Both trace agents over OpenTelemetry, run experiments and score production traffic with LLM, code and human evaluators. Fiddler adds real-time guardrails and monitors classic ML models alongside agents; HoneyHive puts more into the agent developer's workflow: prompt versioning, annotation queues and graph, trajectory and conversation views. How you buy above the free tier also differs.[1][4]
Both are hosted, closed-source platforms with Enterprise self-hosting; Fiddler adds real-time guardrails and classic ML monitoring, while HoneyHive adds prompt versioning, annotation queues and agent views built for multi-step runs. HoneyHive has a self-serve free tier but no published paid plan, so anything above 10,000 events a month goes through sales. Fiddler has a self-serve paid plan at $0.002 per trace.
| Decision factor | Fiddler | HoneyHive |
|---|---|---|
| Company | Independent; $30M Series C in January 2026, $100M raised in total | Independent; $7.4M raised in total, last announced April 2025 |
| Prompt tooling | None | Playground plus prompt versioning and deployment |
| Human review | Span annotations, in public preview | Annotation queues with automations |
| Run-time checks | Partial: real-time content scores; inline blocking through a third-party AI gateway | None; it observes and scores |
| Classic ML monitoring | Yes: drift, performance, explainability, fairness | Not documented |
| Self-hosting | Enterprise: VPC, on-premises or SageMaker | Enterprise: fully self-hosted or hybrid, documented on AWS |
| Free tier | Guardrails-only free plan, by demo request | Developer: 10,000 events a month, 30-day retention, up to 5 users |
| First paid tier | Developer: $0.002 per trace, hosted only | No published paid plan; Enterprise is quote-based |
| Capability | Fiddler | HoneyHive | Prefactor |
|---|---|---|---|
| Observe | |||
| LLM call tracing | ✓ | ✓ | ✓ |
| Multi-step agent tracing (tool calls, nested spans) | ✓ | ✓ | ✓ |
| Cost tracking | Partial | ✓ | ✓ |
| OpenTelemetry support | ✓ | ✓ | ✓ |
| Evaluate | |||
| Offline evals (datasets, experiments) | ✓ | ✓ | ✓ |
| Online evals on production traffic | ✓ | ✓ | ✓ |
| LLM-as-a-judge scoring | ✓ | ✓ | ✓ |
| Code-based evaluators | ✓ | ✓ | ✓ |
| Human review and annotation | Partial | ✓ | ✓ |
| 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 |
On the core evaluation loop the two are close. Both trace multi-step agent runs, accept OpenTelemetry, run offline experiments on datasets and score sampled production traffic with LLM judges and code. The difference is what each has added around that loop.
Fiddler has added run-time protection and breadth across model types. Fiddler Guardrails score requests and responses in real time for harmful content, faithfulness, personal data and secrets, and can block inline when Fiddler sits behind Kong AI Gateway, AgentGateway, LiteLLM or Portkey. The same platform monitors classic ML models for drift, explainability and fairness.
HoneyHive has added tools for the people building agents. It has a playground and prompt versioning with deployment by name or version, annotation queues that route flagged traces to domain experts, and agent graph, trajectory, thread and conversation views. It also traces coding agents such as Claude Code, Codex CLI and Devin, and integrates with Salesforce Agentforce, Databricks Agent Bricks and Microsoft Copilot Studio. It does not block or hold anything at run time; its Policies feature sets evaluation and monitoring standards, not enforcement.
Both feed into the rest of an engineering stack, in different directions. HoneyHive runs experiments in CI through GitHub Actions to catch regressions, reports cost, token and latency by workspace and project, and exports to a data warehouse on Enterprise. Fiddler connects to data and operations tools such as Amazon SageMaker, Databricks, Snowflake, BigQuery, Datadog and PagerDuty, which reflects its roots in classic ML monitoring. If your evaluation results need to land in an existing ML platform, Fiddler's integration list is longer; if they need to gate pull requests, HoneyHive documents that path.
The buying paths are close to opposite. HoneyHive's Developer tier is free with no card for 10,000 events a month, 30-day retention and up to five users, which makes it easy to try. Above that there is no published paid plan: Enterprise is quote-based, so a growing team moves straight into a sales conversation.
Fiddler works the other way round. Its free plan covers guardrails only and is reached through a demo request, but its Developer plan is self-serve at $0.002 per trace and adds observability, experiments, custom evaluators and SSO. A team that wants to pay by usage without negotiating a contract can do that with Fiddler and not with HoneyHive.
Company size is a fair question for a long-term tool. Fiddler has raised $100M in total. HoneyHive's last announced funding was $7.4M, in April 2025, and third-party directories put its staff at around 18 to 19 people; it was named as the observability partner in Microsoft's ASSERT announcement in June 2026.
Both self-host only on Enterprise. HoneyHive offers fully self-hosted deployment, or hybrid with the data plane in your environment and the control plane run by HoneyHive, documented on AWS EKS. Fiddler lists VPC and on-premises deployment, or running inside your AWS account as a SageMaker Partner AI App.
Both are Python-first. Fiddler's native agent SDKs are Python only, and HoneyHive's TypeScript package is a REST API client rather than an auto-instrumenting tracer. Teams on other languages send OTLP to either.
As of 20 October 2026, HoneyHive's Developer tier is free for 10,000 events a month and Enterprise is custom, with no self-serve paid tier between them. Fiddler's Developer plan charges $0.002 per trace, so 100,000 traces a month would be $200, with Enterprise custom. HoneyHive counts events and Fiddler counts traces, so compare on your own volumes using Fiddler's pricing and HoneyHive's pricing.
Fiddler's Enterprise plan adds VPC and on-premises deployment, enterprise guardrails and a named customer success contact. HoneyHive's Enterprise plan adds custom limits and retention, unlimited users and workspaces, SSO, and single-tenant, hybrid or self-hosted deployment.
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. HoneyHive documents no run-time controls, and Fiddler's guardrails check content rather than tool actions. Prefactor has no prompt playground or prompt registry and its platform is hosted, so for prompt work HoneyHive is the better fit, and for classic ML monitoring Fiddler is. 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.