AI inference capacity planning means estimating the tokens, model calls, latency and service capacity a business will need as AI moves from pilots into routine operations. Build a baseline from measured usage, forecast each workflow by volume and model, then stress-test cost and throughput under higher adoption and agent automation. Assign an accountable budget owner and compare forecasts with actual usage monthly.
What should an AI capacity forecast include?
Forecast by workload rather than by licences alone. Record active users or agents, runs per day, calls per run, input and output tokens per call, model, price, retries, context caching and latency requirements. Separate predictable background work from interactive peaks; a monthly cost forecast cannot reveal a concurrency bottleneck.
How do you estimate monthly token demand?
For each workflow, multiply monthly runs by average model calls per run and average tokens per call, separating input and output. For example, 500 daily runs across 22 workdays with four calls per run and 3,000 input plus 500 output tokens per call generate 132 million input and 22 million output tokens each month. Apply current vendor rates separately, not a blended headline price.
What happens when agents increase usage?
Scenario-plan light, expected and heavy adoption. An agent may search repeatedly, call tools, retry failures and inspect longer contexts than a human chat. Track cost per completed useful workflow as well as total tokens, because fewer tokens can still be wasteful if the outcome is poor.
How do you plan for peaks and resilience?
Measure peak calls per minute, latency targets and applicable service limits alongside monthly tokens. Choose which jobs can be queued, delayed, cached or routed to a smaller model. Determine whether committed capacity, multiple deployment regions or a private model is justified by measured workload characteristics and contractual constraints.
Who owns the AI capacity budget?
Give finance visibility into spend while product and technology teams retain responsibility for throughput and quality. Review forecast versus actual each month and flag costly workflows. Start with the wider AI token strategy, then review enterprise AI token economics to identify avoidable consumption. Ask AI Build about capacity forecasting.