowthBook pricing page. The ceiling matters once a second brand, site or business unit needs its own experiments. Confirm whether the visual editor and script tag are included in this tier before you plan around them.

Self-hosted Enterprise

The commercial self-hosted tier adds SSO and SCIM, advanced permissioning, approval workflows and holdouts, according to the GrowthBook pricing page. These controls matter when several marketers or developers launch tests on the same site and changes need review before they go live. The cost is a license on top of the servers, the MongoDB service and the people who run them, so ask for a written quote. Ask the vendor in writing whether the visual editor and script tag are included in this package.

Cloud Pro

GrowthBook hosts the platform for you and bills $40 per seat per month for up to 30 users and 3 projects, according to the GrowthBook pricing page. It suits a team that wants the platform run for it and has several sites or brands to test. Costs grow with every seat, so count the people who will edit tests and set up audiences before you commit. Confirm whether the visual editor and script tag are on this plan, and review the data-handling terms for any event data sent to the cloud.

Flag-based experiments in code

Here the test lives in application code, and the variant is read through an OpenFeature provider. That keeps the evaluation API independent of any single flag vendor, so the same code can move between platforms later. OpenFeature reached the Incubating maturity level at the CNCF on November 21, 2023, and its SDKs cover React and Angular on the web. This setup fits logged-in features and server-side logic that a visual editor cannot reach. The trade-off is engineering time for each variant and a release for every change. Confirm the provider for your language and framework.

Which GrowthBook plan includes the visual editor and script tag

Teams that run tests on their own site, and that do not need approval gates or single sign-on, usually start with the self-hosted Open Source plan. The GrowthBook pricing page lists it as free, with unlimited users, one project and unlimited API requests. The cost sits in the servers, the MongoDB store and the people who operate them, not in seats.

Users: Unlimited users means marketers, analysts and developers can all sign in without a per-seat charge.
Projects: One project is the main constraint. Separate brands or sites that need their own workspaces will have to share a project or move to a paid tier.
API requests: The plan lists unlimited API requests, so automation that pulls experiment data is not capped at the plan level.
Operations: You run the Docker image or Helm chart, the database and every upgrade yourself.

Confirm on the current pricing page that the visual editor and script tag are included in the self-hosted Open Source plan before you budget, because this path should not be assumed to include them.

Does the open-source alternative to Optimizely Web Experimentation change how visitors are counted?

Yes. Optimizely Web Experimentation bills on monthly active visitors, and its snippet counts every visitor on a page where it runs. A self-hosted GrowthBook deployment on the Open Source plan has no per-visitor charge. That plan is free with unlimited users, one project and unlimited API requests. GrowthBook's paid Cloud tiers bill per seat instead, at $40 per seat per month on Cloud Pro for up to 30 users and 3 projects, so the charge follows the people who run experiments rather than the traffic those experiments reach. Under Optimizely, a campaign that doubles visits can raise the bill directly. Under self-hosted GrowthBook, the same campaign adds load to your servers and warehouse, but it does not add a license line.

Running GrowthBook yourself shifts those costs to your own infrastructure and staff. The GrowthBook front end, Express API and Python statistics engine ship in one Docker image, and metadata lives in MongoDB or a compatible database, which the self-hosting documentation suggests running as a managed service in production. Servers, database hosting, file storage for uploads (local, Amazon S3 or Google Cloud Storage) and the people who upgrade, back up and monitor the stack are recurring costs. GrowthBook also runs SELECT queries against your own warehouse, so analysis consumes warehouse compute. That cost rises with event volume and the number of experiments you analyze, so it grows with traffic in a way the license does not.

Before you compare totals, confirm how your current Optimizely contract defines a monthly visitor and whether it counts visitors on every page or only in active experiments. Export a recent month of counts and check them against your analytics. Ask your data team to estimate warehouse query volume for your planned experiments, and confirm whether your warehouse charges for query compute or runs on fixed capacity. Ask your platform team to size MongoDB and hosting. If you need SSO, SCIM or approval workflows, those sit in the self-hosted Enterprise tier, so request a written quote for it. Finally, check the GrowthBook pricing page for which tier includes the visual editor you rely on.

