Feature lists overlap closely, and both are small independent vendors. Confident AI is the pick when evaluation depth, red teaming and a published paid tier matter; HoneyHive is the pick when tracing everything through OpenTelemetry, including coding agents, comes first. How you expect to pay after the free tier is a practical tie-breaker.[1][4]
Confident AI and HoneyHive cover similar ground: agent tracing, offline and online evals, annotation queues and prompt versioning, and neither blocks a live action. Confident AI leads with DeepEval, an open-source metric library, plus red teaming and simulation, and publishes self-serve paid plans. HoneyHive leads with OpenTelemetry-based tracing, including coding agents, and has a free tier but no published paid plan below Enterprise.
| Decision factor | Confident AI | HoneyHive |
|---|---|---|
| Ownership | Independent; Y Combinator Winter 2025 company | Independent; $7.4M total funding, last announced April 2025 |
| Open-source pieces | DeepEval and confident-trace (Apache-2.0) | SDKs, CLI and Agent Skills (MIT) |
| Centre of gravity | Metrics in code, plus red teaming and simulation | OpenTelemetry tracing, evals and monitoring |
| Coding-agent tracing | Not listed | Yes: Claude Code, Codex CLI and Devin |
| TypeScript | DeepEval and confident-trace for TypeScript | REST API client; auto-instrumenting tracer is Python, or use OTLP |
| Self-hosting | Enterprise; AWS, GCP or Azure Kubernetes | Enterprise; full or hybrid, documented on AWS |
| Free tier | 2 seats, 1 GB-month of trace data, 5 test runs a week | 10,000 events a month, 30-day retention, up to 5 users |
| Paid plans | Starter $200 a month; Team $2,000 a month | No published self-serve plan; Enterprise is quote-based |
| Capability | Confident AI | 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 | — | — | ✓ |
| Gateway or proxy in the request path | — | — | — |
| Licence and deployment | |||
| Open source | Partial | Partial | Partial |
| Self-hosting | Partial | ✓ | Partial |
On a feature checklist these two are close. Both trace multi-step and multi-agent runs, run experiments against datasets, score sampled production traffic with LLM and code evaluators, route traces to reviewers through annotation queues, and version prompts with a playground. Both are small independent vendors: the Y Combinator directory lists Confident AI's team as 7, and third-party directories list HoneyHive at around 18 to 19 staff.
The difference is where each one starts. Confident AI grew from DeepEval, an Apache-2.0 framework with more than 40 metrics for agents, multi-turn conversations, RAG and MCP, and adds red teaming, multi-turn and voice simulation and, since October 2026, Journeys (beta) for simulating user paths. HoneyHive grew from tracing: it is built on OpenTelemetry, offers agent graph, trajectory, timeline and conversation views, and has monitoring with alerts.
HoneyHive accepts OTLP and reads GenAI, OpenLLMetry and OpenInference conventions, with an OpenTelemetry Collector integration. Its full tracing SDK is Python; the TypeScript package is a REST client, so TypeScript services usually send OTLP. It traces coding agents such as Claude Code, Codex CLI and Devin, and added Salesforce Agentforce and Databricks Agent Bricks in August 2026.
Confident AI has Python and TypeScript SDKs for both DeepEval and confident-trace, an OTLP endpoint over HTTP only, and integrations for frameworks including OpenAI Agents, Claude Agent SDK, LangGraph, CrewAI, Pydantic AI, Mastra and voice frameworks such as LiveKit and Pipecat. If your team writes TypeScript agents, Confident AI's SDK covers more natively; if you are tracing coding agents or Salesforce and Databricks agents, HoneyHive lists those and Confident AI does not.
HoneyHive's free tier runs on multi-tenant SaaS in AWS us-west-2. On Enterprise it offers a dedicated single-tenant cloud, a hybrid model where you run the data plane and HoneyHive runs the control plane, or a full deployment in your own AWS account or VPC, documented on AWS EKS. Confident AI hosts in the US or EU on every plan, offers custom data residency on request, and self-hosts on AWS, GCP or Azure Kubernetes on Enterprise.
On compliance, Confident AI shows a SOC 2 Type II badge, with the report and a HIPAA BAA on the Team plan and above, plus SSO, RBAC and audit logs. HoneyHive lists SAML SSO, custom roles, a custom DPA and a BAA on Enterprise. For monitoring, HoneyHive has cost, token and latency dashboards with alerts and usage reports by workspace; Confident AI added findings alerts and scheduled workflows in October 2026.
Both use words that suggest enforcement. HoneyHive's Policies set organisation-wide evaluation and monitoring standards; they do not block anything, and in Microsoft's ASSERT announcement HoneyHive is the observability layer while a separate specification enforces. Confident AI's runtime controls assess recorded production data against thresholds over a trailing 24-hour window and report pass or fail. Neither documents blocking or approval of live agent actions.
In practice, both are tools for finding failures and measuring fixes. If your agents take actions such as payments or record changes, plan a separate control for those actions and use either platform to judge how well it works.
As of 20 October 2026, HoneyHive's Developer tier is free with 10,000 events a month, 30-day retention, up to 5 users and one workspace. Above that there is no published self-serve plan; Enterprise, including self-hosting, is quote-based. Confident AI's Free plan covers 2 seats and 1 GB-month of trace data, Starter is $200 a month per organisation with unlimited seats and 5 GB-months, and Team is $2,000 a month with SSO and a SOC 2 report.
So HoneyHive's free tier fits more users, but growing past it means a sales conversation, while Confident AI has a published step up. See Confident AI'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 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. Neither Confident AI nor HoneyHive documents run-time enforcement, so that is the gap both leave. Prefactor has no prompt playground or prompt registry and its platform is hosted, so if prompt iteration or a large open-source metric library is what you need, one of these two is the better pick. 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.