The open-source Optimizely alternative: experiments in your repo
dif is an open-source, git-native alternative to Optimizely. Where Optimizely keeps experiments and feature flags in a hosted platform, dif keeps them as Markdown files in your repo, reviewed in pull requests and versioned in git. If you want experimentation a developer runs from the codebase, not a dashboard aimed at marketers, that is the choice this post is about.
Optimizely is a mature experimentation platform, now part of a broader digital experience suite. It is strong at web and marketing experimentation, and its free Rollouts tier covers basic feature flags. dif is a smaller, focused tool with a different model: one file format for flags and experiments, an open-source CLI and SDK, and state a coding agent can read. This post compares the two fairly and says who each is for.
Key Takeaways
- dif keeps flags and experiments as Markdown files in your repo. Optimizely keeps them in a hosted platform, configured through a dashboard.
- dif is open source (MIT) and self-hostable. The CLI and SDK are free, and dif Cloud is $50/mo for 1M events with unlimited seats. Optimizely’s full platform is enterprise, quote-based, and priced in the tens of thousands per year.
- Optimizely wins on mature statistics, a visual editor for web tests, and personalization. dif does not try to match those.
- dif emits a
context.jsona coding agent reads on session start. A dashboard-based platform cannot hand that to an agent.- dif’s honest cost: rollback is a merge and a deploy, not an instant dashboard toggle.
At a glance
| Dimension | dif | Optimizely |
|---|---|---|
| Where flags and experiments live | Markdown files in your repo | Optimizely's hosted platform |
| License | Open source (MIT), self-hostable | Closed source, SaaS |
| Free tier | Free CLI and SDK, full workflow | Rollouts, free feature flags |
| Paid pricing | dif Cloud, $50/mo for 1M events, unlimited seats | Enterprise, quote-based (tens of thousands/yr) |
| Runtime SDK | ~5 kB, no network call to assign | Full-stack SDK |
| Experimentation | File-based, dif Cloud for stats | Mature stats, web and server-side, personalization |
| Agent-readable state | Yes (context.json) | No |
| Roll back a change | Edit a number, merge, deploy | Toggle in the dashboard |
Where flags and experiments live
This is the difference the rest follow from. In dif, a flag or an experiment is one Markdown file in dif/, next to the code it changes. It has frontmatter on top (owner, surface, variants, weights, metric) and the rationale below. It gets reviewed in a pull request, its history is the git history, and a concluded experiment writes its decision back into the file.
In Optimizely, the flag and the experiment live in the platform. You configure them in the dashboard, and the code references them through the SDK. That is a familiar model, and for a marketing team running web tests it is the right one. For an engineering team, it means the source of truth for a flag sits outside the repo, so the reason a flag exists is a dashboard lookup rather than a file in the diff. dif’s argument for feature flags in git is that the flag belongs where the code it gates already lives.
Open source and self-hosting
dif is MIT licensed. The CLI and SDK are free and open, and dif Cloud is self-hostable, so you can run the whole thing without sending data to a vendor. If dif disappeared tomorrow, your flags are still files in your repo and the SDK is still on your disk.
Optimizely is a closed-source SaaS platform. That buys you a managed service and a support contract, which some teams want. It also means the experimentation engine is a vendor’s, not yours, and leaving takes a migration. For teams that prefer to own the tool and the data, the open-source model is the reason to look at a dif alternative in the first place.
Pricing
Optimizely’s Rollouts tier is genuinely free for basic feature flags, so “free to start” is not a dif-only claim. The difference is above that line. Optimizely’s full experimentation platform is sold enterprise, quote-based, with no public self-serve price. Third-party estimates put entry contracts in the tens of thousands of dollars per year and higher tiers well past a hundred thousand, depending on traffic and modules.
dif’s pricing is flat and public. The CLI and SDK are free forever. dif Cloud is $50/month for 1M events with unlimited seats, then $0.02 per 1,000 events after that. There is no per-seat fee and no sales call to start. For a small engineering team that wants experiments in the repo without an enterprise contract, that gap is the point.
