Both run evals in code, score production traces and version prompts. Opik is a complete Apache-2.0 platform you can self-host for free; Confident AI is the code-first choice for teams already writing DeepEval tests, with red teaming and simulation on its hosted platform. How much of the stack must be open source decides most cases.[1][4]
The difference between Confident AI and Opik is how much is open source: Confident AI open-sources DeepEval, its evaluation framework, while the platform that holds traces and runs online evals is proprietary and self-hosted only on Enterprise. Opik, from Comet, publishes the whole platform under Apache-2.0. Confident AI adds red teaming and conversation simulation; Opik adds guardrails your code can call.
| Decision factor | Confident AI | Opik |
|---|---|---|
| Ownership | Independent; Y Combinator Winter 2025 company with a small team | Comet ML, independent; reports $70M in total funding |
| Open source | DeepEval and confident-trace (Apache-2.0); platform is proprietary | Core platform, UI, backend and SDKs (Apache-2.0) |
| Self-hosting the platform | Enterprise only, with a licence key | Free; all features except user administration |
| Online evals | Yes, referenceless metrics only during tracing | Yes, judge and custom Python metrics on sampled traffic |
| Run-time checks | None; threshold checks run on recorded data over 24 hours | Partial: guardrails your code calls; they fail closed |
| Red teaming and simulation | Yes: red teaming (Enterprise) and Journeys in beta | Not documented |
| Free tier | 2 seats, 1 GB-month of trace data, 5 test runs a week | 25k spans a month, up to 10 members, 60-day retention |
| First paid tier | Starter: $200 a month per organisation, unlimited seats | Pro Cloud: $19 a month, 100k spans |
| Capability | Confident AI | Opik | Prefactor |
|---|---|---|---|
| Observe | |||
| LLM call tracing | ✓ | ✓ | ✓ |
| Multi-step agent tracing (tool calls, nested spans) | ✓ | ✓ | ✓ |
| Cost tracking | ✓ | ✓ | ✓ |
| OpenTelemetry support | ✓ | ✓ | ✓ |
| Evaluate | |||
| Offline evals (datasets, experiments) | ✓ | ✓ | ✓ |
| Online evals on production traffic | ✓ | ✓ | ✓ |
| LLM-as-a-judge scoring | ✓ | ✓ | ✓ |
| Code-based evaluators | ✓ | ✓ | ✓ |
| Human review and annotation | ✓ | ✓ | ✓ |
| Prompt versioning and playground | ✓ | ✓ | — |
| Act on findings | |||
| Runtime controls on live agent actions | — | Partial | ✓ |
| Gateway or proxy in the request path | — | Partial | — |
| Licence and deployment | |||
| Open source | Partial | ✓ | Partial |
| Self-hosting | Partial | ✓ | Partial |
DeepEval is Apache-2.0, runs in Python and TypeScript, and ships more than 40 metrics, including agent, multi-turn, RAG, MCP and multimodal ones. Run on its own it has no UI and no persistence. Confident AI, the platform that stores test runs, traces, datasets and regression reports, is proprietary, and its self-hosted version needs an Enterprise licence key and private container images. confident-trace, an OpenTelemetry-first tracing SDK released in September 2026, is also Apache-2.0.
Opik draws the line elsewhere. Tracing, evaluation, the UI, backend and SDKs all sit in the public Apache-2.0 repository, and the self-hosted version includes every feature except user administration. A team that wants online evals, annotation queues and prompt versioning on its own servers without a licence key can do that with Opik and cannot with Confident AI. Production self-hosting of Opik runs on Kubernetes; the local Docker install is described as not production-ready.
Confident AI is built around writing tests. DeepEval runs test cases locally and in CI/CD, and the platform keeps the history and the regression reports. Its online evals run server-side on traces, spans and threads as they arrive, but only referenceless metrics run during tracing. Code-Eval runs custom Python metrics in a sandbox with no external network calls. Recent releases add trajectory custom metrics, Journeys (beta) for simulating user paths, voice connections through Pipecat, LiveKit and SIP, and a red-teaming probes view; red teaming itself sits in the Enterprise modules.
Opik offers more than 30 built-in metrics, custom judge prompts and an Agent as a Judge that reads traces with tools. Custom Python metrics run in experiments and also as online rules on production threads. It adds Test Suites written as plain-text assertions, resumable evaluations, annotation queues that can fill automatically, and a Prompt Optimizer with six algorithms.
Cost data differs in a small but practical way. Confident AI infers cost automatically only for OpenAI, Anthropic and Gemini; other providers need rates configured. Opik tracks token usage and model cost on traces, and its Cost Intelligence feature extends that to coding agents such as Claude Code and Codex.
Confident AI does not document blocking or approval of live agent actions. The feature it calls runtime controls checks recorded production data against thresholds over a trailing 24-hour window and reports pass or fail, and red-teaming detections flag traces after ingestion. Older documentation described input and output guardrails, but that page now redirects to the docs home, and DeepEval lists guardrails on its roadmap.
Opik's guardrails do block. PII, topic, prompt injection, judge and custom classifier checks run as a validate() call that fails closed and raises an exception. Your application must call them and handle the result, PII and topic checks are English only, self-hosted guards need a separate server with a GPU recommended, and there is no approval workflow for agent actions. For an agent whose prompts or outputs need checking before they reach a user, that is the more direct of the two options.
The plans meter different units. As of 20 October 2026, Confident AI prices per organisation rather than per seat: Free covers 2 seats, 1 project, 1 GB-month of trace spans (excess is dropped) and 5 test runs a week; Starter is $200 a month with unlimited seats, 5 projects and 5 GB-months; Team is $2,000 a month. Beyond the allowance it charges $1 per GB-month ingested or retained.
Opik's Free Cloud covers 25k spans a month for up to 10 team members with 60-day retention, and Pro Cloud is $19 a month for 100k spans and up to 50 members, then $5 per 100k. Self-hosted Opik is free with unlimited spans; self-hosted Confident AI is Enterprise only. Check Confident AI's pricing and Opik's pricing against your own volumes.
We sell the layer this section describes. Read it with that in mind.
Stated plainly, since Prefactor publishes this page: Prefactor is an agent evaluation platform that scores each step of every run and can act on a failing score through runtime policies, approval holds and a kill switch for a run, an agent or a team. Confident AI checks recorded data after the fact; Opik's guardrails block a call your code routes through them, without approval holds or tool-call policies. Prefactor has no prompt playground or prompt registry and its platform is hosted, so for prompt iteration or free self-hosting, Opik or DeepEval is the better fit. See the full comparison of 15 platforms.
Reviewed against public product and documentation pages on October 20, 2026. If a vendor has changed a feature, product name, or positioning since then, send a correction and we will update it. Numbered source links in the page body point to the ordered sources below.
Prefactor scores each step of every agent run and can hold or stop an action when a score says it should not happen. Start on the free tier: 5,000 spans a month, no credit card required.
Prefactor helps teams observe, evaluate, and improve their AI agents in production, across every framework and provider.