Complete pricing guide for DeepEval. Compare all plans, analyze costs, and find the perfect tier for your needs.
Not sure if free is enough? See our Free vs Paid comparison →
Still deciding? Read our full verdict on whether DeepEval is worth it →
mo
mo
mo
mo
mo
Pricing sourced from DeepEval · Last verified March 2026
DeepEval is broader — it covers RAG metrics (contextual precision, recall, faithfulness) plus agent tool use evaluation, conversational quality metrics, bias/toxicity detection, and red-teaming. RAGAS focuses specifically on RAG pipeline evaluation with deeper RAG-specific metrics. With 50+ metrics versus RAGAS's narrower set, DeepEval is the better choice for teams building agents or multi-turn chatbots. If you only need RAG evaluation, RAGAS may be sufficient; for comprehensive agent and LLM testing across 150,000+ developer workflows, DeepEval covers more ground.
Yes. DeepEval includes conversational metrics for coherence, topic adherence, and knowledge retention across multiple conversation turns. The chat simulation feature in Confident AI Premium ($49.99/user/month) can generate multi-turn test conversations automatically, removing the need to manually script dialogue scenarios. Conversational relevancy and knowledge retention metrics specifically score whether agents maintain context across turns. This is particularly useful for customer support bots, tutoring agents, and any long-running conversational system where single-turn metrics miss the bigger picture.
Yes. DeepEval evaluates inputs and outputs regardless of framework — it operates on the text the agent produces rather than hooking into framework internals. It works with LangChain, CrewAI, LlamaIndex, OpenAI Agents SDK, custom agents, and any LLM application that produces text outputs. This framework-agnostic design means you can switch agent frameworks without rewriting your evaluation suite. The tool correctness metric also accepts arbitrary tool call schemas, so agents using custom function-calling formats are supported.
DeepEval metrics are validated against human judgment benchmarks, with each of the 50+ metrics backed by academic research. Accuracy varies by metric and evaluator model — using stronger models (GPT-4, Claude Opus) as evaluators produces more accurate scores than GPT-3.5 or smaller models. The framework regularly updates metrics based on new academic findings, and most metrics include confidence scores or reasoning explanations. For mission-critical applications, teams typically run a calibration round comparing DeepEval scores against human-labeled samples to set appropriate thresholds.
DeepEval is the free, open-source evaluation framework (MIT license) for running LLM tests locally or in CI. Confident AI is the commercial cloud platform built by the same team — it adds collaboration, dataset management, LLM tracing, real-time monitoring, alerting, and dashboards. Pricing for Confident AI starts at $19.99/user/month for Starter and $49.99/user/month for Premium, with Team and Enterprise tiers offering self-hosted deployment and SOC 2 compliance. DeepEval works standalone; Confident AI layers on top for team and production use.
AI builders and operators use DeepEval to streamline their workflow.
Try DeepEval Now →Open-source framework for evaluating RAG pipelines and AI agents with automated metrics for faithfulness, relevancy, and context quality.
Compare Pricing →Open-source LLM testing and evaluation framework for systematically testing prompts, models, and AI agent behaviors with automated red-teaming.
Compare Pricing →AI observability platform with Loop agent that automatically generates better prompts, scorers, and datasets from production data. Free tier available, Pro at $25/seat/month.
Compare Pricing →LangSmith lets you trace, analyze, and evaluate LLM applications and agents with deep observability into every model call, chain step, and tool invocation.
Compare Pricing →Open-source LLM observability and evaluation platform built on OpenTelemetry. Self-host for free with comprehensive tracing, experimentation, and quality assessment for AI applications.
Compare Pricing →