Both trace agents, run offline and online evaluations, route traces to human reviewers and version prompts. Confident AI starts from DeepEval, an open-source metrics library you can run in CI without any platform; Weave sits inside CoreWeave's model platform and suits teams already working in Weights & Biases.[1][6]
Confident AI is a hosted evaluation platform built around DeepEval, an open-source evaluation framework you can run on its own, while W&B Weave is the tracing and evaluation toolkit inside Weights & Biases. CoreWeave acquired W&B in May 2025 and has sold Weave inside CoreWeave Forge since September 2026. Both score production traces and version prompts; only Weave documents guardrails that can block content during a run, and those work from Python code only.
| Decision factor | Confident AI | W&B Weave |
|---|---|---|
| Ownership | Independent; Y Combinator Winter 2025 company with a small team | CoreWeave since May 2025; sold inside CoreWeave Forge since 30 September 2026 |
| Open code | DeepEval and confident-trace, Apache-2.0; platform proprietary | Weave repository, Apache-2.0; self-hosting needs a W&B licence |
| Built-in metrics | 40+ DeepEval metrics, including agent, multi-turn, RAG and MCP | Predefined scorers such as hallucination and summarisation, plus local small-model scorers |
| Online evals | Yes, on the platform; only referenceless metrics during tracing | Monitors on production traces; docs now recommend Signals instead |
| Runtime guardrails | None documented; threshold checks on recorded data over 24 hours | Partial: scorers called from Python code can block or modify inputs and outputs |
| Self-hosting | Platform on Enterprise only; DeepEval runs locally with no UI | Kubernetes with a Weave licence and a W&B Platform install |
| Free tier | 2 seats, 1 project, 1 GB-month of trace data | Forge Free: 1 GB of Weave ingestion a month, up to 5 model seats |
| First paid tier | Starter: $200 a month per organisation, unlimited seats | Forge Pro: from $60 a month, up to 10 model seats |
| Capability | Confident AI | W&B Weave | 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 | — | Not stated | — |
| Licence and deployment | |||
| Open source | Partial | Partial | Partial |
| Self-hosting | Partial | ✓ | Partial |
Confident AI is built on DeepEval, an Apache-2.0 evaluation framework for Python and TypeScript with more than 40 metrics, including agent, multi-turn, RAG, MCP and multimodal ones. DeepEval runs test cases locally and in CI with no account. The hosted Confident AI platform adds what DeepEval lacks: storage for test runs and datasets, regression reports, tracing with online evals, annotation queues, prompt versioning, red teaming and, since October 2026, a beta for simulating user journeys. The company is a Y Combinator Winter 2025 business with a small team, per the YC directory.
Weave comes from the other direction. It is the LLM and agent part of Weights & Biases, which CoreWeave acquired in May 2025. Since 30 September 2026 it has been sold inside CoreWeave Forge, alongside W&B Models, Agent Lens and CoreWeave inference, and W&B hosted accounts sign in through Forge. CoreWeave now recommends Agent Lens, a separate product in public preview, for agent tracing, so Weave's packaging is still settling.
In Confident AI, evaluation code is DeepEval code. You can write custom metrics locally, use G-Eval or DAG metrics with any LLM as judge, or upload Python code evaluators that run in a sandbox on the platform without network access. Online evaluation runs server-side on traces, spans and threads as they arrive, with filters and sampling, but only metrics that need no reference answer run during tracing.
In Weave, you write scorers as Python or TypeScript functions, or as classes in Python, and run them through the Evaluation API over a dataset. Results can be compared across runs and shared as leaderboards. Production traces are scored by monitors using LLM judges, with alerts through Slack or webhooks, although Weave's documentation now labels built-in monitors a previous approach and points to Signals. Both offer annotation queues and a prompt playground.
If your team wants evaluation to live in the test suite and be portable between vendors, DeepEval's open licence is the stronger argument. If it wants evaluations recorded next to model training runs, Weave is.
Neither platform has a free self-hosted version. Confident AI's platform self-hosts on AWS, GCP or Azure Kubernetes with an Enterprise licence key and private container images. Weave self-hosts on Kubernetes with ClickHouse and S3-compatible storage, but needs a Weave-enabled licence and an existing W&B Platform install. Both ingest OTLP over HTTP and read OTel GenAI, OpenInference and OpenLLMetry attributes.
On acting during a run they differ. Weave's guardrails are scorers you call from Python application code that can block or modify inputs and outputs before they reach users; the TypeScript SDK does not support them. Confident AI documents no inline blocking: its threshold checks assess recorded production data over a trailing 24-hour window and report pass or fail. Neither documents approvals on an agent's tool calls.
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 data, with five test runs a week; Starter is $200 a month with unlimited seats, 5 projects and 5 GB-months included, then $1 per GB-month; Team is $2,000 a month. CoreWeave prices Weave through Forge: Forge Free includes up to 5 model seats and 1 GB of Weave ingestion a month, and Forge Pro starts at $60 a month with up to 10 model seats and 1.5 GB, for teams under 50 employees, with extra ingestion listed at $0.10 per MB.
Confident AI's unit is stored trace data over time; Weave's is data ingested each month. A large team with modest traffic may find Confident AI's unlimited seats cheaper; a small team may find Forge's entry price easier. DeepEval itself costs nothing. Compare Confident AI's pricing and CoreWeave Forge pricing with 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 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 reports on recorded data, and Weave's Python guardrails check content without approvals on agent actions, so holding or stopping an action is the part neither covers. Prefactor has no prompt playground or prompt registry and its platform is hosted, and it has no metrics library on the scale of DeepEval's, so for prompt iteration or open-source test suites one of these two 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.