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.
New report: AI Infrastructure Sourcing 2026
A 47-page enterprise buyer’s report to deciding where AI workloads run across hyperscalers, neoclouds, sovereign cloud and private AI compute, with ready-to-use due-diligence and scoring tools.
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.
Research report
Decide where your AI workloads should run
Get the workload-first decision framework, a 50-question provider due-diligence questionnaire, placement and scoring worksheets, and an exit readiness test.
Featured research report
AI Infrastructure Sourcing 2026
47-page report to hyperscalers, neoclouds, sovereign cloud and private AI compute. Includes a 50-question provider due-diligence questionnaire and decision worksheets.
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?
Other research areas
Related research reports
In-depth reports with ready-to-use templates.
- AI Infrastructure Sourcing 2026$49.99 PDF report
- AI Inference Cost Control$49.99 PDF report
Related research reports
Reports connected to this topic.
Latest insights on AI Infrastructure & Operations
Copilot Adoption Metrics Changed: How to Keep 28-Day Dashboards Comparable
GitHub's November 6 dashboard update adds chat and VS Code agent engagement without changing adoption phases. A six-test measurement plan.
Copilot Telemetry Gaps: Why Active Users Can Disappear From Usage Detail
GitHub now flags missing Copilot client telemetry and outdated IDE versions. Six enterprise tests to prevent misleading adoption and cost decisions.
Copilot Code Review Addressed Rate: Why It Is Not a Fix Rate
GitHub's new Copilot code-review metrics measure responses to feedback, not verified fixes. A practical enterprise evidence funnel and five controls.