Startup Guide to AI APIs: What Founders Need to Know in 2025
Building a product with AI integration in 2025? Here are the decisions that matter most — and the ones founders get wrong.
Choosing Your AI Foundation
API vs Fine-Tuning
Start with the API. Fine-tuning requires: labeled training data (expensive to create), significant upfront cost, and ongoing maintenance as base models improve. Fine-tune only when the base model demonstrably cannot do what you need — which is rare in 2025.
Model Selection for Startups
Recommendation: Start with Claude Haiku ($0.25/1M tokens input) for development and testing. Switch to Claude Sonnet ($3/1M tokens) for production quality. Use Opus only when Sonnet demonstrably cannot handle the task.
The 12x cost difference between Haiku and Sonnet is enormous at scale. Test rigorously before committing to the more expensive model.
Prompt Engineering for Products
The System Prompt is Your Product
Your system prompt encodes your product logic. Treat it like code: version control it, test changes rigorously, and document the reasoning behind decisions. A 5% improvement in your system prompt can outperform model switching.
Prompt Caching for Cost Reduction
Anthropic supports prompt caching for system prompts. If your system prompt is 10,000 tokens, cache reads cost 10x less than fresh generation. For products with many requests, this alone can reduce costs 40-60%.
Common Startup AI Mistakes
- Over-engineering the AI layer before validating the use case
- Ignoring latency — users notice when responses take 10+ seconds
- Not implementing proper error handling for AI failures
- Assuming AI output is always correct — build verification layers
- Not setting max_tokens — pay for responses you will truncate
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