Rate Limiting LLM API Calls: Tokens, Budgets, and Consumer Quotas
LLM traffic changes rate limit design. Account for token use, bursts, and separate budgets for each consumer.
Read moreAPI gateway engineering, LLM infrastructure, and production systems for AI.
LLM traffic changes rate limit design. Account for token use, bursts, and separate budgets for each consumer.
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Put a gateway before an MCP server with this practical guide. Configure authentication and rate limits through one routes.json file.
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API keys validate quickly, while JWTs include identity claims without a lookup. Choose based on callers and the activity you must track.
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Routing among providers improves reliability as well as cost. Explore priority failover, weighted traffic, and gateway choices based on latency.
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Rate limiting, auth, and routing are API contract problems. You need a gateway on day one, not after you scale.
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Logging every prompt creates compliance and cost risks. Capture enough for debugging and cost attribution without retaining sensitive model inputs.
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A new underlying model can change endpoint output. Version AI APIs so clients choose when to adopt model upgrades.
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Prompt injection targets models, but infrastructure can reduce exposure. Use gateway pattern matching to screen requests before they reach the model.
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MCP is more than an API format. Its session lifecycle affects how gateways manage connections to MCP servers.
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Fixed and sliding windows, token and leaky buckets, plus concurrent request limits address different needs. Learn where each pattern fits and fails.
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Set token budgets in the gateway beside authentication and rate limits. This guide shows how to define them as policy.
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With config-as-code, generate a developer portal from gateway configuration instead of maintaining a separate system.
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Other gateways we reviewed made configuration a UI concern. We put gateway state in Git with a routes.json-first architecture.
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