Continuous control of your AI estate
Recurring optimisation, governance and orchestration across the AI environments you already run. Engineered to lower cost, increase utilisation and keep AI accountable.
Managed AI Optimisation
Ongoing AI cost optimisation, governance, reporting and performance monitoring across every environment you run AI in.
- Per-model & per-team FinOps
- Inference path tuning
- Capacity & utilisation reviews
- Governance reporting
- Quarterly outcome reviews
AI Infrastructure Management
Oversight and optimisation of enterprise AI infrastructure environments across cloud, private and GPU platforms, one team, one contract.
- 24×7 monitoring
- Incident & change management
- Patch & lifecycle
- Vendor consolidation
- Co-terminated contract
An advisory-led AI infrastructure control layer
Moksha's methodology brings cost, governance and performance under continuous control across the AI environments you already run.
AI observability
Real-time visibility into GPU utilisation, model latency and throughput across every environment.
Optimisation
Continuous workload, cost and capacity optimisation, automated where possible, governed always.
Governance
Model access policies, data handling rules and audit trails aligned to the EU AI Act.
Workload visibility
Per-model, per-team and per-environment workload telemetry in a single pane of glass.
Model routing
Policy-based routing of inference and training across cloud, private and GPU.
Infrastructure intelligence
Predictive recommendations for cost, performance and architectural change.