To control OpenAI API costs, limit generation and unnecessary context, reuse stable prompt prefixes where caching applies, and monitor spending with both alerts and—if you can tolerate requests failing at the cap—a hard spend limit. Alerts only notify you; they do not stop API traffic. Track actual costs as well as token usage, because usage reports and invoice-oriented cost reports can differ.
Set token and context limits for the task
Keep requests no larger than the work requires. Set an appropriate maximum output, remove irrelevant context, and avoid sending an ever-growing conversation history when earlier turns are no longer needed. These controls can reduce the tokens processed or generated, but they also affect behavior: a low output limit may cut off a useful answer, and removing context can make responses less informed.
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Check the endpoint’s parameters
Output-limit parameter names and supported behavior vary by endpoint and model. Use the API reference for the endpoint you call rather than assuming one setting applies everywhere. For reasoning-capable Chat Completions models, the reference documents reasoning_effort; reducing it can use fewer reasoning tokens and produce faster responses, but may affect the result. See the Chat Completions API reference.
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Use conversation truncation deliberately
Realtime supports configurable truncation. Retaining less conversation history can constrain token use, but discarded history may also reduce cache reuse on later turns. Choose a truncation policy with both effects in mind, as described in the Realtime API reference.
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Use prompt caching for repeated prefixes
Prompt caching reuses computation when requests share an eligible prompt prefix. Put stable instructions, tool definitions, and other reusable content first; place request-specific details after that shared material. A changed or new suffix still needs processing, so caching is not a blanket discount on every input token.
Check eligibility and model-specific pricing
OpenAI’s guide says GPT-5.6 and later require a visible prefix of at least 1,024 tokens for caching; earlier model families have different thresholds and behavior. The guide also says cache writes for GPT-5.6 and later are priced at 1.25 times the standard uncached input rate. Retention, eligibility, and cache-read or cache-write prices vary across model families, so check the live prompt caching guide for the model you use.
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Verify cache use in usage data
Do not infer a cache hit merely because two prompts look similar. Inspect cache-read usage for your requests and compare it with the eligible repeated-prefix tokens. Savings depend on how much of your workload has reusable prefixes, whether those requests actually hit the cache, and the model’s applicable rates; there is no workload-independent savings percentage to assume.
Distinguish spend alerts from hard limits
An alert provides visibility, not enforcement. OpenAI’s spend limits guide states: “Spend alerts do not enforce a cap.” API traffic continues after an alert threshold is reached.
| Control | What happens at the threshold | Operational trade-off |
|---|---|---|
| Spend alert | You receive a notification; requests continue. | Useful for awareness, but it does not prevent further spending. |
| Hard spend limit | Affected requests can return HTTP 429 errors after tracked spend reaches the limit. | Can constrain spending, but calls may fail and enforcement is not instantaneous; spend may slightly exceed the configured amount. |
Use an alert for visibility. Set a hard limit only if your application can tolerate requests failing when the limit is reached, and account for the possibility that enforcement will not stop spending exactly at the configured amount.
Monitor costs in the right place
The Usage API reports granular usage and can group or filter by dimensions such as project, user, API key, model, and service tier, depending on the endpoint. Usage totals and costs can differ slightly because consumption and spending are recorded differently. For financial reporting intended to reconcile with an invoice, OpenAI recommends the Costs endpoint or the Costs tab in the Usage Dashboard. See the Usage API reference.
Compare changes against a baseline
- Record costs by project, model, and workload over a representative period.
- Change one input at a time, such as the prompt, output limit, or model setting.
- Compare token categories and actual costs over a comparable interval, using the Costs endpoint or dashboard when you need invoice-oriented figures.
- Check response quality and application errors alongside the cost change; a lower bill is not useful if the new limits cause unacceptable results or failures.
Estimate spending using live rates
OpenAI pricing separates input, cached input, cache writes, and output, with rates that vary by model, context, and processing mode. Estimate cost by multiplying observed usage in each category by its corresponding live rate instead of applying one blended cost-per-token figure. Check the current OpenAI API pricing page before budgeting or comparing models; rates and supported caching behavior can change.
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