Both evaluate agents in production, but from opposite ends. Confident AI starts from tests: DeepEval metrics you run in CI, with a hosted platform for regression reports, online evals and review; Raindrop starts from live traffic, turning plain-language descriptions into classifiers and grouping failures into issues.[1][4]
Confident AI is a code-first evaluation platform built on DeepEval, an Apache-2.0 metrics framework for testing agents locally and in CI, while Raindrop is a hosted production monitor that classifies live agent traffic and groups recurring failures into issues. Both are independent and both score production traces. Confident AI gives you 40+ open-source metrics, annotation queues and prompt versioning; Raindrop gives you plain-language classifiers and experiments on live traffic, with no prompt versioning.
| Decision factor | Confident AI | Raindrop |
|---|---|---|
| Ownership | Independent; Y Combinator W25 company with a small team | Independent; Series A led by CRV in September 2026, $50M raised in total |
| Open-source part | DeepEval and confident-trace, Apache-2.0 | Workshop local debugger and several SDKs, MIT |
| Offline evals | Yes: DeepEval runs 40+ metrics locally and in CI | Partial: Workshop runs evals locally; Simulations in early access |
| Production scoring | Online evals on traces, spans and threads; referenceless metrics only | Signals on every new event; Issue Detection groups recurring failures |
| Human review | Annotation queues with multi-reviewer forms | Labelling samples and dataset members; no reviewer queue |
| Prompt tooling | Prompt Studio with commits, versions and labels | None documented |
| Self-hosting | Enterprise licence, on AWS, GCP or Azure Kubernetes | Beta: Enterprise VPC deployment for select partners |
| First paid tier | Starter: $200 a month per organisation, unlimited seats | Pro: $299 a month plus $0.003 per event |
| Capability | Confident AI | Raindrop | Prefactor |
|---|---|---|---|
| Observe | |||
| LLM call tracing | ✓ | ✓ | ✓ |
| Multi-step agent tracing (tool calls, nested spans) | ✓ | ✓ | ✓ |
| Cost tracking | ✓ | ✓ | ✓ |
| OpenTelemetry support | ✓ | ✓ | ✓ |
| Evaluate | |||
| Offline evals (datasets, experiments) | ✓ | Partial | ✓ |
| Online evals on production traffic | ✓ | ✓ | ✓ |
| LLM-as-a-judge scoring | ✓ | Partial | ✓ |
| Code-based evaluators | ✓ | Partial | ✓ |
| Human review and annotation | ✓ | Partial | ✓ |
| Prompt versioning and playground | ✓ | — | — |
| Act on findings | |||
| Runtime controls on live agent actions | — | — | ✓ |
| Gateway or proxy in the request path | — | — | — |
| Licence and deployment | |||
| Open source | Partial | Partial | Partial |
| Self-hosting | Partial | Partial | Partial |
Confident AI is built around DeepEval, an open-source framework in Python and TypeScript with more than 40 metrics, including agent, multi-turn, RAG and MCP metrics. Any LLM can act as the judge, and custom metrics such as G-Eval and DAG let you define your own. The hosted platform adds a prompt-based evaluator and sandboxed code evaluators written as Python classes.
Raindrop does not ask you to pick or write metrics. You describe a behaviour in plain language, Raindrop drafts a classifier called a Signal, and you refine it by labelling sample events as Match or Not Match. Signals run on every new event and backfill a sampled 90 days. The classifiers come from Raindrop's own models, so you cannot bring your own judge prompt or model.
If your team wants to see and version the exact metric that decides pass or fail, Confident AI fits that habit. If you would rather name the behaviour and let the vendor build the detector, Raindrop does.
DeepEval runs in a test suite, locally or in CI/CD, and Confident AI stores test runs, datasets and regression reports. Since October 2026 it also simulates user paths with Journeys (beta) and voice connections. Its online evals score traces, spans and threads as they arrive, with filters and sample rates, but only referenceless metrics run during tracing.
Raindrop has no hosted offline eval runner. Its pre-release options are Workshop, an MIT-licensed local tool where a coding agent turns failures into evals and reruns the agent until assertions pass, and Simulations, which replays production traffic and test cases against each pull request and is in early access. Its strength is after launch: Issue Detection, Slack alerts, a Triage Agent and Experiments that compare signal rates between cohorts of live traffic.
Confident AI also documents red teaming, human annotation queues with end-user feedback, and compliance reporting. Its 'runtime controls' module checks recorded production data against thresholds over a trailing 24-hour window; it reports pass or fail and does not act on live runs.
Both platforms are proprietary and hosted by default. Self-hosting Confident AI needs an Enterprise licence key and private images, deployed to AWS, GCP or Azure Kubernetes. Raindrop's self-hosting is a beta Enterprise VPC deployment for select partners. DeepEval and Workshop both run on your own machine, but neither is a copy of the hosted platform.
Both take OpenTelemetry over HTTP, not gRPC. Confident AI reads GenAI conventions, OpenInference and OpenLLMetry attributes and drops metrics and logs; its confident-trace SDK can also export to your own collector. Raindrop takes OTLP traces with gen_ai attributes and works with the OpenTelemetry Collector. Neither runs a gateway in the request path.
Cost tracking differs in coverage. Confident AI records token usage and cost on LLM spans, but infers prices automatically only for OpenAI, Anthropic and Gemini; other providers need rates you set. Raindrop calculates spend by model, provider and project from a versioned catalogue of public model rates, including cache and reasoning tokens. Company size is worth a look too: the Y Combinator directory lists a small team at Confident AI, while Raindrop has raised $50M in total.
As of 20 October 2026, Confident AI charges per organisation, not per seat. Free covers 2 seats, 1 project, 1 GB-month of trace spans and 5 test runs a week. Starter is $200 a month with unlimited seats and 5 GB-months, Team is $2,000 a month with 75 GB-months, and extra storage is $1 per GB-month. Raindrop's Hobby plan is free for 1,000 events a month, then ingestion stops until the next month. Pro is $299 a month plus $0.003 per event up to 1M events, then $0.002, with unlimited retention.
One prices stored data and the other prices events, so estimate your own traffic against Confident AI's pricing and Raindrop's plans.
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 a failing score through runtime policies, approval holds and a kill switch for a run, an agent or a team. Neither Confident AI nor Raindrop documents blocking or approval of live agent actions: Confident AI's threshold checks run over recorded data and Raindrop alerts after ingestion. Prefactor has no prompt playground or prompt registry and its platform is hosted, so if an open-source metrics library in CI is the main need, DeepEval is the better starting point. 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.