Give each team or application its own authenticated identity at the gateway, then set token-throughput limits separately from request-rate limits and longer-period budgets. Start with the provider capacity you actually have, apply a shared baseline with narrower model or tool overrides, and test that counters stay isolated and callers handle throttling correctly. A gateway can divide or constrain shared capacity; it cannot create more provider capacity.
Design quotas around an identity the gateway can authenticate
A per-team limit only works if the gateway can tell one team’s traffic from another’s. Issue a distinct gateway credential or authenticated principal for each application or team. Decide whether each counter should follow the team, application, model, tool, or another identity, and use that same definition in policy and monitoring.
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A shared runtime key makes attribution and per-team enforcement ambiguous. A caller-supplied label by itself is not a secure boundary: use an identity the gateway authenticates, and do not let an untrusted caller choose which quota it consumes. Microsoft’s Azure API Management AI Gateway guidance describes separate runtime access keys per application and caller-identity scoping. Its capabilities documentation frames the operational risk directly: one app should not consume the whole TPM quota and block other apps from accessing needed backends.
Map the counter to the boundary you want to protect
- Team: useful when several applications share an allocation and should be managed together.
- Application: isolates independently operated workloads and makes it easier to identify a noisy client.
- Model or tool: useful for tighter controls on a constrained model or a downstream service with its own call limits.
These dimensions are not interchangeable. If a team has several applications but receives a single shared team allowance, decide whether an application may use the full team budget or whether each app also needs its own ceiling. Make that policy choice explicit rather than assuming the gateway will infer it.
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Separate token throughput, request rate, and accumulated budget
These controls answer different questions. Token-per-minute or token-per-hour limits constrain throughput over a window; requests-per-window limits constrain call volume; hourly or daily quotas cap accumulated use over a longer period. A workload can be below its token limit but exceed its request limit through many small calls, or exceed its token limit with a few long prompts and responses.
| Control | What it constrains | Use it when |
|---|---|---|
| Token rate limit (TPM or equivalent) | Token use within a configured time window | Protecting model throughput or dividing a shared token allocation among consumers |
| Request rate limit (RPM or equivalent) | Number of calls during a request window | Protecting a downstream API, constraining bursts, or limiting call volume independently of prompt and response size |
| Longer-period quota or budget | Accumulated use over a longer period, such as an hour or day | Limiting sustained consumption; use a separate spend control when the goal is a financial ceiling |
Use token and request controls together when both model capacity and call volume matter. Neither is a substitute for a financial ledger: different models can have different prices, and token measurement or reconciliation may be estimated or delayed.
Set capacity before choosing per-team numbers
First identify the provider or deployment capacity actually available to gateway consumers and any allocation already reserved for other workloads. Then divide that capacity among teams, leaving room for expected bursts and shared operational needs. The Microsoft documentation gives 500 tokens per minute per subscription key as an illustrative example, not a recommended quota for every team.
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A policy value that adds up to more than available provider capacity does not increase that capacity. It can instead leave consumers competing at the provider or deployment boundary, where they may encounter throttling even though their gateway counters have not been exhausted.
Apply a baseline and deliberate overrides
Establish a gateway-wide baseline, then narrow it where a model, tool, or consumer needs stricter treatment. Microsoft recommends this broad-baseline-plus-targeted-overrides approach for its AI Gateway tier. When both throughput and call volume matter, stack token and request policies so a request must satisfy both.
Choose windows for the product surface in use
Window choices are product-specific, not universal gateway standards. Azure API Management’s portal policy documentation lists minute, hour, and day periods for token limits and 30, 60, 120, or 300 seconds for request-limit windows. Broader APIM capability documentation describes additional periods, including weekly, monthly, and yearly. Confirm the particular policy surface, scope, and supported API version before copying a setting into production.
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Use a shorter window when the operational need is to shape bursts or protect real-time capacity; use a longer window when the goal is to cap sustained consumption. If both needs exist, configure distinct controls rather than treating a daily quota as a rate limit.
Keep the scope and override behavior understandable
Write down the effective scope of every limit: which authenticated identity is counted, which model or tool it covers, and whether the policy inherits a baseline or replaces it. Check what happens when a request matches both a general policy and a narrower override. This matters especially when a team’s shared allowance and an application-level ceiling are both in force.
Know how token accounting is enforced
Token enforcement may be based on an estimate before a request reaches the model, usage reported after the response, or a reservation followed by reconciliation. That implementation affects oversize-request handling, concurrent calls, and how closely the counter tracks actual use.
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- Standard Support covers software updates and round-the-clock emergency help. Add a Basic or Total Security Suite to activate IPS, gateway antivirus, and web filtering so threats are blocked before they reach users.
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Precalculate or estimate before sending
Microsoft describes optional prompt-token precalculation that can reject an oversized prompt before sending it to the backend. This protects the backend from requests that already exceed the configured token boundary, but verify what the selected policy counts and how it treats generated output.
