GenAI features move fast—but production still needs the same discipline as any other release. Teams that treat prompt changes and model swaps as informal tweaks usually discover latency spikes, cost surprises, or unsafe outputs only after users do.
A practical AI DevOps approach starts with versioned prompts, model configs, and retrieval indexes—checked into git like application code. Pipelines should run automated evaluations (quality, latency, groundedness) before promotion, not only unit tests.
Add security and cost gates early: secret scanning for API keys, policy checks on tool access for agents, token/GPU budgets per environment, and canary rollouts for model versions. When quality dips or spend jumps, roll back the config the same way you roll back a bad deploy.
Cloud Ventures helps teams stand up GenAI platforms with these controls built in—so product velocity and operational reliability move together.