Where a script tag test breaks on client-side-rendered apps

Optimizely's snippet and a GrowthBook script tag both change a page after it renders, which is most predictable on server-rendered marketing pages. The four areas below are where teams most often find gaps in client-side-rendered applications. Run each test in your own app before you commit a test plan, and confirm the mechanisms GrowthBook's documentation supports for your stack.

Content flicker before the variant applies

A script tag applies its changes after the page renders, so visitors can briefly see the original headline or layout before the variant appears. This matters most where the first view carries the offer or the call to action. In your app, throttle the network in browser developer tools, load the target page repeatedly, and record the frames where the original shows, then check the GrowthBook documentation for the anti-flicker mechanisms it supports.

Single-page app route changes

In a single-page app, moving between views usually swaps content without a full page load, so a script that ran only on the first request may not apply the variant again. The variant can vanish after in-app navigation, or exposures can log on pages the test was never meant to cover. Click through every route in the test, use the back and forward buttons, and confirm the variant holds and the exposure fires once per intended view.

Elements created after load

Components that mount after an API response, lazy-loaded modules, infinite scroll and virtualized lists create elements after the initial load. Selectors that match only after a fast load can find nothing for a visitor on a slow network, so the change never applies. Target a component that mounts late, test it on a throttled connection, and confirm how GrowthBook's documentation handles elements that appear after the script runs.

Logged-in and personalized content

Pages behind login often render content from account data on the server, and the variant may depend on an identifier the browser script never sees. The same visitor can land in different groups when they use two devices or sign out in the middle of a session. Test a signed-in account page in two browsers, sign out, and compare assignments, then decide how the user ID will reach the test and confirm which identifiers GrowthBook supports.

Running GrowthBook beside Optimizely during the transition

The safest way to run GrowthBook beside Optimizely is to decide, for every experiment, which tool is the system of record and to keep the two from touching the same visitor and the same element. Most teams run both snippets for a period, assign each live test to exactly one tool, and move tests across one at a time as their GrowthBook setup is verified. Before any page loads both tags, confirm that each visual test has a single owner, that the owner is recorded in your test register, and that the other tool is configured to skip that page or URL pattern. A test that is paused in one tool but still active in the other is the most common source of confusing results.

The main technical risks are double-counted visitors, conflicting variant assignments and inconsistent consent handling. If an Optimizely snippet and a GrowthBook script both assign a visitor to a variant that changes the same heading, button or price, the page can show two versions in sequence and the visitor's exposure is recorded in both systems. Run only one visual editor change per element, and keep flag-based experiments in code on separate pages or separate components from any Optimizely or GrowthBook visual test. Also confirm which tool sets cookies or local storage identifiers for the visitor, because two identifiers for one person inflate unique visitor counts and complicate MAU reconciliation, which is covered in the cost section.

Consent and data handling need a single decision, made before either script loads. Document which tags fire before and after consent, which regions get which banner, and where event data goes: Optimizely's servers under its contract, or your own warehouse and GrowthBook's read-only queries under your GrowthBook data source documentation. Ask your privacy and legal teams to sign off on one consent flow that gates both tools, and test it in a staging environment by declining consent and confirming that neither tool sets identifiers. Keep a dated record of the decision, because a split consent model across two vendors is hard to explain to an auditor or regulator later.

How to move active web tests from Optimizely to GrowthBook

This sequence moves marketing and conversion tests from the Optimizely Web snippet to a self-hosted GrowthBook instance without a built-in importer. It ends with verified test assignments and metric counts on a cut-over site and a retired Optimizely snippet.

