Both cover tracing, experiments, online scoring, human review and prompt versioning. Braintrust is the dedicated evaluation tool, organised around experiments and its Loop analysis agent; Datadog is the choice when your services already report to Datadog and you want agent traces beside them. Neither blocks a live agent action from its evaluation product.[1][4]
Braintrust is a standalone hosted evaluation platform built around experiments, while Datadog Agent Observability, formerly LLM Observability, is a module of the Datadog platform that ties agent traces to APM and infrastructure data. Both trace agents, score production traffic, queue traces for human review and version prompts. Braintrust offers a self-hosted data plane on Enterprise and a gateway in preview; Datadog offers neither.
| Decision factor | Braintrust | Datadog |
|---|---|---|
| Ownership | Independent; $80M Series B in February 2026 | Datadog, a public company (Nasdaq: DDOG); needs a Datadog account |
| Centre of gravity | Experiments: dataset, task, scorers, compare | Traces correlated with APM, infrastructure and RUM data |
| Code evaluators | TypeScript or Python, inline in SDK code, pushed by CLI or built in the UI | Python; automatic only in Experiments, submitted from your app in production |
| Self-hosting | Enterprise only, and only the data plane; Braintrust runs the control plane | None documented |
| Gateway | Hosted gateway in public preview, free, no SLA | None; documents tracing a gateway you run, and auto-instruments LiteLLM |
| Run-time blocking | None documented | Separate product: AI Guard, Limited Availability, billed per token scanned |
| Free tier | Starter: 1 GB processed data, 10K scores, 14-day retention | Free: 40K LLM spans a month, 15-day retention |
| First paid tier | Pro: $249 a month, unlimited users | Pro: $160 a month billed annually, 100K LLM spans |
| Capability | Braintrust | Datadog | 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 | ✓ | — | — |
| Licence and deployment | |||
| Open source | Partial | Partial | Partial |
| Self-hosting | Partial | — | Partial |
Braintrust is a product you adopt on its own. You instrument with its SDKs (TypeScript, Python, Go, Java, Ruby and C#) or send OpenTelemetry, and everything lives in Braintrust. It is an independent company that raised an $80M Series B in February 2026.
Datadog Agent Observability, renamed from LLM Observability in June 2026, is one module of the Datadog platform. Its traces sit next to APM, infrastructure and RUM data, so a slow or failing agent step can be followed into the backend services behind it. That is the main reason to choose it, and it only applies if your services already report to Datadog. Without a Datadog account the module is not available.
Braintrust starts from the experiment: a dataset, a task and scorers, run from the SDK, CLI or UI and compared side by side. Its Loop agent reads project data, writes scorers and runs evals, and since September 2026 Patterns group production behaviour for review, with several of these features in public preview. Online scoring rules run scorers on production logs with sampling and filters.
Datadog has the same building blocks inside its module: Experiments on versioned datasets that can be built from production traces, a browser Playground, LLM-as-a-judge evaluators on sampled production spans, and Annotation Queues for human review. Patterns and Bits Evals, its agentic help with the eval loop, were announced in Preview in June 2026.
One practical gap sits in code evaluators. Braintrust code scorers can be defined in SDK code, pushed by CLI or created in the UI. Datadog runs code evaluators automatically only in Experiments; for production traffic you run them in your own application and submit the scores. For human review, note that Braintrust's free Starter plan allows one human review score per project.
Braintrust runs as SaaS in US and EU regions. On Enterprise you can run the data plane in your own AWS, GCP or Azure account, or have Braintrust operate it there, but Braintrust always operates the control plane, including the UI and authentication. Datadog documents no self-hosted deployment at all, and Agent Observability is not available on Datadog's US government sites.
Braintrust also offers a gateway: one API to OpenAI, Anthropic, Google, AWS and others with caching and logging. The hosted version is free, in public preview, with no SLA. Datadog has no gateway of its own; it documents how to trace one you run and auto-instruments LiteLLM.
Both accept OpenTelemetry. Braintrust takes OTLP traces and logs at its /otel/v1/traces endpoint and converts LLM calls into its own span type. Datadog takes OTLP without its SDK, reading the GenAI conventions (v1.37 or later) or OpenInference, with a 3 to 5 minute ingestion delay. For cost, Datadog calculates spend from token counts for over 800 text-based models; Braintrust captures cost per traced LLM call and added a coding agent insights dashboard, in public preview, in October 2026.
The two meter different things. As of 20 October 2026, Braintrust charges a platform fee plus processed data and scores: Starter is free with 1 GB (then $4 per GB) and 10K scores (then $2.50 per 1,000), and Pro is $249 a month with 5 GB and 50K scores, then $3 per GB and $1.50 per 1,000 scores. Every plan has unlimited users. Datadog bills only spans that call an LLM provider: Free covers 40K a month, and Pro is $160 a month billed annually for the first 100K, then $3.50 per 10K.
Large agent payloads push up Braintrust's data meter; frequent model calls push up Datadog's. Tool and retrieval spans cost nothing on Datadog's meter. Model both against your own traffic using Braintrust's pricing and Datadog's pricing.
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 can act on what it finds through runtime policies, approval holds and a kill switch scoped to a run, an agent or a team. Braintrust documents no run-time blocking, and Datadog's evaluation module does not block either; Datadog sells that separately as AI Guard, in limited availability. Prefactor has no prompt playground or prompt registry and its platform is hosted, so if prompt iteration is the daily work, Braintrust or Datadog covers it better. 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.