Set AI API controls in two layers: the provider’s quotas (requests and tokens allowed by your organization, project, model, or usage tier) and limits in your own application or gateway (traffic you agree to accept from users). Then cap generated output at a realistic size, schedule calls against every quota dimension, and handle 429 responses with bounded backoff. A provider quota is not the same thing as protection for your endpoint, and an output cap does not by itself control request rate.
What you are controlling
An AI request can be constrained by several independent counters. A request may be below its requests-per-minute (RPM) allowance but above its input-tokens-per-minute (TPM) allowance, or it may fit both minute limits while exceeding a daily, spend, image, audio, or organization-level quota. The first limit reached determines whether the provider accepts or throttles the call.
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Provider-enforced quotas
These limits are imposed by the model service. Their scope and values depend on the provider, model, account or usage tier, and sometimes the project or organization. They are upstream of your code: changing your gateway’s threshold cannot increase them. Treat the provider’s live console as authoritative rather than copying a tier table into configuration.
Application and gateway limits
Your own limiter protects an API, worker pool, or gateway from bursts, abusive clients, and accidental fan-out. You choose the identity key (for example, authenticated user, tenant, API key, account, or IP address), route, algorithm, and threshold. It protects your infrastructure even when the upstream provider has capacity, but it cannot guarantee that the provider will accept every request you pass through.
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Output controls
An output control limits how much a model can generate for one request. It is separate from RPM and from your input prompt. A generous allowance can consume token quota and increase cost; an allowance that is too small can truncate a useful answer. Reasoning models may count hidden reasoning tokens inside the completion budget, so visible text can be shorter than the configured maximum.
Inspect the live quota before changing code
Start with the exact provider, model, endpoint, and account or project used in production. A development key, a different project, or a fallback model can have different limits.
| Provider | Common dimensions | Scope and important qualifications | Where to verify current values |
|---|---|---|---|
| OpenAI | Requests per minute (RPM), requests per day (RPD), input or total tokens per minute (TPM), tokens per day (TPD), images per minute (IPM), and audio-minute limits for applicable models | Limits can apply at organization and project levels; some model families share a pool. The approved monthly usage limit is different from organization or project spend limits. | The Limits page in the OpenAI platform console, for the organization and project that production uses. |
| Gemini API | Requests per minute (RPM), input tokens per minute, and requests per day (RPD); some models add image-per-minute or token-per-day dimensions | Limits are per project rather than per API key, vary by model and usage tier, and can include rolling 10-minute spend limits for eligible tiers. Daily quotas reset at midnight Pacific time. | The quota and limits view in Google AI Studio for the active project. |
| Claude API | Requests per minute, input tokens per minute, and output tokens per minute | Limits are set at the organization level and depend on usage tier. The current Help Center guidance (June 26, 2026) says a 429 identifies the exceeded limit. | The limits view in Claude Console and the current API documentation. |
Record the values with their scope and reset behavior in configuration management. Do not turn an account-specific limit into a universal promise in documentation: providers can change tiers, models can have separate pools, and actual capacity can be lower during overload.
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Choose a safe output allowance
Use the parameter for the endpoint you actually call
Parameter names are not interchangeable. For OpenAI Responses requests, use max_output_tokens. For Chat Completions requests using reasoning models, use max_completion_tokens; the current reference describes this as including visible and reasoning tokens. The older max_tokens field is deprecated for Chat Completions and is not compatible with every model. Confirm the selected model’s reference before deploying a shared client.
For the Gemini API, generation configuration uses maxOutputTokens. Its default varies by model. Optional controls such as temperature and topP alter sampling, not the hard ceiling. Safety settings can change whether a candidate is blocked: explicitly specified categories override their defaults while omitted categories retain defaults. Check model support for each optional field.
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Do not assume that every provider exposes the same output field or that an absent field means unlimited output. Make the endpoint and model part of the client configuration, and reject an unsupported parameter during testing rather than silently dropping it.
Derive a cap from the product task
- Define the useful result, such as a 120-word summary, a JSON object with a fixed set of fields, or a multi-paragraph explanation.
- Measure prompt and completion usage for representative short, median, and worst-case inputs. Include any hidden reasoning allowance reported by the provider.
- Set the cap above the largest useful completion, with a small operational margin, rather than choosing a very large model maximum.
- Log finish reasons or truncation indicators. If useful answers regularly hit the cap, raise it or shorten the requested task; if they never approach it, lower it to release quota and control cost.
Shape output without confusing soft and hard controls
- State the desired length in plain language, such as “answer in six bullet points, each no more than 20 words.”
- Give an example with the intended level of detail and field order.
- Use a supported stop sequence when a reliable delimiter marks the end of the result. A stop sequence can end generation early but cannot make a model produce missing content.
