Feature lists overlap closely: tracing, experiments, online evals, human review queues and prompt versioning. Datadog suits teams whose services already report to Datadog and who want to buy self-serve; HoneyHive suits teams that want a dedicated evaluation platform, including a self-hosted one on Enterprise.[1][4]
Datadog Agent Observability, formerly LLM Observability, is a module of the Datadog platform that links agent traces to APM and infrastructure data, while HoneyHive is a standalone hosted agent observability and evaluation platform built on OpenTelemetry. Both cover tracing, offline experiments, online evals, annotation queues and prompt versioning. Datadog has a self-serve paid tier and no self-hosting; HoneyHive has a free tier, then sales-led Enterprise pricing that includes self-hosting.
| Decision factor | Datadog | HoneyHive |
|---|---|---|
| Ownership | Datadog, a public company (Nasdaq: DDOG); needs a Datadog account | Independent; $7.4M total funding last announced in April 2025 |
| Product shape | Module of the Datadog platform, linked to APM and infrastructure data | Standalone platform built on OpenTelemetry |
| Code evaluators in production | Run in your app and submitted; automatic only in Experiments | Server-side Python evaluators on sampled production traces |
| SDKs | Python and Node.js; Java and Go limited; HTTP API and OTLP | Full tracing SDK in Python; TypeScript REST client; OTLP |
| Self-hosting | None documented | Enterprise only, on AWS: fully self-hosted or hybrid |
| Run-time blocking | Separate product: AI Guard, Limited Availability | None documented |
| Free tier | Free: 40K LLM spans a month, 15-day retention | Developer: 10,000 events a month, 30-day retention, up to 5 users |
| First paid tier | Pro: $160 a month billed annually, 100K LLM spans | No self-serve paid plan; Enterprise is quote-based |
| Capability | Datadog | HoneyHive | 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, renamed from LLM Observability in June 2026, is part of the Datadog platform. Its traces sit beside APM, infrastructure and RUM data, so an agent failure can be followed into the backend services behind it, and billing joins an account you may already have. That is its main advantage, and it disappears if your services do not report to Datadog.
HoneyHive is a dedicated agent observability and evaluation platform. It is independent and small: its last announced funding was $7.4M in total, in April 2025. In June 2026 it was named the observability partner in Microsoft's ASSERT and Agent Control Specification announcement. It covers areas a general monitoring vendor does not focus on, such as traces from coding agents like Claude Code, Codex CLI and Devin, and integrations with Salesforce Agentforce, Databricks Agent Bricks and Microsoft Copilot Studio.
For multi-agent runs, HoneyHive offers agent graphs, trajectories, threads, timelines and, since September 2026, a conversation view. Datadog shows tools used and handoffs for OpenAI Agents SDK, LangGraph and CrewAI in Python, and auto-instruments Claude Agent SDK, Google ADK, Pydantic AI and Strands.
Both run experiments against datasets, score sampled production traffic with LLM judges, route traces to human reviewers through annotation queues and version prompts with a playground. The differences are in the details. HoneyHive runs server-side Python evaluators on production traces, so deterministic checks run inside the platform. Datadog runs code evaluators automatically only in Experiments; for live traffic you run them in your own application and submit the scores.
For release checks, HoneyHive documents CI regression gating through GitHub Actions, and Datadog documents CI/CD automation of Experiments. Datadog's LLM judges run on your own connected provider account; HoneyHive's evaluator calls can be routed through Portkey. HoneyHive lowered its default online sampling to 10% in May 2026, and Datadog lets you set sampling, for example 10%, to control judge cost. HoneyHive versions its LLM evaluators and supports Jinja2 templates in their prompts.
Datadog can also run DeepEval, Pydantic and NVIDIA NeMo evaluators. HoneyHive added coding-agent and multi-agent evaluator templates in April 2026 and, in August, evaluators that detect ground truth automatically.
Datadog documents no self-hosted deployment, and Agent Observability is not available on Datadog's US government sites. Lapdog, a local trace viewer, is for development only.
HoneyHive's free tier is multi-tenant SaaS in AWS us-west-2. On Enterprise it offers a dedicated single-tenant cloud, a hybrid model with a self-hosted data plane and HoneyHive's control plane, or a fully self-hosted deployment in your AWS account or VPC. Self-hosting is documented for AWS only, so teams on Azure or GCP should ask before planning around it. Alerts have been available on self-hosted HoneyHive since March 2026.
The buying models differ more than the prices. As of 20 October 2026, Datadog is self-serve: Free covers 40K LLM spans a month with 15-day retention, and Pro is $160 a month billed annually, or $200 month to month, for the first 100K LLM spans, then $3.50 per 10K. Only spans that call an LLM provider are billed.
Retention is priced differently too. Datadog sells retention beyond 15 days as an add-on, at $6.50, $8 or $9 per 10K spans for 30, 60 or 90 days. HoneyHive sets custom retention, limits and data residency in its Enterprise quote, along with unlimited users and workspaces and SSO.
HoneyHive's Developer tier is free with 10,000 events a month, 30-day retention and up to five users, with no card required. It publishes no self-serve paid plan, so anything beyond the free tier means an Enterprise quote. If you need to move from a trial to a paid plan without a sales cycle, that matters. Compare Datadog's pricing and HoneyHive'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 can act on what it finds through runtime policies, approval holds and a kill switch scoped to a run, an agent or a team. HoneyHive documents no run-time blocking; its Policies set evaluation standards, not enforcement. Datadog's module does not block either, and 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 or self-hosting is the requirement, Datadog or HoneyHive 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.