How should I choose between on-demand and provisioned AI capacity?

Scope

Use this comparison for hosted model inference where both consumption-based and reserved/provisioned purchasing modes are available. The decision concerns capacity and service behavior, not which model or cloud is best.

Why it happens

Teams divide monthly tokens by seconds and call the result demand. That erases the workload shape. A chat request with a long streaming response occupies capacity differently from a short classification. Batch jobs can move in time; interactive calls cannot. Bursts, retries, regional failover, and long-tail outputs determine queueing even when the daily average looks modest.

Provisioning also creates option cost. A commitment may save money at stable utilization but reduce flexibility when a new model, architecture, or product changes the workload. On-demand capacity preserves flexibility but may expose the service to quotas, variable latency, or a more expensive steady base.

Diagnosis

Build a four-week demand profile at one-minute or finer resolution. Separate traffic by model, input and output token bands, latency objective, priority, region, and whether it can wait. Record arrival bursts, service time, throttles, retries, and abandoned requests.

Then ask:

• Is there a base level present in most intervals?
• Which percentile must meet the latency objective?
• Can overflow degrade, queue, route, or fail safely?
• What happens during a regional or provider impairment?
• How likely is the model or workload to change during the commitment?
• What is the cost of idle capacity versus a missed outcome?

Solution

Model at least three plans: on-demand only, provisioned only, and hybrid. For each, simulate low, expected, and high demand plus one failure scenario. Use current provider capacity measurements or load tests; do not translate units from marketing labels alone.

For a hybrid plan, assign the predictable, latency-sensitive base to provisioned capacity. Define overflow eligibility, priority, maximum queue time, and quality implications. Keep batch or delay-tolerant work away from the interactive reserve unless spare-capacity use is explicitly safe.

Add a renewal decision before the commitment expires. Review actual utilization, overflow, outcome failures, price changes, and model migration plans.

Artifact

Complete one sheet per candidate plan:

Decision input	Low	Expected	High/failure
Requests and token shape by class
Arrival burst and concurrent work
Required p50/p95/p99 latency
Provisioned units and measured headroom
On-demand overflow and quota
Queue, degrade, route, or reject rule
Idle, usage, overflow, and commitment cost
Failed-outcome or delay cost
Region/provider failure behavior
Model-change exit cost

Record the quote date, provider documentation version, load-test artifact, owner, renewal date, and rollback plan.

Common mistakes

• Sizing from average daily tokens.
• Assuming a provider unit maps to a fixed request rate across token shapes.
• Counting provisioned cost without on-demand overflow, or vice versa.
• Ignoring commitment risk when models and product behavior change quickly.
• Reserving capacity without a tested queue, overflow, and regional-failure policy.