Research
AI Infrastructure & Operations
Running AI reliably in production takes infrastructure, disciplined operations and cost control. This area covers AI compute and cloud platforms, MLOps and LLMOps, model evaluation and monitoring, and AI cost management.
Topics in AI Infrastructure & Operations
Overview
Once an AI system goes live, it needs the same operational discipline as any critical application, plus some new practices. Models must be versioned, tested before release, monitored for quality and drift, and replaced as better options appear. Generative AI adds prompts, retrieval pipelines and guardrails that also need to be managed.
Costs behave differently too. Usage-based pricing for model APIs and accelerator capacity can grow quickly with adoption, so cost visibility has to be designed in from the start.
What leaders need to get right
- Platform choice. Decide when to use managed AI services, rented capacity or your own infrastructure.
- Release discipline. Test models and prompts against defined criteria before every change.
- Monitoring. Watch quality, safety, latency and cost in production.
- Unit economics. Know the cost per task, query or user.
Questions leaders should ask
- What tests must a model or prompt pass before it goes live?
- How would we know if an AI system started giving worse answers?
- What does one AI query or task cost us?
- How quickly could we switch model providers?
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