How a Degradation State Machine Keeps LLM Services Running After Token Quota Runs Out

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A software developer has outlined a practical approach to handling token quota exhaustion in large language model (LLM) services, moving beyond standard prevention strategies. The solution centers on a degradation state machine that transitions a service through four states — NORMAL, WARNING, DEGRADED, and EXHAUSTED — based on token usage thresholds. Rather than crashing when quotas run dry, the system routes requests to cached responses or returns controlled 503 errors depending on request priority. A response cache, keyed by prompt hash, is built up during the WARNING phase so critical functionality can continue even after the quota is fully consumed. The implementation, shared as part of MonkeyCode's product outreach, uses Python and is designed to be tunable for different traffic patterns and deployable on free-tier infrastructure.

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ProgrammingDEV Community ·

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