One works at the model layer, the other at the agent layer, so they complement rather than replace each other.[1][2]
Fiddler covers the model layer: output metrics, drift, and root-cause tracing for ML engineers. Prefactor evaluates the agent layer, judging each production run against its task with cost per agent and version. A model can pass every metric while the agent takes the wrong action, so the layers pair.
| Decision factor | Fiddler | Prefactor |
|---|---|---|
| Where it fits | Monitoring the model in production | Knowing the agent did its job |
| What it watches | Model and LLM signals: drift, output metrics | Agent outcomes: quality, drift, cost per run |
| Unit of analysis | The model | The agent and its run |
| Primary buyer | ML engineers and data scientists | Teams running agents in production |
| How it attaches | Instrument models and LLM calls | Native SDK, core SDK, or OpenTelemetry ingest, no rebuild |
| Use them together? | Monitor the model with Fiddler | Evaluate the agent with Prefactor |
Best for ML engineers and data scientists who need to monitor how their models and LLMs behave in production.
Best for teams running agents in production who need to know each one is doing its job, and prove it.
| Capability | Fiddler | Prefactor |
|---|---|---|
| Model observability | ||
| Model and LLM output metrics (hallucination, toxicity, PII) | ✓ | — |
| Model drift monitoring | ✓ | — |
| Span-level tracing and root-cause analysis | ✓ | Ingests the traces |
| Evaluating the agent in production | ||
| Quality score per run against the agent's objective | Model metrics, not task outcome | ✓ |
| Cost attributed per agent and version | — | ✓ |
| Drift detection in agent behaviour against a baseline | Model drift, not agent behaviour | ✓ |
| Hold or escalate a risky action | — | ✓ |
| Across your stack | ||
| Agent evaluation on any framework | — | ✓ |
| One queryable record per agent | — | ✓ |
| Audit trail for a decision | — | ✓ |
We sell the layer this section describes. Read it with that in mind.
Fiddler tells you whether the model is behaving: drift, hallucination and toxicity metrics, where a problem started. Whether the agent completed its task is a different question, and a separate layer.
An agent can call a model that passes every output metric and still take the wrong action. Prefactor evaluates each run against the agent's objective and tracks the result per agent, across versions.
The right action at ten times the cost is its own failure mode. Prefactor attributes cost per agent and per version, next to the quality score.
Agent behaviour can shift after a model update or a prompt edit while the model metrics hold. Prefactor catches the shift before a user hits it.
Prefactor ingests traces from Fiddler's instrumentation or any OpenTelemetry source, so model monitoring and agent evaluation sit next to each other rather than competing.
Reviewed against public product and documentation pages on March 19, 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.
Book a demo and we will evaluate a live agent on a fleet like yours: quality per run, drift after a change, and cost per agent.
Prefactor helps teams observe, evaluate, and improve their AI agents in production, across every framework and provider.