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DeepEval vs Dify

DeepEvalDify

Bottom line: DeepEval for engineering teams treating evals like tests; Dify for teams building LLM apps and agents quickly.

	DeepEval Open-source LLM evaluation framework with pytest-style testing. Visit	Dify Open-source platform for building production-ready LLM apps and agents. Visit
Votes	0	0
Pricing	Freemium	Freemium
Category	Coding	Coding
Tags	llm-evaluationtestingopen-sourceragci-cd	llmopsopen-sourceai-agentsragworkflow
Best for	Engineering teams treating evals like tests Teams gating deployments on LLM quality RAG and agent developers	Teams building LLM apps and agents quickly Organizations with data-residency needs Developers who want an open-source, self-hostable stack
Pros	pytest-style workflow fits developer habits 50+ research-backed metrics out of the box Apache-2.0 and free to use Covers RAG, agents, conversations, and safety Integrates into CI/CD for quality gates	Genuinely open-source and self-hostable for strong data control All-in-one: workflow, RAG, agents, and prompt IDE in one workspace Low-code visual canvas lowers the barrier to building Broad model and provider support, including self-hosted models Large, active community and a marketplace ecosystem
Cons	Eval reliability depends on judge model/config Competitive, crowded evaluation category Richer collaboration features require Confident AI cloud LLM-as-a-judge adds model API costs Requires writing and maintaining test suites	License is not fully permissive; multi-tenant resale and branding removal are prohibited Real cost is dominated by separate LLM token spend, not the platform fee Message-credit model on paid tiers can feel limiting at scale Self-hosting adds ops and maintenance burden Free tier's one-time credits are essentially a demo allowance

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