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

DeepEval vs EvalGate: what should Python teams use?

DeepEval suits Python teams seeking pytest-native LLM evaluations and a broad library of quality metrics. EvalGate is an option for local behavior gates used across TypeScript and Python, with approved baseline lineage and optional hosted review.

Choose DeepEval when

Python-first, metric-oriented LLM testing using pytest.

Choose EvalGate when

Reviewed cross-language application behavior and CI regression gates.

What each product does better

EvalGate	DeepEval
One regression-gate workflow across Python and TypeScript.	Python-native testing patterns and extensive metric support.

Pricing

DeepEval is available as an open-source framework; hosted Confident AI is a separate offering.

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 DeepEval terms and product documentation

Limitations

Do not assume full parity with DeepEval's Python metric catalog.

How to migrate or run both

Retain pytest and DeepEval tests. Translate a few critical behavioral cases into EvalGate, verify the results and add a CI gate only after real execution.

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.