Credo AI documents your AI policy. Prefactor tells you the agents held to it.
One captures policy on a periodic cadence, the other evaluates every production run against it, so they are not alternatives.[1]
What is the difference between Credo AI and Prefactor?
Credo AI covers the documentation side of AI risk: model testing, policy mapping, and risk registers on a review cycle. Prefactor evaluates each agent run in production between those reviews: quality, drift, and cost per run. One documents the programme, the other measures the live agents.
Credo AI or Prefactor, in one table
| Decision factor | Credo AI | Prefactor |
|---|---|---|
| Where it fits | Documenting AI policy | Knowing each agent did its job in production |
| Primary buyer | Risk, legal, and responsible-AI teams | Teams running agents in production |
| Cadence | Periodic reviews and audit cycles | Every run, as agents execute |
| What it covers | Model portfolio: bias, fairness, documentation | Agent outcomes: quality, drift, cost per run |
| How it attaches | You assess and document your models | Native SDK, core SDK, or OpenTelemetry ingest, no rebuild |
| Use them together? | Document policy with Credo AI | Evaluate the agents with Prefactor |
What each one is for
- Model risk management: bias testing, fairness assessment, and performance validation across a model portfolio.
- Policy documentation: mapped to the EU AI Act, NIST AI RMF, and ISO 42001.
- Third-party AI assessment: evaluating external providers against your stated requirements.
- AI asset catalogue: a register across models, applications, and datasets.
- Recognition: named a Leader in the Forrester Wave for AI Governance Solutions, Q3 2025.
Best for risk, legal, and responsible-AI teams that need to document and demonstrate responsible AI across a model portfolio, ahead of a regulatory review.
- Judges each run on 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 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 tracked in the same place.
Best for teams running agents in production who need to know each one is doing its job, and prove it.
Side by side, by what each one answers
| Capability | Credo AI | Prefactor |
|---|---|---|
| Documenting AI policy | ||
| Model risk documentation (bias, fairness) | ✓ | — |
| Compliance mapping (EU AI Act, NIST, ISO 42001) | ✓ | — |
| AI asset catalogue (models, apps, datasets) | ✓ | — |
| Evaluating agents in production | ||
| Quality score per run | — | ✓ |
| Cost attributed per agent and version | — | ✓ |
| Drift detection against a baseline | — | ✓ |
| Hold or escalate a risky action | — | ✓ |
| Across your stack | ||
| Agent evaluation on any framework | — | ✓ |
| One queryable record per agent | Portfolio-level catalogue | ✓ |
| Evidence for a specific decision | Periodic compliance reports | Per-run audit trail |
Where Credo AI stops: between the review cycles
We sell the layer this section describes. Read it with that in mind.
Credo AI answers whether your AI programme is documented and defensible: which models exist, how they were tested, what policy they map to. It does not say whether an agent did its job between reviews.
A model can pass its bias tests and still ship an agent that drifts after a prompt change and starts returning wrong answers.
Prefactor keeps a quality score per run, tracks it per agent across versions, and flags when behaviour drifts.
Every run leaves a record you can hand to a customer or an auditor.
Prefactor reads them through a native SDK or any OpenTelemetry source, so the policy Credo AI captures and the runtime evidence Prefactor produces sit side by side.
Which one fits
Stay with Credo AI alone if
- Your priority is documenting and testing models ahead of a regulatory review.
- Your model reviews happen on a periodic cadence, not per run.
- You are not yet running agents in front of customers.
Add Prefactor when
- Agents are doing real work for real users between review cycles.
- You need a quality score per agent, not a periodic model report.
- A regression after a prompt or model change has to surface before a user hits it.
- Someone asks you to prove an agent behaved, run by run.
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 replace Credo AI?
Does Prefactor produce documentation for a review?
Can you use Prefactor and Credo AI together?
Does Credo AI evaluate agents in production?
Does Prefactor work with agents on any framework?
Related
Evaluate your production agents, run by run
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.
Unified performance platform for agents, authentication, and risk management