DeepEval

Overview

DeepEval brings evaluation into a developer-owned test runner: checks can run as Pytest-native tests in CI/CD or as Python scripts, with results kept in local files. That approach suits pre-production quality gates and regression testing, but the maker describes DeepEval OS as limited to pre-production use. Teams that need shared workspaces, no-code evaluation, or annotation workflows will need a different layer.

Key features

The breadth of its evaluation toolkit is a clear strength. DeepEval offers more than 50 research-backed metrics, spanning hallucination, faithfulness, answer relevance, summarization, toxicity, and bias, and supports text, image, and audio evaluation, including conversational and voice use cases. G-Eval, DAG, QAG, and JevEval provide several evaluation approaches rather than locking teams into one method.

For teams without a large labeled dataset, synthetic goldens can be generated from a knowledge base, and conversations can be simulated across user personas. Agent steps can be traced and graded in the terminal and test runner, making failures easier to inspect as part of a development workflow. Integrations cover frameworks including LangChain, Pydantic AI, OpenAI Agents, LangGraph, LlamaIndex, and CrewAI, while model-provider connections include OpenAI, Anthropic, Gemini, Azure OpenAI, Ollama, Amazon Bedrock, and others.

DeepEval also supports tool-call checks, safety evaluations, regression runs, custom metrics, LLM-as-a-judge, human review workflows, prompt versioning, and CI/CD integration. Local use has a telemetry tradeoff: basic telemetry goes to PostHog by default, though it excludes personally identifiable information and stored results. Users can opt out with DEEPEVAL_TELEMETRY_OPT_OUT=1.

Pricing

DeepEval — 0.00 USD per free. The free plan is the open-source LLM evaluation framework, licensed under Apache 2.0, with a local and CI/CD test runner. It is a compelling starting point for engineers who can own their test runner and keep evaluation in pre-production. Its key limit is not a stated quota but the product boundary: DeepEval OS is described for pre-production testing, with results in local files.

For enterprise collaboration and deployment, Confident AI Evals is offered separately, with custom pricing. The enterprise offering includes shared workspaces, no-code evaluation workflows, annotation queues, and options to self-host on customer infrastructure or use the maker's cloud. Prospective customers can book a demo. Enterprise security features include SSO, role-based access control, granular permissions, audit logs, SOC 2 Type II, GDPR compliance, and custom data retention. Data sent to Confident AI is stored in databases in its private AWS cloud, except for organizations on the VIP plan.

Platforms

DeepEval supports Linux, macOS, and Windows, as well as self-hosted deployment. Its Python-script and Pytest-native workflow makes it most relevant to teams already comfortable building evaluation into code and CI/CD.

Who it's for

Choose DeepEval if you want a free, open-source way to build broad LLM and agent checks into development, especially when you need multimodal metrics, synthetic data, or trace inspection. It is less suitable as a standalone choice for teams seeking production monitoring, shared evaluation operations, or hosted collaboration in the open-source edition.

Pros and cons

• Pros: Broad coverage across 50+ metrics, three modalities, and multiple evaluation methods gives engineering teams room to test varied AI behaviors in one framework.
• Pros: Pytest-native checks, CI/CD support, and local trace inspection fit regression testing into established development workflows.
• Pros: Apache 2.0 licensing and a 0.00 USD per free plan make the framework accessible for local and self-hosted evaluation.
• Cons: The open-source edition is positioned for pre-production; local result files and an engineer-owned runner are limiting for teams that need centralized operations.
• Cons: Telemetry is on by default, so teams that require no telemetry must explicitly opt out.

Alternatives

For a broader comparison, see LLM Evaluation Tools, AI Agent Evaluation Tools, and AI LLM Evaluation Tools.

• Promptfoo is a free-plan alternative with 10k red-team probes per month, all LLM evaluation features, and local or self-hosted use; pick it if that published probe allowance and red-team focus suit your evaluation needs.
• Giskard offers a free open-source library with local deployment, a basic LLM vulnerability scan using adversarial techniques, and a basic RAG evaluation report; it fits readers prioritizing those scan and report workflows.
• Braintrust has a free Starter plan with 1 GB processed data, 10,000 scores, 14-day retention, unlimited users, projects, and datasets; choose it if those hosted usage limits and collaboration terms fit better than a local test runner.
• Confident AI has a free plan with 2 seats, 1 project, 5 test runs per week, and 1 GB-month of trace spans; it is a natural option for readers seeking a separate workspace-based offering from the same maker.
• Galileo offers a Pro plan at 100.00 USD per month, billed yearly, with 50,000 traces per month, standard RBAC, advanced analytics, and dedicated Slack support; consider it if those managed capabilities and annual billing suit your needs.
• Maxim AI has a free Developer plan with up to 3 seats, 1 workspace, 10k logs per month, 3-day retention, and email support; it suits readers whose needs fit those published caps.
• Parea AI offers a free plan for up to 2 team members, 3k logs per month with one-month retention, 10 deployed prompts, and Discord community support; choose it if that usage profile is a fit.
• Whisper is a free option for Windows, macOS, and Linux users.

Verdict

DeepEval is a strong choice for engineers who want an open-source, code-first evaluation framework with unusually broad metric, modality, and agent coverage. Its main reason to choose it is the ability to build repeatable checks into local and CI/CD workflows without a paid entry point; its main reason to look elsewhere is the pre-production boundary, which leaves teams needing centralized, collaborative or production-facing evaluation to another offering.