Exa

Exa has carved a specific niche: web search that speaks the language of AI agents. Most search APIs return links and snippets; Exa returns structured data, highlights, and even full reasoning runs. That's a meaningful difference when your model needs to act on the results, not just display them. The 90% token reduction on highlights is the feature we'd reach for first—it directly cuts your LLM costs, which often exceed API fees. In practice, we'd pick Exa for coding agents (it powers Devin), lead enrichment (HubSpot uses it), and any agent that needs real-time facts. The sub-180ms Instant Search keeps agent loops snappy, while Deep Search handles the heavy lifting when you need citations and multi-step research. Where Exa bites: pricing is usage-based, and costs can scale if you're not careful with Deep Search or high-volume requests. The free tier ($20 credit upfront, $10 monthly) is enough for prototyping, but production workloads will hit the meter quickly. Also, the legacy /research endpoint and fields are deprecated as of April 2026—you must migrate to Deep Search, which is more powerful but pricier. Compared to alternatives: Perplexity's API is fine for consumer-grade answers but lacks Exa's structured outputs and latency guarantees. Brave's API is cheaper for simple web queries but doesn't offer the same agent-centric features. If you need raw HTML crawling at massive scale, you might pair Exa with a dedicated crawler, but for most agent use cases Exa is the complete package. We'd watch the index scale trajectory—Exa claims 1.4T URLs tracked and 80B documents served, on track to exceed Google-scale by 2027—so reliability should improve. Bottom line: Exa is the right choice if your AI agent's value depends on current, structured web data. It's not for static