KLA vs Fiddler
Fiddler is strong for AI observability, monitoring, and guardrails programs. KLA focuses on workflow decision governance (checkpoints + queues) and verifiable evidence exports.
Tracing is necessary. Regulated audits usually ask for decision governance + proof: enforceable policy gates and approvals, packaged as a verifiable evidence bundle (not just raw logs).
For ML platform, compliance, risk, and product teams shipping agentic workflows into regulated environments.
Last updated: Dec 17, 2025 · Version v1.0 · Not legal advice.
Who this page is for
A buyer-side framing (not a dunk).
For ML platform, compliance, risk, and product teams shipping agentic workflows into regulated environments.
What Fiddler is actually for
Grounded in their primary job (and where it overlaps).
Fiddler is built for AI observability and monitoring: tracking performance, risk signals, and guardrail outcomes across AI systems. It’s a strong fit when your program starts with measurement and reporting.
Overlap
- Both can support risk/quality measurement programs and ongoing monitoring signals.
- Both can support “prove it” conversations — the difference is whether proof is packaged from workflow decisions or assembled from monitoring outputs.
- Both can be used together: monitoring for broad coverage, and a control plane for enforcing approval gates in specific workflows.
What Fiddler is excellent at
Recognize what the tool does well, then separate it from audit deliverables.
- Unified AI observability positioning (monitoring, evaluation, safety/guardrails framing).
- Strong fit when the program starts with model/agent monitoring, reporting, and guardrail signals.
Where regulated teams still need a separate layer
- Decision-time workflow governance: who can approve/override/stop an agent action, and how that gate is enforced.
- Policy checkpoints embedded in the workflow that can block/review/allow actions (with evidence of enforcement).
- Deliverable-shaped evidence exports (Annex IV mapping + oversight records + manifest + checksums), not only monitoring dashboards.
Out-of-the-box vs build-it-yourself
A fair split between what ships as the primary workflow and what you assemble across systems.
Out of the box
- Monitoring and reporting across AI systems (quality, safety, and risk signals).
- Guardrail and evaluation framing for responsible AI programs.
- Dashboards/alerts for continuous monitoring and incident response workflows.
Possible, but you build it
- A decision-time gate that blocks high-risk workflow actions until approved (with escalation and override rules).
- Workflow decision records (approvals/overrides) tied to business actions, not just model outputs.
- A packaged evidence bundle export mapped to Annex IV/oversight deliverables, with verification artifacts.
- Retention and integrity controls for long-lived audit records.
Concrete regulated workflow example
One scenario that shows where each layer fits.
Credit underwriting recommendation
An agent proposes approve/deny decisions with supporting rationale. Monitoring tells you how the system behaves over time; regulated workflows often also require a decision-time gate before the final decision is issued.
Where Fiddler helps
- Monitor drift, performance regressions, and guardrail outcomes across models and cohorts.
- Trigger investigations when risk signals breach thresholds.
Where KLA helps
- Enforce an approval checkpoint before a high-impact decision is issued or acted on.
- Capture who approved/overrode the recommendation (and what they saw) as an auditable decision record.
- Export a verifiable evidence pack for reviewers and auditors (manifest + checksums).
Quick decision
When to choose each (and when to buy both).
Choose Fiddler when
- Your primary requirement is broad AI monitoring and reporting across many models.
- You are building a measurement program first and governance controls later.
Choose KLA when
- You need to govern workflow actions (not only monitor models) with approvals and policy gates.
- You need evidence packs with integrity verification for audits.
When not to buy KLA
- You only need monitoring dashboards and alerts and don’t require approval queues or evidence exports.
If you buy both
- Use monitoring tools to understand performance and risk signals.
- Use KLA to enforce controls at decision time and export the evidence pack auditors ask for.
What KLA does not do
- KLA is not designed to replace broad AI monitoring platforms for organization-wide reporting.
- KLA is not a request gateway/proxy for model access.
- KLA is not a prompt experimentation suite.
KLA’s control loop (Govern / Measure / Prove)
What “audit-grade evidence” means in product primitives.
Govern
- Policy-as-code checkpoints that block or require review for high-risk actions.
- Role-aware approval queues, escalation, and overrides captured as decision records.
Measure
- Risk-tiered sampling reviews (baseline + burst during incidents or after changes).
- Near-miss tracking (blocked / nearly blocked steps) as a measurable control signal.
Prove
- Tamper-proof, append-only audit trail with external timestamping and integrity verification.
- Evidence Room export bundles (manifest + checksums) so auditors can verify independently.
Note: some controls (SSO, review workflows, retention windows) are plan-dependent — see /pricing.
RFP checklist (downloadable)
A shareable procurement artifact (backlink magnet).
# RFP checklist: KLA vs Fiddler Use this to evaluate whether “observability / gateway / governance” tooling actually covers audit deliverables for regulated agent workflows. ## Must-have (audit deliverables) - Annex IV-style export mapping (technical documentation fields → evidence) - Human oversight records (approval queues, escalation, overrides) - Post-market monitoring plan + risk-tiered sampling policy - Tamper-evident audit story (integrity checks + long retention) ## Ask Fiddler (and your team) - Can you enforce decision-time controls (block/review/allow) for high-risk actions in production? - How do you distinguish “human annotation” from “human approval” for business actions? - Can you export a self-contained evidence bundle (manifest + checksums), not just raw logs/traces? - What is the retention posture (e.g., 7+ years) and how can an auditor verify integrity independently? - How do you connect monitoring signals to enforceable workflow gates and a packaged evidence export for audits?
Sources
Public references used to keep this page accurate and fair.
Note: product capabilities change. If you spot something outdated, please report it via /contact.