Do I need to be technical to use EvalGate?

You do not need to write code to understand a test result or discuss the standards your AI should meet. Connecting an application and configuring tests may require an engineer or a coding agent.

Does EvalGate automatically fix or retrain my AI?

No. Finding a failure, fixing it, approving the fix, and training a model are different actions. EvalGate helps your team evaluate behavior and retain evidence; the supported workflow and its permissions determine what can happen next.

What does a passing result mean?

It means the tested example met the configured checks. It does not prove that every risk was tested or that future behavior is guaranteed. Missing required evidence is not a pass.

What counts as one billed result?

One saved test-case outcome counts as one result, whether it passes or fails. Running several scorers on that outcome does not multiply the result count. AI model usage is billed separately by your model provider.

Can an agent buy EvalGate usage on its own?

An authorized administrator sets up paid usage through hosted checkout. Do not treat that handoff as a fully autonomous payment API. Paid result usage is uncapped by default; review billing before authorizing an agent to incur costs.

Which integrations does EvalGate support?

EvalGate supports GitHub Actions, a GitLab CI recipe, OpenTelemetry/OpenInference OTLP, LangChain, CrewAI, AutoGen, Vercel AI SDK, signed webhooks, and Slack delivery. Dedicated CircleCI, Teams, Jira, PagerDuty, and LlamaIndex integrations are not currently advertised.

Does EvalGate support shadow releases?

Deployable Assets can run a non-serving shadow candidate for requests routed through EvalGate's governed invocation endpoint. Shadow failures become evidence without replacing a valid serving response; arbitrary bypass traffic is not mirrored.

Vendor comparison · Reviewed October 14, 2026

Langfuse vs EvalGate: tracing, release gates, or both?

Langfuse is a strong fit for teams seeking open-source or self-hosted LLM observability. EvalGate focuses on reviewed behavior contracts, approved baselines and regression decisions in CI. They can complement one another rather than requiring a replacement.

Choose Langfuse when

Self-hosted tracing, prompt observability and open-source workflows.

Choose EvalGate when

Local or CI regression gates that protect reviewed outcomes.

What each product does better

EvalGate	Langfuse
Code-reviewed baseline and explicit regression result linked to a change.	Open-source and self-hostable telemetry workflows.

Pricing

Langfuse offers a free cloud tier, paid options and self-hosting; compare your actual usage units.

EvalGate's local gate can run without a hosted account. Hosted result charges and separate model-provider inference fees are not equivalent to a competitor's trace, seat or token units.Compare EvalGate cost models

Verify current Langfuse terms and product documentation

Limitations

EvalGate does not replace Langfuse's self-hosted observability stack.

How to migrate or run both

Retain Langfuse traces and instrumented applications. Use real incidents to create reviewed EvalGate tests; keep both if they serve different needs.

Test a real regression

The public EvalGate refund-agent example reproduces a passing baseline, a breaking change that gets blocked, and a corrected passing run using the published SDK without signup. This is evidence for the checked behaviors, not proof of universal safety.

Open executable example

EvalGate remains controlled beta. See thecurrent feature-status inventorybefore using a release control in production.