Reserve tokens, then reconcile usage
LiteLLM documents reserving tokens before a call and reconciling against actual usage afterward. When a request has no output-token cap, its proxy estimates an output reservation. An estimate that is too low can under-reserve during concurrent long responses; one that is too high can reject work that would otherwise fit. Set explicit output bounds where the application can do so, and test representative concurrency rather than assuming reservations exactly match final usage.
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Compare documented gateway and provider controls
The options below describe documented capabilities, not independently tested performance or a product ranking. Check current product documentation, tier availability, release behavior, and API version before deployment.
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| System and source | Identity and limits documented | Accounting and visibility | Financial-control boundary |
|---|---|---|---|
| Azure API Management portal policies and AI Gateway tier | The portal documents token limits by period and request limits by window. APIM capabilities describe subscription key, originating IP, or policy-expression counters; the AI Gateway tier describes caller-identity and IP counting, separate runtime keys per application, and baseline policies with narrower overrides. | Microsoft documents optional prompt-token precalculation. The AI Gateway tier documents remaining-token and consumed-token response headers, plus a remaining-quota header for hourly or longer periods; the portal describes policy monitoring and 429 responses with Retry-After. | Microsoft characterizes gateway policies as operational controls and points to provider billing or Azure Cost Management for financial reporting. |
| LiteLLM documentation | Documents shared team budgets, team-level RPM and TPM, per-model limits, and virtual keys. | Documents remaining per-model request and token response headers, token reservation before a call, and reconciliation after usage. Budget enforcement requires a database; confirm current storage and failure behavior. | In a database-less deployment, the documented budget enforcement does not cap spend. |
| Kong AI Rate Limiting Advanced policy documentation | Documents a policy that inspects LLM responses to calculate token cost and enforce limits; team-specific counter semantics are not stated in the cited documentation. | Documents limit, availability, and reset headers and configurable pricing per million tokens. Cross-replica or regional counter semantics and store-failure behavior are not stated in the cited documentation. | Configurable token pricing is documented; a financial-budget guarantee is not stated in the cited documentation. |
| OpenAI API rate-limit and spend-limit documentation | Provider rate limits and organization/project spend limits are documented separately. Team-level gateway isolation is not established unless teams are mapped to projects and credentials appropriately. | The rate-limit guide documents response headers, including remaining project-scoped tokens. | Monthly API spend limits are a separate provider-side control; the provider-approved usage limit is separate from the configured spend limit. |
Configure and validate the policy in this order
- Create identities: issue a distinct authenticated gateway credential or principal for each team or application boundary you intend to meter.
- Record capacity: document provider/deployment capacity and existing allocations before setting consumer ceilings.
- Set token throughput: select a supported window and per-identity value that respects available capacity and leaves room for other consumers.
- Add request-rate limits: use a request window where call volume or downstream quotas need protection independently of token use.
- Apply overrides: set a default baseline, then add narrower model or tool controls; confirm whether stacked controls must both pass.
- Confirm counter behavior: determine whether counters are shared across gateway replicas, regions, or instances, whether a database or Redis is required, and what happens if the counter store is unavailable.
- Expose and inspect signals: review headers or telemetry for consumed and remaining quota, and check policy outcomes in monitoring.
- Exercise limit cases: send representative traffic for each identity, then deliberately exceed token and request limits to verify the expected rejection and isolation.
- Validate caller behavior: confirm clients treat throttling as a signal to slow down rather than immediately repeating the same request.
- Keep financial controls separate: use provider billing, Azure Cost Management, or dedicated provider spend-limit controls for financial reporting and budgets.
Handle throttling and storage failures safely
Azure API Management’s portal documentation says throttled calls return HTTP 429 with a Retry-After header. Clients should honor that delay and use bounded backoff rather than retrying immediately, which can intensify a burst. Validate the actual response and policy scope in the deployed configuration instead of assuming a client library handles every gateway’s throttling behavior.
Shared counters also require an explicit resilience decision. If a gateway deployment uses a database or Redis for enforcement, establish whether a store outage causes requests to be rejected, allowed without a cap, or handled some other way. LiteLLM’s documentation makes database dependence material to its budget behavior; release-specific configuration and failure modes should be confirmed before treating that limit as a hard spend or usage barrier.
Use provider spend limits for money, not gateway token quotas
Token quotas are useful operational guardrails, but they are not exact dollar caps. Token prices vary by model, and estimation, reconciliation, or delayed reporting can separate a gateway counter from the final billed amount. Microsoft recommends provider billing or Azure Cost Management for financial reporting. OpenAI separately documents monthly organization and project API spend limits, distinct from the provider-approved usage limit. Configure the financial control in the relevant provider control plane and use gateway quotas for traffic management.
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