Both trace agents, run experiments on datasets and score production traffic. Braintrust is built for the development loop: datasets, scorers, prompt versions and an analysis agent; Fiddler is built for production oversight: monitoring across ML and LLM systems, real-time guardrails and deployment inside your own network. Where your team spends most of its time usually decides it.[1][4]
Braintrust is a hosted platform built around experiments and prompt iteration; Fiddler is a monitoring and guardrails platform that covers classic ML models, LLM applications and agents. Braintrust has prompt versioning and no run-time checks; Fiddler has real-time guardrail scoring and no prompt tooling. Fiddler can run fully on-premises on Enterprise, while Braintrust self-hosts only its data plane.
| Decision factor | Braintrust | Fiddler |
|---|---|---|
| Ownership | Independent; $80M Series B in February 2026 | Independent; $30M Series C in January 2026 |
| Centre of gravity | Experiments: dataset, task, scorers, compare | Production monitoring and guardrails, including classic ML models |
| Prompt tooling | Versioned prompts, playgrounds and environments | None |
| Run-time checks | None; scoring and alerts run after logging | Partial: real-time guardrail scores; inline blocking through a third-party AI gateway |
| Self-hosting | Enterprise only, and only the data plane | Enterprise: VPC, on-premises or in your AWS account via SageMaker |
| SDKs | TypeScript, Python, Go, Java, Ruby, C# | Python SDKs; other languages via OpenTelemetry or REST |
| Free tier | Starter: 1 GB processed data, 10K scores, 14-day retention | Guardrails-only free plan, by demo request |
| First paid tier | Pro: $249 a month | Developer: $0.002 per trace, hosted only |
| Capability | Braintrust | Fiddler | Prefactor |
|---|---|---|---|
| Observe | |||
| LLM call tracing | ✓ | ✓ | ✓ |
| Multi-step agent tracing (tool calls, nested spans) | ✓ | ✓ | ✓ |
| Cost tracking | ✓ | Partial | ✓ |
| 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 |
Braintrust starts from the experiment. You bring a dataset, define a task and scorers in TypeScript or Python, run variants and compare. Prompts are versioned objects you can call from code without redeploying, and human reviewers can be assigned logs or experiment rows, including blind reviews. Loop, its in-product agent, reads results, writes scorers and runs evals, and since September 2026 Patterns group production behaviour for review (in public preview).
Fiddler starts from production. Evaluator Rules score new production spans that match a filter, with backfill and downsampling to limit judge cost, and its Evals SDK runs experiments against golden datasets with 13 pre-built evaluators. Human review is lighter: span annotations entered public preview in August 2026, with no review queue documented. Fiddler has no prompt versioning or playground at all.
A team that rewrites prompts and agent logic every week will find more of its daily work in Braintrust. A team whose main question is whether a deployed system is still behaving, across both ML models and agents, will find more in Fiddler.
Judge cost is handled differently too. Fiddler offers its own hosted judge model, Ministral 3 8B, generally available since September 2026, and lets evaluator rules downsample traffic; an API for tracking judge spend is in private preview. Braintrust publishes its pre-built judges in the MIT-licensed Autoevals library and added workflow evals through provider batch APIs, which it promotes as roughly 50% cheaper eval runs. Several newer features on both sides, including Braintrust's Patterns and thread view and Fiddler's annotations, are still in preview, so check status before you depend on them.
Braintrust does not act during a run. Online scoring runs asynchronously after logging, and alerts to Slack, PagerDuty or webhooks fire after the fact. Its gateway, in public preview with no SLA, routes model calls and caches them but does not document blocking.
Fiddler Guardrails score requests and responses in real time for harmful content, faithfulness, personal data and secrets, and return the score for your application to block on. Inline blocking or redaction without code changes needs a third-party gateway: Kong AI Gateway, AgentGateway, LiteLLM or Portkey. Neither vendor documents approval steps or policy checks on an agent's tool calls.
Deployment differs as well. Braintrust's Enterprise plan lets you host the data plane in AWS, GCP or Azure, but Braintrust always runs the control plane: the UI, logins and metadata. Fiddler's Enterprise plan lists VPC and on-premises deployment, or running inside your AWS account as a SageMaker Partner AI App. If a review requires everything inside your own network, Fiddler documents that path and Braintrust does not.
Braintrust ships SDKs in six languages and integrates with a long list of agent frameworks, including the OpenAI Agents SDK, Claude Agent SDK, LangGraph, CrewAI, Mastra, Pydantic AI and Vercel AI SDK. Fiddler's native agent SDKs are Python only, covering LangGraph, LangChain, Strands Agents and Google ADK, with a generic OpenTelemetry SDK for custom Python agents. Teams on TypeScript or Go can still send OTLP traces to Fiddler, but without the native helpers.
The two charge on different units. As of 20 October 2026, Braintrust Starter is free with 1 GB of processed data, 10K scores and 14-day retention, and Pro is $249 a month with 5 GB, 50K scores and 30-day retention; every plan includes unlimited users. Fiddler's Developer plan charges $0.002 per trace with no published platform fee, so 100,000 traces a month would be $200. Fiddler's free plan covers guardrails only and is reached through a demo request. Self-hosting is Enterprise and quote-based at both.
Check Braintrust's pricing and Fiddler's pricing against your own trace counts, data volumes and score counts before you decide.
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. Braintrust documents no run-time controls, and Fiddler's guardrails check content rather than tool actions. Prefactor has no prompt playground or prompt registry and its platform is hosted, so for prompt iteration Braintrust is the better pick, and for a full on-premises deployment Fiddler is. 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.