Promethean vs Holistic AI
Holistic AI sells AI governance assessment and audit-readiness tooling, sales-led at six-figure annual contracts. Promethean ships per-decision cryptographic evidence, self-service from €0–€2,499/mo. Different shapes for the same compliance problem — they layer rather than replace.
Comparison at a glance
| Dimension | Holistic AI | Promethean |
|---|---|---|
| Pricing model | Sales-led enterprise · ~€100k–200k+/year per common public-reference reports | Self-service · €0 Dev · €79–€2,499/mo paid · Stripe checkout |
| Primary value | Bias auditing · risk assessment · model governance · explainability reporting | Per-decision cryptographic evidence · audit-trail regulators verify air-gapped |
| Coverage | ML model lifecycle (training, evaluation, deployment monitoring, bias testing) | LLM-in-the-loop runtime decisions (signed receipt per call, regulator handoff packets for 7 frameworks) |
| Verifiability | Reports + dashboards | Cryptographically verifiable chain · regulator runs verify.mjs air-gapped |
| Time-to-first-decision | Sales cycle · scoping engagement · onboarding (weeks to months) | 30 seconds (signup → key → install → first signed L12 entry) |
| Open source | Closed proprietary | Apache-2.0 verifier · BSL-1.1 source-available SDK |
| Where it runs | Holistic AI's platform integrated with operator's MLOps | SDK on operator's runtime · chain on operator's disk · verifier downloadable |
| Bias auditing built-in | Yes — core capability | No — out of scope (operator-supplied via separate tooling) |
When Holistic AI is the right primary
- You're auditing classical ML models (not just LLMs) for statistical bias, demographic disparity, performance drift.
- You need formal bias-assessment reports to attach to regulator submissions or internal ethics-committee reviews.
- You want a vendor providing both software and consulting services — model governance is part of a wider organisational change programme.
Holistic AI's depth on bias measurement and model governance reflects the maturity of their thinking on those topics. They are a real category leader for ML-lifecycle governance.
When Promethean is the right primary
- You're shipping LLM-in-the-loop features in regulated products (not training models from scratch).
- You need per-decision audit evidence with cryptographic non-repudiation — a regulator who asks "how do you prove this decision happened at the time it claims" gets a deterministic answer, not a vendor-platform tour.
- You want self-service onboarding · sign up + ship in a day · cancel anytime.
- You want the regulator to be able to verify your audit trail without going through your vendor's platform — independent verifier, public trust anchors, no Promethean infrastructure required at verification time.
When you use both
A regulated AI deployment with both an underlying ML model (recommendation, scoring) and an LLM-in-the-loop layer (explanation, summarisation, gating): Holistic AI covers the bias-and-governance audit of the ML model; Promethean covers the per-decision evidence for the LLM-in-the-loop part. The regulator handoff packet then references both: Holistic AI's bias report + Promethean's L12 chain.
For an Annex III high-risk AI system, this layered architecture is often what regulators want to see: one tool for the model lifecycle, one tool for the runtime evidence.
What Promethean is honest about
- Promethean does not do bias auditing. That's Holistic AI's territory; we surface the existence of bias-assessment evidence as operator-supplied in our handoff packets.
- Promethean does not do model lifecycle management (training data versioning, evaluation suites, staging). MLflow / Weights & Biases / similar tools cover that.
- Promethean's operating focus is the runtime audit trail — what happened, signed, chained, verifiable. The wider compliance programme is operator- and partner-owned.