Introduction
The choice between calling a proprietary model's API and self-hosting an open source model shapes cost structure, control, and engineering overhead in ways that aren't obvious until a team has committed to one path.
The Case for Proprietary APIs
Hosted APIs from providers like Anthropic and OpenAI require no infrastructure management, benefit from continuous model improvements without any work on the team's part, and typically offer the strongest raw capability for complex tasks. For most startups, this is the pragmatic default — it lets the team focus engineering time on the product, not on model infrastructure.
The Case for Open Source Models
Self-hosted open source models make sense when data sensitivity rules out sending information to a third-party API, when usage volume is high enough that hosting costs undercut API pricing at scale, or when a narrow, well-defined task can be handled by a smaller, fine-tuned model without needing frontier-level capability.
Cost Isn't as Simple as It Looks
Self-hosting isn't free — it requires GPU infrastructure, ongoing maintenance, and engineering time to keep the serving stack running reliably. The breakeven point versus API costs is usually higher in practice than founders initially estimate, particularly once reliability and uptime engineering is accounted for.
Making the Decision
Start with proprietary APIs unless there's a specific, concrete reason not to — a hard compliance requirement, a proven cost problem at scale, or a narrow task that a smaller open model handles well enough. Revisit the decision once real usage data exists, not based on a theoretical cost projection.
Conclusion
For the vast majority of startups, proprietary APIs are the right starting point. Open source self-hosting is a scaling optimization to revisit later, not a default architecture decision to make on day one.
Frequently Asked Questions
Should a startup self-host an open source LLM to save money?+
Usually not initially — self-hosting requires GPU infrastructure and ongoing engineering time that often costs more in practice than the API savings, until usage volume is genuinely high.
When does self-hosting an open source model make sense?+
When data sensitivity rules out sending information to a third-party API, when usage volume is high enough to undercut API pricing at scale, or when a narrow task can be handled by a smaller fine-tuned model.
Is a proprietary API riskier for a startup long-term?+
Not particularly — most startups benefit more from proprietary APIs' continuous improvements and zero infrastructure burden than they would from the control self-hosting offers, at least until a specific, concrete reason to switch emerges.