Inventory active and paused web experiments
The marketing or CRO lead lists every active and paused web experiment in Optimizely, with its target URLs, audience conditions, traffic allocation, variants, and the goal events each test measures. Record which tests are live, which are paused, and which share pages or audiences, because overlapping tests need to be handled together. Export the current configuration and the experiment results so you have a baseline. The step is done when every test has a row in one inventory sheet and someone other than the author has reviewed it.
Decide which tests move and which move to code
Work through the inventory with product and engineering leads and classify each test. Tests that change copy, layout, or styling on server-rendered marketing pages fit GrowthBook's visual editor and script tag. Tests that change logic inside a client-side-rendered application are usually better rebuilt as flags in code, using a GrowthBook SDK or OpenFeature. Mark tests that you will retire rather than migrate. The output is a decision column in the inventory with a named owner for each test that moves to code.
Install self-hosted GrowthBook with MongoDB
The platform team deploys GrowthBook in your own infrastructure. GrowthBook ships its Next.js front end, Express API, and Python statistics engine in one Docker image, and it maintains an official Helm chart for Kubernetes. Metadata is stored in MongoDB or a compatible database, and the self-hosting documentation suggests a managed service such as MongoDB Atlas or AWS DocumentDB for production. Confirm which plan includes the visual editor and script tag before you commit, since the free self-hosted plan may not include them. The step is done when the instance runs behind your access controls and backups cover the database.
Connect the warehouse and rebuild goal metrics
The analytics engineer connects GrowthBook to the warehouse that holds your conversion events, such as Snowflake, BigQuery, or Postgres. GrowthBook runs only SELECT queries and transmits aggregated statistics, so the data stays under your control. Rebuild each goal from your Optimizely goal definitions as a metric that reads the same events, and document any event that differs in naming or timestamp. The output is a set of metrics that return values for a known date range, which the next step will check against Optimizely.
Recreate each test on a staging site
The front-end lead installs the GrowthBook script tag on a staging copy of the site, in place of the Optimizely snippet, and recreates each test that was marked for migration. Set up the same URL targeting, audience conditions, variant changes, traffic split, and goal metric for each test, and confirm the variants render on each target page. Keep the Optimizely snippet off staging so the two tools do not change the page at the same time. The step is done when every migrated test renders correctly on staging across the browsers and devices your traffic uses.
Verify assignments and metric counts
The analytics lead compares GrowthBook with the Optimizely export for the same date range. Check that the share of visitors in each variant is close to the configured split, that the number of exposed users matches, and that conversion counts for each goal line up within an agreed tolerance. Investigate differences caused by bot filtering, cookie handling, sticky bucketing, or event timing before accepting them. The output is a short reconciliation report signed off by the owner of each test, which is the evidence you need before cut-over.
Cut over and retire the Optimizely snippet
The release owner pauses the Optimizely experiments that have been reconciled, deploys the GrowthBook script tag to production, and starts the migrated tests in GrowthBook. Watch the first live data for errors, flicker, or assignment drift, and keep the Optimizely snippet available until the reconciliation holds on live traffic. Once the migrated tests are stable, remove the Optimizely snippet from the site and archive the final export with the inventory and the reconciliation report. Confirm the end state with the contract owner before ending the Optimizely subscription.

What does an open-source web testing migration cost and require?

What does an open-source web testing migration cost compared with the software?

The GrowthBook software has no license fee on the self-hosted Open Source plan, but the migration is paid work and the platform carries recurring running costs. That plan lists unlimited users, 1 project and unlimited API requests, according to the GrowthBook pricing page. Confirm current prices there, since cloud tiers and self-hosted Enterprise features are priced separately. Costs fall into four groups: one-time migration work (inventory, rebuilding tests, metrics and snippet placement, QA), recurring infrastructure (application servers, MongoDB or a compatible database, backups), warehouse query costs for analysis, and staff time to operate the platform. Ask any quote to separate these lines so you can see which costs are one-time and which recur every year.

How long does a migration take and what sets the timeline?

The timeline depends on the scope agreed in writing, and no single duration applies to every site. The main drivers are the number of active and archived tests, how many pages and variants each test touches, and how many goals and metrics must be rebuilt on your warehouse. Whether analysis must match historical Optimizely results also adds review time. Pages with complex targeting, personalization or custom JavaScript take longer to verify. Staging availability, approval cycles and your change board set the pace as well. Ask for a phased plan that lists inventory, pilot tests, a parallel run and cutover, along with what each phase needs from your team before any date is committed.

What must our team provide before a migration starts?

