The original title is "From MVP to Enterprise: Architecting AI APIs That Don't Fail at 3AM"

Short summary

Technical guide contrasting startup vs enterprise AI API architecture using model routing with fallback logic. Startup patterns prioritize p99 latency and token cost (97.5% savings vs GPT-4o); enterprise tier adds SLAs, custom DPAs, and dedicated support. Includes production Python code and real pricing data.

• •Model routing with fallback logic reduces costs 97.5% vs direct GPT-4o calls for commodity tasks
• •Startup architecture needs: cheap models, low-friction provider switching; enterprise needs: 99.9% SLA, custom DPA, Net-30 invoicing
• •Unified API endpoint reduces engineering overhead vs integrating OpenAI, DeepSeek, Qwen separately

Generated with AI, which can make mistakes.