Last month, 48% of visitors to documentation sites across Mintlify were AI agents, not humans.

Claude Code, Cursor, and other coding agents are becoming the actual customers reading your docs. And they read everything.

This changes what good documentation means. Humans skim and forgive gaps. Agents methodically check every endpoint, read every guide, and compare you against alternatives with zero fatigue.

Your docs aren't just helping users anymore. They're your product's first interview with the machines deciding whether to recommend you.

That means: clear schema markup so agents can parse your content, real benchmarks instead of marketing fluff, open endpoints agents can actually test, and honest comparisons that emphasize strengths without hype.

Mintlify powers documentation for over 20,000 companies, reaching 100M+ people every year. We just raised a $45M Series B led by @a16z and @SalesforceVC to build the knowledge layer for the agent era.

Make Your Docs Agent-Ready

Every product shipping AI features eventually hits the same wall: three model providers, no unified cost tracking, a caching layer someone bolted on during a hackathon, and a failover story that amounts to "hope Anthropic stays up." The AI gateway the layer handling routing, retries, caching, rate limits, and cross-provider observability stops being optional somewhere around your third model integration. The real question is whether to build that layer or buy it, and the reflex to build, the one that feels like the senior engineering move, is usually the wrong instinct.

Building wins in a narrow set of conditions, mostly around scale and specificity. When your gateway sits on the critical path of millions of requests, a vendor's per-call pricing and extra network hop turn into real money and real latency; at that volume, sub-5ms overhead and caching logic tuned to your exact token economics justify a dedicated team. Building also wins when the gateway is the product if you sell AI infrastructure, you cannot outsource your core differentiator to a vendor and still call it a moat.

The other legitimate driver is control you simply cannot buy: air-gapped deployments, data residency that forces self-hosted models, or compliance constraints no vendor roadmap will move fast enough to satisfy. Those are architecture decisions, not preferences.

For everyone else, buying is the disciplined choice. Kong, Portkey, and LiteLLM already solved multi-model failover, retries, cost attribution, and observability the unglamorous plumbing that takes a team six to twelve months to reach parity on, then never stops demanding maintenance as providers churn their APIs. Every quarter spent rebuilding that plumbing is a quarter your engineers are not spending on the product that actually sets you apart.

The maintenance tail is what teams underestimate. Ten LLM providers means ten API contracts that drift, deprecate, and break on someone else's schedule. A vendor absorbs that churn as their core business; you absorb it as a permanent tax on a team you hired to build something else.

Check the boxes that apply, then tally the points positive favors BUILD, negative favors BUY.

Technical capability

☐ 6+ engineers with real AI infrastructure expertise (+3)

☐ Need sub-5ms gateway overhead to hit performance SLAs (+2)

☐ Require air-gapped or custom semantic caching logic (+3)

☐ Current traffic under 100 requests per second (−2)

Business context

☐ AI infrastructure is your core product differentiator (+3)

☐ Must be live in under three months (−3)

☐ Traffic exceeds 500 requests per second (+2)

☐ Prefer predictable OpEx over funding AI-infra hires (−2)

Organizational & risk

☐ Can commit 12+ months to reach vendor feature parity (+2)

☐ GDPR or data residency requires self-hosted models (+2)

☐ Need multi-model failover and observability immediately (−3)

☐ Can tolerate vendor lock-in for 18–24 months (−2)

Where you land:

Two costs never make it into the build-versus-buy spreadsheet. The first is opportunity cost: three engineers on a gateway for a year is roughly €180k of salary spent producing infrastructure a vendor would have rented for €3k a month. The second is the cost of getting a hard requirement wrong late discovering data-residency obligations after launch can force a $95k replatform that early scoping would have avoided outright.

Build when scale or strategy genuinely demands it. Buy when they don't, and spend the year you just saved on something your customers can actually see.