Fiddler watches the model. Prefactor knows if the agent is doing its job.
One works at the model layer, the other at the agent layer, so they complement rather than replace each other.[1][2]
What is the difference between Fiddler and Prefactor?
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.
Fiddler or Prefactor, in one table
| 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 |
What each one is for
- Model and LLM observability: prompt and response monitoring, hallucination detection, toxicity scoring, and PII leakage detection.
- Drift monitoring: model performance and drift tracked across both traditional ML and LLM deployments.
- Root-cause tracing: span-level tracing and hierarchical analysis for agentic systems.
- Metric library: a large set of out-of-the-box and custom metrics for model evaluation.
- Built for practitioners: tooling for the engineers and data scientists who build and operate models.
- Recognition: named in the Gartner Market Guide for AI Evaluation and Observability Platforms.
Best for ML engineers and data scientists who need to monitor how their models and LLMs behave in production.
- A verdict per run: outcome quality, cost, and whether the agent stayed in its approved scope.
- A quality score per agent tracked across versions, so a regression shows up as a trend.
- Drift detection when agent behaviour shifts after a model update or a prompt edit, before a user hits it.
- Holds or escalates a risky action for review before it reaches a user, not after.
- One record across frameworks: agents on any framework, side by side in one place.
Best for teams running agents in production who need to know each one is doing its job, and prove it.
Side by side, model layer and agent layer
| 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 | — | ✓ |
Where Fiddler stops: the model can pass while the agent fails
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.
Which one fits
Stay with Fiddler alone if
- Your question is how the model and its outputs behave in production.
- ML engineers and data scientists are the ones acting on the signals.
- Model-level metrics and traces cover what you need today.
Add Prefactor when
- You need to know whether the agent, not just the model, did its job.
- A quality score per agent matters more than a per-output metric.
- Cost per agent and per version has to sit alongside the quality score.
- A regression in agent behaviour after a change has to surface before a user hits it.
How we reviewed this comparison
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.
Sources reviewed
Prefactor contextMethodology
- Reviewed public product, documentation, and launch material visible at the time of writing.
- Mapped each page to the primary buyer, control layer, and runtime capabilities each vendor describes publicly.
- Prefer direct product and documentation pages over analyst summaries or reseller material.
Does Prefactor do model monitoring like Fiddler?
What is the difference between model evaluation and agent evaluation?
Can you use Prefactor and Fiddler together?
Does Fiddler check whether an agent did its job?
Does Prefactor work with agents on any framework?
Related
Prove what the agent did, not just how the model behaved
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