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Start with identity: decide whose usage each limit counts
A gateway can only enforce a meaningful per-team limit if it can reliably attribute calls to that team. Map each team or application to a distinct credential or authenticated principal. A shared key used by several teams makes their usage difficult to distinguish, while a caller-supplied label alone should not be treated as secure isolation.
Choose the counter key for each policy deliberately. Depending on the gateway and policy surface, usage may be counted by subscription, runtime key, authenticated caller identity, originating IP, or a policy-defined expression. Microsoft’s Azure API Management AI Gateway guidance describes separate runtime access keys for applications and caller-identity scoping. An IP-based counter may be useful for some traffic controls, but it is not automatically equivalent to a team identity.
- Team-wide cap: Count all applications belonging to a team against one team identity or shared team counter, if the gateway supports it.
- Application isolation: Give each application its own identity and counter when one application should not consume another’s allowance.
- Model or tool guardrail: Add a narrower counter for a costly model or a downstream tool when that resource needs separate protection.
These scopes are not interchangeable. Decide whether the goal is to protect a team’s allocation, isolate applications within a team, or protect a particular backend. Some gateways may require policy expressions or a separate counter design to represent the scope you want.
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Separate token throughput, request rate, and longer-term quota
These controls answer different operational questions, so use them separately where needed:
- Token-throughput limit: Restricts the tokens attributed to an identity within a configured period. It helps divide constrained model capacity among consumers.
- Request-rate limit: Restricts how many calls an identity can make during a shorter interval. It can protect an API with strict call quotas or constrain bursts even when requests are small.
- Longer-period usage quota: Limits accumulated use over an hour, day, or another supported period. This can help manage operational allocation over time, but should not be mistaken for a precise monetary budget.
Do not assume a setting called “TPM” is the only limit a caller needs to satisfy. Microsoft documents token periods of minute, hour, and day in its Azure API Management portal policy surface, and request windows of 30, 60, 120, or 300 seconds on that same surface. These are documented Azure product options, not universal gateway standards. Microsoft’s broader APIM capability documentation describes additional token periods, including weekly, monthly, and yearly; confirm the product surface and API version you are configuring before relying on those periods.
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A token cap does not necessarily protect a downstream service from a flood of small requests, and a request cap does not prevent a few large prompts or responses from consuming a token allocation. Apply both when both forms of resource use matter.
Set a baseline first, then add targeted overrides
Record the actual capacity available from the model provider or deployment before assigning team limits. A gateway divides or constrains usage; it does not increase the underlying provider capacity. Microsoft describes the risk directly: one application can use a shared TPM quota and block other applications from reaching their backends.
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- Inventory the capacity and consumers. Identify provider or deployment limits, which teams and applications share them, and which models or tools have tighter constraints.
- Define a gateway-wide baseline. Set a default token policy, and a request policy if call volume also needs control, for the identities covered by the gateway.
- Allocate room among consumers. Choose limits that fit within available capacity while leaving room for other teams and expected bursts. The appropriate values depend on the deployment and workload; Microsoft’s published example of 500 tokens per minute per subscription key is illustrative, not a general recommendation.
- Add narrower overrides. Tighten the limit for a constrained model or tool rather than unnecessarily restricting every model or consumer.
- Stack controls when necessary. On a protected model, require calls to satisfy both token and request limits if compute capacity and downstream call volume are independent concerns.
Microsoft’s AI Gateway guidance recommends a broad baseline with narrower controls for constrained models or tools, and documents stacking token and request policies. Check the effective policy scope: a broad policy and a narrower override may interact differently across gateway products and configuration surfaces.
Understand how token use is measured and enforced
Token enforcement is only as predictable as the gateway’s accounting method. Microsoft documents optional prompt-token precalculation, which can reject an oversized prompt before forwarding it to a backend. LiteLLM documents another approach: reserve tokens before the call, then reconcile the reservation against actual usage after the response.
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In LiteLLM’s documented behavior, when a request omits an output-token cap, the proxy estimates an output reservation. That estimate can be too low for concurrent long responses or too high and reject a request that would otherwise fit. Where appropriate, set explicit output bounds in clients and test representative request sizes and concurrency rather than assuming every reservation equals final usage.