Experimentation and statistics
Optimizely wins this row, and it is worth saying plainly. A decade of work has gone into its stats engine, its visual editor for building web tests without code, and its personalization and targeting. If you run marketing experiments on a content site, or you need a mature sequential-testing stats model out of the box, Optimizely is built for that and dif is not trying to be.
dif’s experimentation is developer-first and newer. An experiment is the same Markdown file as a flag, with a hypothesis and a held split. Assignment is deterministic and local: the variant is a hash of the user ID, so there is no network call to assign and a user never flickers between variants. dif Cloud handles the statistical analysis and writes results back to your pull request, but that analysis stack is younger than Optimizely’s. A/B testing for developers covers how the experiment lifecycle works.
Agent-readable state
This is where dif has something Optimizely does not. dif build emits a context.json that lists every active flag and each surface’s latest learning, and a coding agent like Claude Code or Cursor reads it on session start. dif init also writes managed blocks into CLAUDE.md and AGENTS.md.
A coding agent cannot log into the Optimizely dashboard, so it cannot see which flags exist there or why. As more of a codebase is written and maintained by agents, feature flags for AI agents that live in files, not a dashboard, are readable state the agent can actually use. No dashboard-based platform gives an agent that today.
The honest tradeoff
dif rolls back by editing a weight to 0 and merging. That is a deploy, not a dashboard toggle. If your build takes ten minutes, your worst-case kill is ten minutes. Optimizely can flip a flag in the dashboard in seconds. For a payment path that needs a sub-second kill switch, that speed matters, and dif is the wrong tool for that specific flag.
dif is also new and small next to Optimizely. Fewer integrations, a younger analytics stack, and none of the web and personalization tooling. The case for dif is not that it does everything Optimizely does. It is that for flags and experiments in the repo, owned and open, a developer moves faster without the platform.
Who each is for
Choose Optimizely if you run marketing or web experiments with a visual editor, need a mature stats engine and personalization, or want a managed enterprise platform with a support contract.
Choose dif if you want flags and experiments as files in your repo, reviewed in PRs, on an open-source tool you can self-host, with a 5 kB SDK and state your coding agent can read. dif is built for engineering teams that would rather own the flag than log into a dashboard.
FAQ
Is there an open-source alternative to Optimizely? Yes. dif is an open-source, MIT-licensed alternative where flags and experiments are Markdown files in your repo. The CLI and SDK are free and self-hostable, and dif Cloud is an optional hosted layer for statistical analysis.
How is dif different from Optimizely? dif keeps flags and experiments in your repo as files, reviewed in pull requests. Optimizely keeps them in a hosted platform configured through a dashboard. dif is open source and developer-first; Optimizely is a mature, closed-source platform strong at web and marketing experimentation.
Is dif cheaper than Optimizely? The dif CLI and SDK are free, and dif Cloud is $50/month for 1M events with unlimited seats. Optimizely’s Rollouts tier is free for basic flags, but its full experimentation platform is enterprise and quote-based, commonly in the tens of thousands per year. For a small engineering team, dif is far cheaper.
Does dif do everything Optimizely does? No. Optimizely has a mature stats engine, a visual editor for web tests, and personalization that dif does not match. dif focuses on flags and experiments in the repo, an open-source SDK, and agent-readable state.
Can I migrate from Optimizely to dif? You re-author each active flag as a Markdown file and swap the SDK call at the render site. Because dif flags are plain files, there is no export format to reconcile; you write the flags you want to keep and delete the rest.
Getting started
dif is the open-source Optimizely alternative for teams that want experiments in the repo instead of a dashboard. Install the CLI and scaffold a project:
npm install -g @dif.sh/cli
dif init
dif init writes the dif/ directory, the generated client, and the managed agent blocks. From there you write a flag as a file, gate the code, and run dif validate in CI. The feature flags page shows the model, and dif Cloud pricing covers the optional hosted analytics.
Optimizely’s product names, tiers, and pricing come from Optimizely’s free feature flagging page and third-party pricing data on G2. Verify current pricing with Optimizely, since enterprise contracts are negotiated and change.