Four inputs decide whether work can start on schedule:

Traffic and event data: historical volumes for each tested page, plus event definitions, so sample sizes and metrics can be checked.
A staging site: a copy of production pages where the snippet or script tag can be placed, tested and reviewed without affecting visitors.
Warehouse access: read-only access to the warehouse holding conversion data, such as Snowflake, BigQuery or Postgres. GrowthBook runs only SELECT queries and transmits only aggregated statistics, according to the GrowthBook data source documentation.
A named owner: one accountable person who approves test designs, metric definitions and cutover, and who settles disagreements.

Confirm access paths with your security team early, since warehouse credentials and staging access usually need formal approval.

Can our internal team migrate from Optimizely without outside help?

A capable internal team can do it, but the work spans skills that marketing-led teams often hold separately: web tagging, SQL against the warehouse, statistics review and platform operations. Running GrowthBook yourself means operating its application image, its MongoDB or compatible database, and upgrades. GrowthBook's Helm chart supports Kubernetes deployments, and v5.1.0 was released on September 22, 2026, so plan for a regular upgrade cycle. Internal staff time is still a cost, even when no invoice is issued. Many teams split the work: internal staff own test ideas and content, while outside help scopes the migration, rebuilds metrics and sets up the platform. Decide that split in writing before work starts.

How does MAU billing change when we move off Optimizely?

The per-visitor billing model changes, but the cost drivers move elsewhere. The Optimizely pillar describes Web Experimentation as counting every visitor on a page with the snippet, which ties license cost to traffic. GrowthBook's cloud tiers bill per seat, with Cloud Pro listed at $40 per seat per month on the GrowthBook pricing page at the time of research, so confirm that figure before budgeting. The self-hosted Open Source plan has no seat cap or API request limit. Visitor growth still raises server load, database size and warehouse query volume, so those infrastructure lines grow with traffic. Check your current Optimizely order form for its own MAU definition and any overage terms before you set the cutover date.

Can the script tag test a client-side-rendered app?

The script tag can test some pages in a client-side-rendered app, but single-page applications need extra checks before you rely on it. Visual edits run in the browser after the page loads, so client-side route changes, hydration timing and content flicker can all affect what visitors see and how results are read. Test on the rendered DOM in staging, and check each route the experiment touches. Confirm the current limits in the GrowthBook documentation, since this guide does not list them. For changes inside application logic, such as pricing displays, layouts or feature code in React or Angular, flag-based experiments in code are usually the better fit. The OpenFeature standard supports web SDKs for both frameworks, which lets application code call one interface.

How DeployOpen scopes and delivers a web testing replacement

DeployOpen plans, implements, customizes, integrates, hardens, documents and supports self-hosted open-source platforms inside your own infrastructure. For a web testing replacement, we start from the tests you actually run, the pages and sites they touch, and the data residency rules that apply to visitor data.

How we scope

We inventory live Optimizely Web snippets, active tests, audiences and goals before any design work starts.
The written scope names which sites, templates and single-page apps receive the GrowthBook script tag and which stay on existing tooling.
Engagements are fixed-scope, with deliverables and timelines agreed in writing before work begins; a quote follows from that scope.

How we build

We deploy GrowthBook in your cloud account or data center, with MongoDB or a compatible database and the official Helm chart where Kubernetes is used.
We configure the script tag or SDK path, consent handling and attribute mapping so variant assignment matches your current behavior.
We connect GrowthBook to your warehouse with read-only SELECT access and test metric queries against known results before any live test.

How we hand over

You receive documentation of the deployment, the test configuration, the metric definitions and the warehouse access model.
We run a parallel validation period where results from the old and new setup are compared and any gaps are recorded for your decision.
Runbooks cover backups, GrowthBook upgrades, rollback to the Optimizely snippet and who approves new tests.

How we support

Ongoing support, upgrades and continuous testing are available as retainers, scoped in writing like the initial engagement.
Security testing of the deployed GrowthBook instance and its exposed endpoints can be included in the scope if you request it.
We prepare configuration and evidence for your compliance assessors; independent auditors and certification bodies issue any formal reports.

Open-source alternative to Optimizely Web Experimentation