Both pair a code-first eval library with a hosted platform for traces and results. Confident AI is the deeper evaluation tool, with 40+ DeepEval metrics, server-side online evals and red teaming; Logfire is the wider observability tool, tracing HTTP, database, logs and metrics around your agent and adding a gateway. Pick the one that closes the bigger gap in your current stack.[1][4]
Confident AI is an evaluation-first platform built around DeepEval, its open-source evaluation framework, while Logfire is Pydantic's OpenTelemetry observability platform with evals, prompt versioning and an AI gateway added. Both start from an open-source library, DeepEval under Apache-2.0 and pydantic-evals under MIT, and keep the hosted platform proprietary. Confident AI goes deeper on evaluation, red teaming and simulation; Logfire goes wider on tracing the whole application.
| Decision factor | Confident AI | Logfire |
|---|---|---|
| Ownership | Independent; Y Combinator Winter 2025 company | Pydantic, independent; Sequoia-backed |
| Open-source library | DeepEval and confident-trace, Apache-2.0 | pydantic-evals and SDKs, MIT |
| Production evals | Server-side on ingested traces; referenceless metrics only | Run in your application in the background |
| Human review | Annotation queues, multi-reviewer forms, feedback API | Run annotations in beta; queues in early access |
| OpenTelemetry | OTLP over HTTP; OTel metrics and logs are dropped | Built on it; traces, logs and metrics |
| Gateway | None; integrates with LiteLLM, Portkey and others | Yes, with guardrails and spend caps on model calls |
| Free tier | 2 seats, 1 GB-month of trace data, 5 test runs a week | Personal: 10M records a month, 1 seat |
| First paid tier | Starter: $200 a month per organisation, unlimited seats | Team: $49 a month, 5 seats |
| Capability | Confident AI | Logfire | 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 | ✓ | Partial | ✓ |
| Prompt versioning and playground | ✓ | ✓ | — |
| Act on findings | |||
| Runtime controls on live agent actions | — | Partial | ✓ |
| Gateway or proxy in the request path | — | ✓ | — |
| Licence and deployment | |||
| Open source | Partial | Partial | Partial |
| Self-hosting | Partial | Partial | Partial |
DeepEval, Confident AI's open-source framework, runs test cases locally and in CI with more than 40 metrics, including agent, multi-turn, RAG, MCP and multimodal metrics, and custom metrics through G-Eval and DAG. The hosted platform stores test runs, datasets and regression reports. In October 2026 Confident AI added Journeys in beta for simulating user paths, voice connections through Pipecat, LiveKit and SIP, and a view of red-teaming probes. Red teaming sits in its Enterprise modules.
Logfire's eval tooling is narrower: pydantic-evals provides LLM judges that record their reasoning, plain-code scorers, datasets in code or hosted in Logfire, and experiments compared against a baseline. Logfire does not list red teaming or user simulation. Its strength is elsewhere. Because its SDKs are OpenTelemetry wrappers, a single trace shows the HTTP request, database queries, logs and metrics around each model and tool call. If your agents fail mainly on reasoning and output quality, Confident AI covers more of that. If failures often start in the surrounding system, Logfire shows more of it.
Confident AI runs online evaluation tasks on its own servers as traces, spans and threads are ingested, with filters and sample rates. Only referenceless metrics, which need no expected answer, run during tracing. Code-Eval metrics, written as Python classes on DeepEval's base metric, run in a sandbox on the platform with no external network calls. Separately, threshold checks assess recorded production data over a trailing 24-hour window and report pass or fail.
Logfire's live evaluations run inside your application through pydantic-evals, in the background at a sample rate you choose, and send results as OpenTelemetry events that Logfire aggregates. They do not alter outputs. That keeps evaluators versioned with your code but puts their cost on your compute.
For human review, Confident AI has annotation queues for traces, spans, threads and test cases, multi-reviewer forms, an end-user feedback API and metric-to-human alignment. Logfire's run annotations are in beta and annotation queues are early access.
Confident AI released confident-trace, an OpenTelemetry-first SDK for Python and TypeScript, in September 2026. Its OTLP endpoint reads GenAI semantic conventions, OpenInference, OpenLLMetry and Vercel AI SDK attributes over HTTP only; gRPC is not supported, and OpenTelemetry metrics and logs are dropped. Logfire ingests traces, logs and metrics, and its SDKs cover Python, JavaScript and TypeScript, and Rust, with any other OpenTelemetry SDK able to send data. Both list Pydantic AI, LangGraph, CrewAI and Google ADK integrations.
Confident AI has no gateway of its own and traces through LiteLLM, Portkey, OpenRouter and others. Logfire's gateway can flag, redact or block model calls and cap spend, though only for traffic routed through it.
Cost tracking differs as well. Confident AI records token usage and cost on LLM spans and sums them per trace, but infers prices automatically only for OpenAI, Anthropic and Gemini; other providers need configured rates. Logfire shows cost as a queryable trace column wherever telemetry includes token data, and its gateway meters spend with per-project, per-user and per-key limits.
As of 20 October 2026, Confident AI prices per organisation, not per seat. Free covers 2 seats, 1 project and 1 GB-month of trace spans, with excess spans 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 with 75 GB-months and SSO; extra storage is $1 per GB-month. Logfire Personal is free for 10M records and one seat, Team is $49 a month for five seats, and Growth is $249 a month with unlimited seats; extra records are $2 per million.
Self-hosting the platform is Enterprise only for both. DeepEval itself runs locally at no cost, without a UI or stored history. Check Confident AI's pricing and Logfire's pricing against your data volume and team size.
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 a run and acts on what it finds through runtime policies on tool calls and data access, approval holds for risky actions and a kill switch for a run, an agent or a team. Confident AI documents no blocking of live agent actions, and Logfire's gateway covers model calls only. Prefactor has no prompt playground or prompt registry and its platform is hosted, so if a free, open-source eval framework is what you need, 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.