Also establish what happens when the gateway’s counter store is shared, unavailable, or disconnected. LiteLLM says its budgets require a database; the documented database-less deployment does not cap spend through that budget mechanism. Verify the current release’s storage requirements and failure behavior before treating a policy as a hard boundary. The same operational question applies to any gateway that relies on a shared store: confirm whether counters are consistent across instances or regions and what the gateway does if that store cannot be reached.
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How documented gateway and provider controls differ
The following compares capabilities described in vendor documentation; it is not a performance ranking. Exact availability and behavior can depend on the product tier, release, policy surface, or API version.
| Product or control plane | Identity or scope documented | Limits and token accounting documented | Operational visibility or financial boundary |
|---|---|---|---|
| Azure API Management portal policy | Token policies can be associated with a subscription key, originating IP, or policy expression, as described in Microsoft’s broader APIM capability documentation. | Portal documentation lists token periods of minute, hour, and day, and request windows of 30, 60, 120, or 300 seconds. Microsoft also describes optional prompt-token precalculation in broader APIM documentation. | Throttled calls return HTTP 429 with Retry-After. Portal monitoring can be used to review policy outcomes. Confirm the configured API version and scope. |
| Azure API Management AI Gateway tier | Microsoft documents caller-identity scoping and separate runtime access keys per application. | Microsoft recommends a broad baseline with narrower overrides and describes stacking token and request controls. | Documented response signals include remaining-token and consumed-token headers, plus a remaining-quota header for hourly or longer periods. Microsoft characterizes gateway policies as operational controls and points to provider billing or Azure Cost Management for financial reporting. |
| LiteLLM | Documentation describes team budgets, virtual keys, and per-model limits. | Documentation describes team-level RPM and TPM and pre-call token reservation followed by reconciliation. An exact set of quota windows is not stated in the cited LiteLLM documentation. | Remaining per-model request and token headers are documented. Budgets require a database; verify the current release’s behavior and storage configuration. |
| Kong AI Rate Limiting Advanced | Team-specific counter semantics are not stated in the cited Kong documentation. | The policy can inspect LLM responses to calculate token cost and enforce limits. Documentation describes configurable pricing per million tokens. | Documentation describes limit, availability, and reset headers. Do not infer a team-isolation model from these capabilities alone. |
| OpenAI API provider controls | OpenAI documents provider rate limits and project-scoped token headers. A project limit does not by itself create team-level gateway isolation; teams need an appropriate project and credential mapping. | Provider rate limits are separate from a gateway’s own counters. | OpenAI separately documents monthly API spend limits for organizations and projects; the provider-approved usage limit is distinct from configured spend limits. |
Keep gateway throttling separate from financial budgets
Tokens are not a dependable stand-in for dollars. Model prices can differ, usage measurement may be estimated or reconciled after a call, and provider billing may report spend on a different basis or schedule. Use gateway limits to manage operational access and allocation; use provider billing or a dedicated spend-limit control plane for financial monitoring.
OpenAI documents monthly API spend limits for organizations and projects, with the approved usage limit separate from the configured spend limit. For Azure, Microsoft points administrators to provider billing or Azure Cost Management for financial reporting. Verify the scope and behavior of the provider-side controls separately from gateway policies.
Validate isolation, enforcement, and client behavior
Test the effective policy with distinct credentials and representative traffic before relying on it for shared production capacity. Include enough concurrent and differently sized requests to exercise the accounting behavior you expect.
Quick Recap
- Check identity isolation. Send traffic under two team or application identities and verify each counter reflects only its intended callers. If a team-wide shared counter is intended, verify that the team’s applications contribute to that shared counter.
- Exercise each limit independently. Test a token overage with a low request count, then a request-rate overage with small requests. Confirm that both controls activate when configured together.
- Inspect telemetry. Review gateway monitoring and logs. Where supported, inspect remaining-token, consumed-token, remaining-quota, or provider rate-limit headers to understand the effective scope and reset behavior.
- Test throttling recovery. Azure APIM portal documentation says throttled calls return HTTP 429 with
Retry-After. Ensure clients honor the returned delay rather than immediately retrying and amplifying load. For other gateways, use the actual documented response signals and behavior for that product. - Test storage and replica behavior. Where counters rely on a database, Redis, or another shared store, verify enforcement across the instances and regions that serve traffic, and test the documented behavior when the store is unavailable.
- Review allocation after workload changes. Revisit limits when teams, models, provider capacity, or request patterns change; a previously reasonable allocation can become restrictive or insufficient.
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