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Split.io

An enterprise feature delivery platform that provides feature flagging, experimentation, and data-driven deployment capabilities.

Our take: Acquired by Harness in 2023, Split.io now operates as Harness Feature Management & Experimentation (FME) — though the split.io domain and brand persist.

Best forSplit.io is designed for enterprise engineering teams, product managers, DevOps teams, and large organizations that need robust, scalable feature flagging and experimentation capabilities with enterprise-grade reliability
ReviewedPricing: Freemium
7.3 out of 105 criteria · see breakdown
Try Split.ioFree (10 seats/10 flags); paid ~$33/seat/mo
The bottom line

Best-in-class enterprise feature flagging with native experimentation — now embedded in Harness's full DevOps suite.

Our score

Reviewed by the Conversion Gems editorial team. Hover a facet to focus it; click to pin.Tap a facet to focus it.

Community ratings

Shown for context only. Our score is independent.

G2
4.6 out of 5
300+ reviews

4.6 / 5 aggregate · across 1 source

Third-party ratings shown verbatim; aggregate weighted by review volume.

What it is, and why we rate it

One line each. Open a row for detail.

What it really isSplit.io (Harness FME) — enterprise feature flagging and experimentation platform.
Our takeAcquired by Harness in 2023, Split.io now operates as Harness Feature Management & Experimentation (FME) — though the split.io domain and brand persist.
Split.io is a feature delivery and A/B testing DevOps tool, and a free Developer tier with 10 seats and 10 flags is available. Core differentiator: SDK-side flag evaluation means targeting logic fires locally, never exposing user data to Split's servers.
Why we rate itSplit pioneered the 'flag + metric' model, connecting every release to measurable business outcomes.
Its architecture is more private and lower-latency than cloud-dependent competitors, and the Harness acquisition adds full CI/CD pipeline context few rivals can match.
The catchFull experimentation capabilities and enterprise governance require the Enterprise tier (custom, contact sales); small teams hit free limits quickly and Growth plan pricing is not self-serve transparent.

Best / not good for

Best for
  • Enterprise engineering teams managing progressive feature delivery at scale
  • Product teams wanting experiments tied directly to feature flag rollouts
  • DevOps/platform teams integrating feature management into CI/CD pipelines
Not good for
  • Small startups needing transparent, self-serve flat pricing
  • Teams requiring open-source or self-hosted feature flag options
  • Non-technical stakeholders wanting no-code feature toggle management
Trade-offs

Friction report

Trade-offs to know before signing up.

  1. Time to value
    Moderate: SDK integration takes hours, not days; free tier onboards quickly but meaningful experimentation requires additional setup and data wiring.
  2. Scale breakpoint
    MTK-based billing escalates at high-traffic volumes; enterprise contracts required for advanced compliance and governance features.
  3. Walled garden
    High: flag history, experiment results, and targeting rules are tightly coupled to the Harness/Split platform with limited portability.
Still a fit?
If none of these block you, start on the free plan.
Try Split.io

A look inside

Split.io product screenshot

Frequently Asked Questions

What is “Branch AI” and why does it exist?

Branch AI is a workflow-automation platform that integrates natural-language processing with business-process automation to let users generate, edit, and route workflow items without leaving their existing software stack. It is designed for teams that need to automate repetitive tasks like ticket routing, approval routing, or data-capture from disparate sources.

How does the AI ensure accuracy when generating new content?

The system uses a hybrid approach that combines deterministic rule-based routing with a transformer-based language model trained on millions of published documents. Before an output is considered final, the system runs a sanity-check that compares the generated content against the source’s original metadata, ensuring that no false claims are generated.

Can the AI be taught new policies or workflow rules?

Yes – the platform allows administrators to upload a custom “policy file” that encodes the organization’s internal rules (e.g., email-routing thresholds, escalation thresholds, data-privacy constraints). Those policies are compiled into the AI’s workflow engine and applied to every new output, keeping all generated content aligned with existing procedures.

What level of accuracy can users expect?

Benchmark testing shows a 92 % success rate on typical routing-and-approval tasks, with a 93 % confidence score on content that meets both readability and compliance standards. However, for highly specialized or regulated sectors (finance, healthcare, legal), a final human-review step is still recommended.

How does the system protect user data and comply with privacy standards?

All processed data is encrypted in transit and at rest, follows SOC 2 Type II and GDPR best practices, and stores only the minimal metadata required for workflow execution. Role-based permissions and audit-trail logging are built-in to control who can view or modify the generated content.

Alternatives

  • Step up

    LaunchDarkly for more mature enterprise governance controls and a larger flag-management ecosystem.

  • Lighter alternative

    Flagsmith (open-source/self-host) or PostHog feature flags for smaller teams wanting cost transparency.

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Tags

#Experimentation#ProductAnalytics#ABTesting

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