The Build vs Buy Decision in 2026
API pricing for frontier models has dropped 80% over three years. At the same time, open-source models have closed much of the capability gap — creating a genuine decision point for organizations at volume.
The Math: API vs Self-Hosted
At 10 million tokens per day: OpenAI GPT-4o at $0.01/1K tokens = $36,500/year. A single A100 running Llama 4-17B at $3/hour = roughly $2/day. The crossover is typically 5-10 million tokens per day.
Hidden Costs of Self-Hosting
Add: engineering time ($150K+/year), reliability redundancy (doubles hardware costs), and ongoing model update management. True total cost of ownership is typically 3-4x the pure compute cost.
The Decision Framework
Self-hosting wins above 10M tokens/day with existing ML infrastructure. APIs win when volume is variable or unpredictable, or when you need the latest frontier capabilities immediately.
Frequently Asked Questions
How much does it cost to run an LLM in production?
GPT-4-class API costs run approximately $0.01-0.03 per 1K tokens in 2026. Self-hosting a 70B model on 4× A100 GPUs costs $7-12/hour on cloud. Quantization, batching, and caching reduce costs 3-10×.
What is vLLM?
vLLM is a high-throughput inference engine using PagedAttention to improve GPU memory efficiency, enabling 10-24× higher throughput compared to naive implementations — the de-facto standard for self-hosted LLM serving.
How do I reduce AI API costs?
Use smaller models for simple tasks, implement prompt caching, cache frequent identical queries, use batching for non-real-time workloads, and compress prompts. See our RAG pipeline guide for retrieval-based cost reduction.