LLM Inference Serving Software Pricing 2026

LLM inference serving is the infrastructure that runs large language models in production to generate responses at low latency and high throughput. Serving platforms handle GPU scheduling, batching, KV-cache management, and autoscaling. They let you deploy open-weight models (like Llama or Mistral) behind an API without managing raw GPU clusters yourself.

Managed inference APIs typically charge per million input and output tokens, with prices varying by model size and provider. Self-hosting on dedicated GPUs is priced by GPU-hour, which can be cheaper at sustained high utilization but expensive if GPUs sit idle. Smaller open models cost dramatically less per token than large frontier models.

It depends on utilization. Per-token managed APIs are cheapest for bursty or low-volume workloads because you pay only for what you use. Renting dedicated GPUs becomes cheaper once your traffic is high and steady enough to keep the hardware busy. The crossover point is driven by your tokens-per-day and how well you can batch requests.

Watch for idle GPU time on reserved instances, cold-start latency and the over-provisioning needed to avoid it, data egress fees, and the engineering effort for quantization, batching, and autoscaling tuning. Output tokens usually cost more than input tokens, so long generations can quietly dominate the bill.