Both are hosted platforms that trace agents and judge production traffic. Braintrust is the broader eval workbench: experiments, scorers you write, human review and prompt versioning; Raindrop is narrower and production-first, classifying every event and grouping failures into issues. Whether you want to author the scoring yourself usually decides it.[1][4]
Braintrust is a hosted evaluation platform built around experiments, custom scorers and prompt versioning, while Raindrop is a hosted production monitor that classifies live agent traffic with plain-language signals and groups recurring failures into issues. Both are independent, proprietary platforms with open-source pieces, and both now look for patterns in production traces. Braintrust lets you write your own scorers in code or as LLM judges; Raindrop builds its classifiers with its own models.
| Decision factor | Braintrust | Raindrop |
|---|---|---|
| Ownership | Independent; $80M Series B in February 2026 | Independent; Series A led by CRV in September 2026, $50M raised in total |
| Centre of gravity | Experiments: dataset, task, scorers, compare | Production traffic: signals, issues, live-traffic experiments |
| Scorers | LLM judges and code scorers you write, plus the Autoevals library | Signals drafted from a plain-language description by Raindrop's models; no custom judge prompt or model |
| Production pattern-finding | Patterns and Topics, in public preview | Issue Detection, whose second version shipped with Raindrop 2.0 in June 2026 |
| Prompt tooling | Versioned prompts, playgrounds and environments | None documented |
| Self-hosting | Enterprise only, data plane only; Braintrust runs the control plane | Beta: Enterprise VPC deployment for select partners |
| Free tier | Starter: 1 GB processed data, 10K scores, 14-day retention | Hobby: 1,000 events a month, 1 custom signal, 14-day retention |
| First paid tier | Pro: $249 a month | Pro: $299 a month plus $0.003 per event |
| Capability | Braintrust | 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 |
Braintrust expects you to define quality yourself. Scorers are LLM judges or TypeScript and Python functions, written in the UI or in code, and the open-source Autoevals library supplies ready-made ones. The same scorers run in offline experiments and, through online scoring rules with sampling and filters, on production logs.
Raindrop takes the opposite approach. You describe a behaviour in plain language and Raindrop drafts a classifier called a Signal. Since August 2026 these run on rd-signal-2, which combines generated filter code, a task-specific classifier and an in-house reasoning model for ambiguous cases. Signals run on every new event and backfill a sampled 90 days.
The trade-off is control against effort. Braintrust gives you full say over the judge prompt, the model and the code, at the cost of writing and maintaining them. Raindrop gets you classifiers quickly, but you cannot bring your own judge prompt or model, and you label sample events as Match or Not Match to tune them.
Braintrust's workflow starts before launch: a dataset, a task, scorers, then variants compared side by side. Its Loop agent reads results, writes scorers and runs evals, and since September 2026 Patterns and Topics group production behaviour for review. Several of these newer features are still in public preview.
Raindrop starts after launch. Issue Detection groups recurring failures, Experiments compare signal rates between cohorts of live traffic, and a Triage Agent investigates issues in the web app, Slack or over MCP. For pre-release testing it offers Workshop, an MIT-licensed local tool where a coding agent turns failures into evals and reruns the agent until they pass, and Simulations, which tests each pull request against synthetic copies of your services and is in early access.
Human review differs too. Braintrust assigns review scores to reviewers, supports blind reviews and required scores, though Starter allows only one human review score per project. Raindrop lets you annotate dataset members and label sample events, but documents no reviewer queue.
Braintrust versions prompts with templating and environments and lets code call them without a redeploy. It also runs a hosted gateway to the major model providers, in public preview with no SLA. Raindrop offers neither prompt versioning nor a gateway; it can read total cost reported by a third-party gateway such as OpenRouter.
Neither is self-hosted by default. Braintrust's Enterprise plan lets you run the data plane in AWS, GCP or Azure while Braintrust operates the UI, logins and metadata. Raindrop's self-hosting is a beta Enterprise VPC deployment rolling out to select partners. Both accept OpenTelemetry traces; Raindrop takes OTLP over HTTP only, without gRPC, metrics or logs.
Both track model spend. Braintrust records token usage and cost on each traced call and supports cost alerts. Raindrop's Costs page breaks spend down by model, provider and project from a versioned catalogue of public model rates, and its Experiments report median cost per event. Framework coverage is wide on both sides, but most of Raindrop's integrations, including LangChain, CrewAI and the OpenAI Agents SDK, are labelled beta, so check the one you depend on.
As of 20 October 2026, Braintrust charges a platform fee plus processed data and scores. Starter is free with 1 GB and 10K scores, Pro is $249 a month with 5 GB and 50K scores, and every plan has unlimited users. Raindrop charges a platform fee plus a fee per event. Hobby is free for 1,000 events a month, and ingestion stops for the rest of the month once you pass it. Pro is $299 a month plus $0.003 per event up to 1M events, then $0.002, with unlimited retention against Braintrust Pro's 30-day default.
Because one meters gigabytes and scores and the other meters events, run your own volumes through Braintrust's pricing and Raindrop's plans. Raindrop's PII Guard redaction add-on is billed separately, from $80 a month.
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, not only the final answer, and acts on a failing score through runtime policies, approval holds and a kill switch scoped to a run, an agent or a team. Neither Braintrust nor Raindrop documents run-time controls: both judge traces after they are logged and alert. Prefactor has no prompt playground or prompt registry and its platform is hosted, so if prompt iteration is the core need, Braintrust is the better pick. 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.