AI / ML

Over 70% of new SaaS products launched in 2026 integrate at least one AI API, making this the fastest-growing category by developer adoption. The market has stratified into three tiers: foundational model providers (OpenAI, Anthropic, Google Gemini) offering general-purpose language and multimodal models; specialized providers focusing on vision, speech-to-text, or embedding generation; and inference platforms like Fireworks AI and Together AI that host open-weight models with optimized serving infrastructure. Token pricing dropped roughly 40% year-over-year through 2025 and continues to fall as competition intensifies, but cost at scale still varies by 5-10x depending on model size, provider, and whether you use batch or real-time endpoints. Agentic workflows — where AI models call tools, browse the web, and execute multi-step tasks — have moved from experimental to production, making function-calling reliability and structured output support critical evaluation criteria. Context window sizes now range from 32K to over 1M tokens across providers, but effective retrieval within those windows varies significantly. When choosing an AI API, run benchmarks on your actual data rather than relying on leaderboard scores. Measure latency at your expected concurrency, test rate limit behavior under burst traffic, and verify data retention policies — some providers train on API inputs by default unless you opt out. For latency-sensitive applications, consider providers that offer regional endpoint deployment or edge inference. The emergence of model routers and gateway APIs (like LiteLLM and Portkey) lets teams abstract across multiple providers with fallback logic, reducing single-vendor risk.