Both trace agents, run experiments on datasets, score production traffic and queue traces for human review. Confident AI is an evaluation-first product you can adopt on its own; Datadog Agent Observability is the choice when your telemetry already lives in Datadog. Your existing monitoring stack usually settles this.[1][4]
Confident AI is a standalone evaluation platform built around DeepEval, its open-source metrics framework; Datadog Agent Observability, formerly LLM Observability, is a module of the Datadog platform and needs a Datadog account. Datadog fits teams that want agent traces beside their APM and infrastructure data. Confident AI fits teams that want deeper evaluation, red teaming and a self-hosted option on Enterprise.
| Decision factor | Confident AI | Datadog |
|---|---|---|
| What it is | Standalone evaluation and tracing platform built on DeepEval | Module of the Datadog platform; needs a Datadog account |
| Ownership | Independent; Y Combinator Winter 2025 company | Datadog, a public company (Nasdaq: DDOG) |
| Self-hosting | Enterprise only, on your AWS, GCP or Azure Kubernetes | None; Lapdog is a local development viewer only |
| Code evals in production | Code-Eval metrics run in a sandbox on the platform | Run in your application and submitted; automatic only in Experiments |
| Runtime blocking | None; threshold checks run on recorded data | Through AI Guard, a separate product in limited availability |
| Billing unit | Per organisation, with trace storage in GB-months | LLM spans only; tool and agent spans are not billed |
| Free tier | 2 seats, 1 GB-month of trace data, 5 test runs a week | 40K LLM spans a month, 15-day retention |
| First paid tier | Starter: $200 a month, unlimited seats | Pro: $160 a month billed annually for 100K LLM spans |
| Capability | Confident AI | 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 |
Datadog renamed LLM Observability to Agent Observability in 2026. It is one product inside the Datadog platform, so agent traces, evaluations and costs sit next to your APM, infrastructure and real-user data, correlated with the backend services your agents call. That correlation is the main reason to choose it, and it only applies if you use Datadog for the rest of your stack.
Confident AI does not depend on any other monitoring product. It is a standalone platform, from an independent Y Combinator company, built around DeepEval. If your infrastructure monitoring lives elsewhere, or you want evaluation kept separate from operations tooling, it is the simpler starting point.
One detail links the two: Datadog lists DeepEval among the evaluation libraries it supports, alongside Pydantic and NVIDIA NeMo evaluators. A team that writes DeepEval metrics is not locked out of Datadog.
Both run experiments against versioned datasets, score production traffic with LLM judges on a sample, and offer annotation queues and prompt versioning. The difference is in the detail. Datadog runs code evaluators automatically only inside Experiments; in production you run them in your own application and submit the results through its SDK or HTTP API. Some newer features, such as Patterns and Bits Evals, were in Preview as of mid-2026.
Confident AI runs custom code metrics in a sandbox on its platform, and DeepEval brings more than 40 metrics for agents, multi-turn conversations, RAG and MCP. It also offers red teaming and multi-turn and voice simulation, which Datadog does not list. One limit: Confident AI's online evals run only referenceless metrics during tracing.
Datadog has no self-hosted option for the platform and is not available on its US government sites. Confident AI offers US or EU hosting on all plans and self-hosting on Enterprise. Both accept OTLP traces: Datadog without its own SDK, using GenAI semantic conventions or OpenInference, with a delay of three to five minutes; Confident AI over HTTP only, reading GenAI, OpenInference, OpenLLMetry and Vercel AI SDK attributes.
Neither blocks agent actions from the observability product itself. Datadog sells AI Guard separately for blocking prompts, responses and tool calls; it was in limited availability as of June 2026 and is billed per token scanned. Confident AI documents no inline blocking: its threshold checks assess recorded data over a trailing 24-hour window.
Language coverage differs. Datadog's Python SDK covers the most frameworks; its Java SDK has a single auto-instrumented integration, for OpenAI, and its Go SDK is experimental. Confident AI has Python and TypeScript SDKs and points other languages, including Go, Java and Rust, to the raw OpenTelemetry SDK.
Datadog calculates cost automatically from public rates for more than 800 text models, with custom values for self-hosted models. Confident AI infers cost automatically only for OpenAI, Anthropic and Gemini, so other providers need project-level cost rules. Datadog keeps traces for 15 days by default and sells longer retention as an add-on; Confident AI lists configurable retention.
As of 20 October 2026, Datadog bills LLM spans, meaning calls to a model provider; tool, agent and retrieval spans are free. Free covers 40K LLM spans a month with 15-day retention, and Pro is $160 a month billed annually for 100K LLM spans, then $3.50 per 10K. Longer retention costs extra. Confident AI bills per organisation: Free covers 2 seats and 1 GB-month of trace data, Starter is $200 a month with unlimited seats and 5 GB-months, Team is $2,000 a month, and extra storage is $1 per GB-month.
An agent that makes many tool calls per model call is cheap to trace on Datadog; one with large payloads uses Confident AI's storage faster. Compare Confident AI's pricing and Datadog's 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. Datadog offers blocking through AI Guard, a separate product, and Confident AI checks recorded data after the fact; neither documents approval holds. Prefactor has no prompt playground or prompt registry, its platform is hosted, and it does not correlate with APM data the way Datadog does. 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.