Consider task requirements (speed, accuracy, cost), whether to use APIs or self-host, model capabilities, and your latency and privacy requirements.
Selecting AI models involves balancing multiple factors based on your specific needs.
Task fit: different models excel at different things. GPT-4 and Claude are strong at reasoning and nuanced writing. Smaller models like GPT-3.5 or Claude Instant are faster and cheaper for simpler tasks. Specialized models exist for code, embeddings, image generation.
API vs self-hosted: APIs (OpenAI, Anthropic, Google) are easiest—no infrastructure, always latest models. Self-hosting open models (Llama, Mistral) gives you control, privacy, and potentially lower per-request costs at scale, but requires ML infrastructure expertise.
Cost modeling: calculate cost per request based on token usage. A chatbot handling millions of messages has different economics than an internal tool used occasionally. Smaller models or caching repeated queries can dramatically reduce costs.
Latency requirements: streaming responses feel faster for users. Smaller models respond quicker. Edge deployment reduces network latency. For real-time applications, response time might matter more than capability.
Privacy and compliance: sensitive data may require self-hosted models or specific providers with compliance certifications. Understand where data goes and how it's retained.
Evaluation: test models on your actual use cases before committing. What works in demos might fail on your edge cases. Build evaluation datasets representing real usage patterns.
Start with APIs for speed, establish what works, then optimize (smaller models, self-hosting, caching) based on actual usage patterns and costs.
Consider task requirements (speed, accuracy, cost), whether to use APIs or self-host, model capabilities, and your latency and privacy requirements.
Join our network of elite AI-native engineers.