- Use a provider’s verbosity setting, where available, as a soft detail preference. OpenAI’s GPT-5 guidance describes verbosity as affecting detail, not as a hard token limit.
- For machine-readable results, use the endpoint’s supported structured-output feature and validate the response in application code. A prompt alone is not a schema guarantee.
OpenAI’s guidance also notes that its allowance calculation can use the greater of the configured maximum and an estimate based on prompt characters. Keep prompts, examples, and retrieved documents under control when token ceilings are tight.
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Use a scheduler or token bucket before sending a request. The limiter should represent every quota dimension that applies to the selected model and should be shared by all workers drawing from the same organization or project quota. A per-process counter is insufficient when several instances send traffic concurrently.
1. Normalize the request cost
Estimate input tokens before dispatch and reserve the configured output allowance (including reasoning tokens where the provider counts them). Keep separate counters for requests, input tokens, output tokens, and any daily or spend budget that matters. If the estimate is uncertain, reserve a conservative amount and reconcile it with the provider’s usage data after completion.
2. Select an enforcement algorithm
- Token bucket: tokens refill at a defined rate and a bucket permits a bounded burst. It is useful when a small burst is acceptable but sustained traffic must match the provider rate.
- Sliding window: counts recent events continuously and avoids the sharp boundary of a fixed minute.
- Fixed window: resets at a clock boundary and is simple, but can allow a cluster at the end of one window followed immediately by another cluster at the start of the next.
Use one limiter per relevant scope. For a project-level provider quota, all tenants may need to draw from a shared project bucket, while a tenant bucket can prevent one customer from consuming the entire pool.
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3. Reserve, dispatch, and reconcile
- Determine the provider, model, project, estimated input tokens, requested output ceiling, and idempotency key for the operation.
- Ask the shared limiter for both a request slot and the required token reservations. If any dimension is unavailable, wait until its next refill instead of sending and causing a 429.
- Dispatch the call with a deadline and a correlation ID.
- On success, record actual input and output usage and return unused reservations to the bucket when your accounting model supports that safely.
- On rejection, record the status, provider error code, retry signal, and reservations. Do not count a failed attempt as free merely because no answer was returned; some providers count unsuccessful requests toward minute limits.
Persist counters in a store that supports atomic updates or use a dedicated rate-limit service. Clock skew between workers can make a fixed-window implementation appear to exceed its threshold, so prefer server-side timestamps and atomic expiry.
Protect your own endpoint with a gateway limit
Provider quotas protect the upstream service; a gateway limit protects your application. Decide what “one client” means before choosing a number. An IP key is easy to deploy but merges users behind a corporate NAT. An authenticated account or tenant key is fairer for a multi-tenant product. An API-key key is appropriate when customers receive distinct credentials, but it must be combined with an account or project ceiling to stop key rotation from bypassing protection.
Set the route, window, and threshold
- Choose the route to protect, such as
/v1/summarize, rather than applying one threshold blindly to every endpoint. - Measure normal request rate, concurrency, payload size, latency, and burst behavior for each client class.
- Select fixed-window, sliding-window, or token-bucket semantics and document the burst allowed by that choice.
- Start below the point where your workers, database, or provider budget becomes unstable; raise it only after observing sustained traffic.
- Return a clear 429 response with a retry hint when your gateway rejects a request, and include a request ID for support.
Cloudflare’s AI Gateway documentation illustrates a configured limit of 100 requests per 60 seconds with a selected counting method. That is an example configuration, not a recommended threshold or a measured capacity. Gateway products differ in whether their counters are global, regional, best-effort, or strongly enforced.
AWS API Gateway documents token-bucket throttling and cautions that “Throttles are applied on a best-effort basis and should be thought of as targets rather than guaranteed request ceilings.” Design downstream services so a short overshoot is safe instead of treating a gateway setting as an absolute wall.
Handle 429 responses and overload safely
Read the response before retrying
Capture the HTTP status, provider error code, response body, and Retry-After header. OpenAI, Google, and Anthropic can use different error shapes; classify by the response rather than by a string match in one SDK. Anthropic’s current guidance says a 429 identifies the exceeded limit and supplies retry-after. Wait at least that duration.
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If no retry delay is supplied, use bounded exponential backoff with jitter. For example, calculate a delay that grows after each attempt, add a random fraction to prevent synchronized workers, and cap both the delay and total operation time. Stop after a small, configured attempt count and return a useful failure to the caller. Never spin in a tight loop.
Reduce pressure while waiting
- Lower concurrency or pause the queue that targets the exhausted model or project.
- Reduce output ceilings and prompt size only when doing so still meets the task’s quality requirement.
- Spread non-urgent work over time; OpenAI recommends gradual traffic increases and identifies the Batch API as an option for eligible asynchronous jobs.
