Executive Summary

Roughly 125,000 enterprises are actively embedding models into core operations and their security, compliance and MLOps teams are facing fragmented governance across models, data and compute—leading to inconsistent access controls, incomplete audit trails, and runaway inference costs that increase regulatory and operational risk. New laws like the EU AI Act demand explainability, auditable risk assessments and documented provenance, which current point solutions and homegrown stacks cannot reliably provide at scale. You could build a unified control plane that ties model artifacts, data lineage and compute orchestration into a single governance fabric: policy-as-code enforcement, immutable audit logs, automated risk scoring and explainability, plus a cost-aware routing layer that optimizes inference across cloud and on-prem resources. Expose open APIs and prebuilt connectors to MLOps, identity and cloud providers, and package enterprise offerings around a $500K ACV model (the $62.5B TAM from 125,000 enterprises) with professional services and SLAs to land proof-of-value deployments. The timing is favorable—AI-as-infrastructure, regulatory pressure and rising inference bills create buyer urgency; internal scoring gives this opportunity a Market Score of 92/100 and Revenue Potential of 90/100. To win in a medium-competition landscape you must demonstrate provable outcomes (meaningful inference cost reduction and measurable audit-time compression), pursue vendor-neutral integrations and compliance certifications, and be candid about the toughest hurdles: long sales cycles, complex legacy integrations and the engineering effort required to deliver ironclad provenance across heterogeneous stacks.

Market Opportunity

Control enterprise AI: unified model, data and compute governance platform targets a $62.5B = 125,000 enterprises x $500K ACV (enterprise security+AI governance adjacencies) total addressable market with medium saturation and a year-over-year growth rate of 35%+ CAGR driven by AI adoption and regulatory requirements.

Key trends driving demand: AI-as-infrastructure -- Organizations are embedding models into core ops, increasing demand for robust governance.; Regulatory pressure -- New laws (e.g., EU AI Act) require explainability, audit trails and risk assessments.; Cost-conscious inference -- Rising model inference costs push centralized orchestration and optimization.; Composable MLOps -- Standardized tooling (MLflow, k8s, vector DBs) enables integrated governance platforms..

Key competitors include Arize AI, Fiddler AI, Weights & Biases (W&B), Databricks (Unity Catalog + MLflow), WhyLabs.

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Control enterprise AI: unified model, data and compute governance platform

Executive Summary

Market Validation

Market Opportunity

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