How to Architect AI APIs for Reliability From Startup to Enterprise Scale

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A software architect with experience building LLM-backed services for both early-stage startups and Fortune 500 companies outlines why AI integration strategies must differ based on risk tolerance and scale. For startups, direct provider integrations with multiple AI vendors can consume significant engineering time on payment and verification infrastructure before any product features ship. The author recommends unified API gateways that support hundreds of models under a single key and payment method, reducing overhead and enabling easier model switching. For enterprise deployments, requirements shift toward contractual SLAs, multi-region failover, and formal support escalation paths. Key metrics to monitor at any scale include p99 latency, token cost per active user, and provider error rates rather than average response times.

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Insufficient source content to generate a reliable summary

The provided article text contains no substantive information beyond a GitHub repository link and brief description. No verifiable facts about OLAP, OLTP, or DuckDB were present in the supplied content. A meaningful and accurate summary cannot be written without fabricating details. Please provide the full article text for proper summarisation.

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