CompletionKit vs DeepEval
DeepEval is a powerful, open-source Python eval framework with deep metric coverage, from the Confident AI team. CompletionKit is the simpler bet: a plain-language rubric plus a few deterministic checks, a judge you can trust, and a flat price that lets the whole team take part.
Last updated June 21, 2026.
The short version
DeepEval is a mature, open-source (Apache 2.0) Python evaluation framework from Confident AI, with a hosted platform on top. It ships fifty-plus metrics with deep RAG coverage (plus agentic, multi-turn, safety, and multimodal), runs like pytest so evals can gate your CI, and is widely adopted (roughly 16k GitHub stars). It is genuinely powerful, and the right tool if you want metric breadth and you live in Python and pytest. Confident AI adds hosted dashboards, prompt management and serving, an MCP server, and a REST API, priced per user.
CompletionKit makes a different bet: keep it simple enough for the whole team. You define what good means as a plain-language rubric scored 1 to 5 and one judge applies it, with deterministic checks (valid JSON, a required token, no refusal) for the binary cases that need no model. We score how far to trust that judge, and members are included at a flat price rather than per seat. It drafts the next revision from the judge's feedback and serves the winning prompt to your app. It is the loop a PM and an engineer can run together, not a framework you assemble in code. It is source-available under BSL 1.1, so you can read the code and self-host it; DeepEval is fully open source under Apache 2.0.
Side by side
| DeepEval | CompletionKit | |
|---|---|---|
| Metric model | 50+ metrics | 1 rubric + checks |
| Automated judge-trust score | – | |
| No-code metric authoring | Paid | |
| Pricing | Per user | Flat |
| LLM-as-a-judge scoring | ||
| Suggests the next revision | ||
| Serves versioned prompts | ||
| RAG / agentic metrics | – | |
| pytest / CI-native | Via API | |
| MCP server | ||
| License | Apache 2.0 | BSL 1.1 |
Which one fits
Reach for DeepEval
You want metric breadth and depth, especially for RAG, agents, multi-turn, or safety; evals written as code that gate merges in pytest and CI; a team that lives in Python; or a free, Apache-2.0 framework you run locally. It is more mature and more widely adopted, and the stronger choice for code-first evaluation.
Reach for CompletionKit
You want a metric anyone can write (a plain-language rubric, plus deterministic checks for the pass/fail cases, rather than a fifty-metric catalog to configure), a judge whose scores you can trust because the agreement is measured, and a flat price that lets your whole team take part without a per-seat charge. The loop a PM and an engineer run together, source-available and self-hostable.
Not either-or. Some teams run DeepEval in CI for code-level and RAG metrics, and CompletionKit for the simple, calibrated, whole-team loop.
Questions
Is DeepEval better than CompletionKit?
For some teams, yes, and we will say so. DeepEval has far more metrics (deep RAG, agentic, multi-turn, safety, multimodal), runs like pytest so evals gate your CI, fits the Python ecosystem, is mature and widely adopted, and is genuinely open source under Apache 2.0. If those are your priorities, use DeepEval. CompletionKit makes a narrower bet: one simple judge anyone can configure, scored for how much you can trust it, that the whole team can use without paying per seat.
Isn't DeepEval cheaper, since it's open source?
The DeepEval framework is free and Apache 2.0, and you can run it locally. The hosted platform, Confident AI, is priced per user: Starter is $19.99 per user per month and Premium is $49.99 per user per month, with the no-code UI and full API access on the paid tiers and self-hosting reserved for Enterprise. Per-seat pricing adds up once you want non-coders in the loop. CompletionKit's Team plan is a flat $99 per month with unlimited members, so a team of six is about $300 a month on Confident AI Premium versus $99 flat with us.
Can a non-coder use DeepEval?
The framework itself is Python code. Confident AI's free tier gives a viewing dashboard (test reports, tracing, prompt versions), but building evals without code (no-code workflows, dataset annotation, human feedback) sits on the paid tiers. In CompletionKit, defining a metric is writing a plain-language rubric in the UI, and that is the core path, not a paid add-on, so anyone, a founder, a PM, a domain expert, can author and review evals directly.
What does CompletionKit actually do differently?
Three things, and they reinforce each other. One, the metric model stays small: you describe what good looks like as a 1-to-5 rubric and one judge applies it, plus a handful of deterministic checks (valid JSON, a required phrase, no refusal) for the pass/fail cases, instead of choosing and configuring from a catalog of fifty-plus metrics. Two, we score how far to trust that judge against your own labels (a Wilson interval and a quadratic-weighted kappa), where DeepEval leaves that cross-check manual. Three, members are included at a flat price, so the whole team can take part without a per-seat tax. We are not the more powerful framework; we are the one a whole team can use and believe.
Can I use both?
Yes. They are not mutually exclusive. Some teams run DeepEval in CI for code-level and RAG metrics and use CompletionKit for the simple, calibrated, whole-team improvement loop. Both ship an MCP server and a REST API, so wiring them alongside each other is straightforward.