- Route only when an explicitly configured fallback has compatible quality, safety, and data-handling properties. A fallback is not a reason to replay the same request repeatedly.
Make retries safe
Use an idempotency key or durable job identifier for operations that can create side effects. A timeout after the provider accepted a request is not proof that it was never processed. For streaming, an error can arrive after output has already started; do not automatically replay after consuming partial output unless your application can deduplicate or safely resume.
Keep separate counters for attempts, successful completions, throttled calls, and elapsed retry time. This exposes a retry storm that would be hidden if dashboards counted only successful responses.
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OpenAI
Check the organization and project limits for the exact model. Some model families share a pool, so adding a second model may not create independent capacity. Keep approved monthly usage limits separate from spend limits configured by your organization or project. For Responses, set max_output_tokens; for reasoning-model Chat Completions, set max_completion_tokens and account for visible plus reasoning tokens. Verify compatibility because the deprecated max_tokens field is not a universal replacement.
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Gemini API
Quotas are attached to the project, not to an individual API key. Inspect RPM, input TPM, RPD, and any model-specific image or token dimensions in Google AI Studio. Remember that RPD resets at midnight Pacific time, and eligible tiers may have rolling 10-minute spend limits. Configure maxOutputTokens in the generation configuration, then use temperature and top-p only for sampling behavior. Safety settings affect blocking thresholds and are independent of the output ceiling.
Claude API
Inspect the organization-level limits for the current usage tier in Claude Console. Track requests, input tokens, and output tokens as separate reservations. On a 429, read the exceeded-limit information and retry-after, wait at least that long, and reduce concurrency before trying again. Do not copy a static tier table into production configuration because limits and tiers can change.
Measure and tune the controls
Every request log should contain the provider, model, project or organization scope, route, client key, requested output cap, estimated input tokens, actual input and output tokens, latency, status, finish reason, retry count, and total elapsed time. Exclude prompt or response content from ordinary metrics when it could contain sensitive data.
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- Truncation: count responses that hit the output cap or end without the expected delimiter or structured fields.
- Throttle rate: separate your gateway’s 429s from provider 429s and overload responses.
- Fairness: identify tenants or routes consuming disproportionate shared capacity.
- Cost and latency: correlate output caps, reasoning usage, retries, and queue time with the user-visible result.
Tune from observed distributions: lower an oversized cap when completions consistently finish early, raise it when useful results are truncated, and adjust gateway bursts after testing traffic at window boundaries. A configured limit is a control to measure, not a promise that upstream capacity will always be available.
Common failure modes and fixes
| Symptom | Likely cause | Fix |
|---|---|---|
| Requests succeed at low traffic but fail during a burst | Fixed-window boundary burst, per-worker counters, or a shared project quota | Use a shared sliding-window or token-bucket limiter, reduce concurrency, and test adjacent-window behavior. |
| RPM appears under limit while requests still receive 429 | Input or output TPM, daily quota, spend limit, or another model-specific dimension is exhausted | Log every dimension, reserve tokens before dispatch, and inspect the provider’s live limits. |
| Answers stop mid-sentence or fail schema validation | Output ceiling is too low, or hidden reasoning consumed part of the allowance | Inspect finish reasons, increase the task-specific cap, reduce prompt size, and validate structured output in code. |
| Retry traffic makes throttling worse | Immediate retries, synchronized workers, or retries after partial streaming output | Honor Retry-After, add bounded jittered backoff, cap attempts, and use idempotency or deduplication. |
| One customer exhausts capacity for everyone | Only a global limiter exists | Add per-tenant or per-account limits plus a shared provider-project ceiling. |
| Changing an API key does not increase quota | The provider limit is project- or organization-scoped | Check the documented scope and configure the limiter around that shared scope. |
Deployment checklist
- Confirm the production provider, model, endpoint, project, organization, and usage tier.
- Copy live quota values into configuration with scope, reset period, and review date.
- Use the endpoint’s supported output field and set a task-specific ceiling.
- Account for reasoning tokens where the provider includes them in the completion allowance.
- Share request and token counters across all workers that consume the same upstream quota.
- Choose a gateway key, route, algorithm, burst, and threshold from measured traffic.
- Return 429 responses with a retry hint and distinguish gateway throttles from provider throttles.
- Honor
Retry-After; otherwise use bounded exponential backoff with jitter. - Protect non-idempotent operations with idempotency or durable job identifiers.
- Monitor usage, truncation, retries, latency, cost, and fairness, then tune from production evidence.
Final design principle
Keep three decisions separate in code and dashboards: how much traffic your service accepts, how much provider quota each request reserves, and how much text or reasoning one response may consume. That separation makes a 429 diagnosable, prevents retry storms, and lets you improve output quality without accidentally turning a larger completion cap into an outage.
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