Every technical leader is facing this decision right now: do we build our own AI agent infrastructure, or use something off-the-shelf? The wrong answer wastes either months of engineering or years of competitive advantage.
The 5 Factors That Determine Build vs Buy
Differentiation
Build when
The agent IS the product or core IP
Buy when
The agent supports the product but isn't the differentiation
If a competitor using the same platform would have identical capabilities, you need to build. If agents are infrastructure (like your email provider), buy.
Timeline
Build when
6+ months acceptable
Buy when
Need output in days or weeks
A production-ready agent from scratch takes a serious engineer 4–8 weeks minimum. Buying gets you there in hours.
Volume
Build when
Very high volume with cost optimisation needs
Buy when
Medium volume where per-run cost is acceptable
At massive scale (millions of runs/month), the unit economics favour building. At sub-10k runs/month, buying is almost always cheaper total.
Customisation
Build when
Needs proprietary models, fine-tuning, or deeply custom logic
Buy when
Standard tasks well-served by existing agents
If you need to fine-tune on proprietary data or wire deeply into internal systems in novel ways, build. Standard tasks — research, writing, analysis — are well-served by platforms.
Team
Build when
Have ML engineers and LLM expertise in-house
Buy when
Team is generalists or focused on product
Building well requires people who understand prompt engineering, evaluation, reliability, and LLM cost optimisation. Most product teams don't have this yet.
The Hidden Cost of Building
Teams consistently underestimate the ongoing cost of maintaining LLM-based systems. Models change. Prompts degrade. New model versions break existing behaviour. Error rates need monitoring. Token costs need optimisation as models update their pricing.
A "simple" agent built in a weekend often requires a half-time engineer to maintain reliably in production. That's your real build cost.
The Middle Path: Buy First, Build Later
The best strategy for most teams is hybrid: start with a platform API to validate whether agent-powered features drive value for your users. Once you have proof of value and understand the exact requirements, then build the specific parts that need to be proprietary.
This is how the best tech companies approach infrastructure decisions generally — use commodity solutions for commodity problems, build only where differentiation matters.
Start with our API. Build what's proprietary later.
Full B2B API access. 100 free credits. Up and running today.