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 a PM or 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 is simple: you describe what good looks like as a 1-to-5 rubric and one judge applies it, 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.

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.

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: one plain-language rubric, a judge you can trust, and a flat price that lets the whole team take part.

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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, one judge applies it, 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. CompletionKit is source-available under BSL 1.1, a more restrictive licence than DeepEval's Apache 2.0, and we do not call it open source.

Side by side

	DeepEval	CompletionKit
Shape	Python framework + hosted platform	Hosted improvement loop
Metric model	50+ metrics (G-Eval, DAG, RAG)	One judge, plain-language 1-5 rubric
Judge trust / calibration	Manual
Team pricing	Per user, $20-50/user/mo	Flat, members included
Build evals without code	Paid tier
LLM-as-a-judge scoring
Suggests the next revision
Versioned prompts served to your app
Deep RAG / agentic / safety metrics		–
pytest / CI-native		Via API
MCP server
License	Apache 2.0 (open source)	BSL 1.1 (source-available)

A few cells need context. "Manual": DeepEval lets you log human scores but leaves quantifying judge-vs-human agreement to you; CompletionKit computes it (Wilson interval, quadratic-weighted kappa). "Build evals without code": Confident AI's free tier is a viewing dashboard, while no-code authoring and dataset annotation sit on paid tiers; in CompletionKit it is the core path. Both tools suggest revisions, serve versioned prompts, and ship an MCP server and a REST API, so those are not differences. DeepEval is more open than us (Apache 2.0 versus our source-available BSL 1.1), and more mature.

Which one fits

Reach for DeepEval if you want metric breadth and depth, especially for RAG, agents, multi-turn, or safety; if you want evals written as code that gate merges in pytest and CI; if your team lives in Python; or if you want a free, Apache-2.0 framework you run locally. It is more mature and more widely adopted than CompletionKit, and it is the stronger choice for code-first evaluation.

Reach for CompletionKit if you want a simple metric anyone can write (a plain-language rubric, not a 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. It is the loop a PM and an engineer run together, source-available and self-hostable, focused on evaluate, improve, ship.

They are not either-or. Some teams run DeepEval in CI for code-level and RAG metrics, and use 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 a PM or 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 is simple: you describe what good looks like as a 1-to-5 rubric and one judge applies it, 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.

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