Both trace agents, run experiments and online evals, queue traces for human review and version prompts. Datadog fits teams that already send APM and infrastructure data to Datadog and want agent data beside it; Maxim AI fits teams that want pre-release simulation, including voice, and an option to deploy in their own VPC. Whether you already pay for Datadog usually decides it.[1][4]
Datadog Agent Observability, formerly LLM Observability, is one module of the Datadog platform and puts agent traces and evals next to your APM and infrastructure data; Maxim AI is a standalone platform for simulating, evaluating and tracing agents. Datadog is SaaS only, while Maxim AI offers an Enterprise in-VPC deployment. Both can block content only through a separate product: AI Guard for Datadog, Bifrost Enterprise for Maxim AI.
| Decision factor | Datadog | Maxim AI |
|---|---|---|
| Ownership | Datadog, a public company (Nasdaq: DDOG) | Independent; $3M seed led by Elevation Capital in 2024 |
| Product | Agent Observability, renamed from LLM Observability in June 2026 | Evaluation platform at getmaxim.ai/evals, plus the Bifrost gateway |
| Hosting | Datadog SaaS only; not on US government sites | SaaS, or Enterprise in-VPC on AWS, GCP or Azure |
| Pre-release simulation | Experiments and a Playground on versioned datasets | Multi-turn text and voice simulation, plus test runs |
| Blocking | Via AI Guard, a separate product in limited availability, billed per token scanned | Via Bifrost Enterprise guardrails on inputs, outputs and MCP tool calls |
| Free tier | 40K LLM spans a month, 15-day retention | Up to 3 seats, 10k logs a month, 3-day retention |
| First paid tier | Pro: $160 a month billed annually for 100K LLM spans | Professional: $29 per seat a month, 100k logs |
| Capability | Datadog | Maxim AI | 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 |
Datadog Agent Observability is one product inside the Datadog platform and needs a Datadog account. Its main advantage is correlation: LLM and agent traces sit next to the APM traces, logs and infrastructure metrics your backend already sends, so a slow agent step can be tied to the service that caused it. Datadog renamed the product from LLM Observability in June 2026, and its documentation still lives under the old path.
Maxim AI is a standalone evaluation and tracing platform from an independent company. It does not replace your infrastructure monitoring, but it can forward traces through data connectors to New Relic, Snowflake or any OpenTelemetry collector. Both accept OpenTelemetry: Datadog ingests OTLP directly using GenAI conventions or OpenInference, and Maxim AI ingests OTLP over HTTP with GenAI, OpenInference and AI SDK conventions.
If Datadog is already your system of record for production, adding agent data there is the shorter path. If it is not, buying into Datadog for agent evaluation alone is a larger commitment than adopting Maxim AI.
Datadog documents no self-hosted deployment, and the product is not available on Datadog's US government sites. Lapdog, a local trace viewer, runs without an account for development but is not a production option. Maxim AI offers an Enterprise deployment in your VPC on AWS, GCP or Azure, either fully isolated or as a data plane peered to Maxim's control plane, and its Bifrost gateway self-hosts for free.
Evaluation workflows differ in one practical detail. Datadog's code evaluators run automatically in Experiments, but in production you run them in your own application and submit the results; its LLM judges do run automatically on sampled production spans. Maxim AI runs pre-built, LLM, code and API-based evaluators on production logs at session, trace and node level, from its Professional plan upwards, and adds multi-turn simulation of text and voice agents before release.
Prompt work and SDK coverage are closer than they look. Datadog's prompt versioning is a registry retrieved at runtime in Python, Go and JavaScript, with per-environment deployment and A/B tests of prompt versions, though some sub-features were in Preview. Maxim AI has a playground, versions, partials, deployment rules and prompt optimisation. Both offer annotation queues for human review. Outside Python, Datadog's Java SDK auto-instruments only OpenAI and its Go SDK is experimental; Maxim AI lists SDKs for Python, JavaScript and TypeScript, Go and Java.
Neither evaluation product blocks live actions by itself. Datadog's blocking of prompts, responses and tool calls comes from AI Guard, a separate product in limited availability and billed per token scanned. Maxim AI's comes from Bifrost, whose guardrails check inputs, outputs and MCP tool-call arguments and results and can block, redact or transform content; guardrails are listed as a Bifrost Enterprise feature. In both cases, price the second product before assuming you have run-time blocking.
As of 20 October 2026, Datadog's Free plan covers 40K LLM spans a month with 15-day retention. Pro is $160 a month billed annually ($200 month to month) for the first 100K LLM spans, then $3.50 per 10K annually. Only spans that call an LLM provider are billed; tool, agent, workflow and retrieval spans are not. Longer retention costs extra.
Maxim AI's Developer tier is free for up to 3 seats and 10k logs a month with 3-day retention. Professional is $29 per seat a month with 100k logs and 7-day retention, Business is $49 per seat a month with 500k logs and 30-day retention, and overage is $1 per 10k logs. Datadog's price follows model-call volume regardless of team size; Maxim AI's follows team size first. See Datadog's pricing and Maxim AI'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 and acts on what it finds through runtime policies, approval holds and a kill switch scoped to a run, an agent or a team, in the same product as the evaluation. Datadog and Maxim AI both put blocking in a separate product, AI Guard or Bifrost Enterprise, and neither documents approval holds for agent actions. Prefactor has no prompt playground or prompt registry and its platform is hosted, and it does not replace infrastructure monitoring the way Datadog can. 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.