0%0%Renting frontier-model APIs is the right start — but at scale, or under data-residency rules, you need your own serving stack: GPUs that aren't idle half the day, Kubernetes that autoscales with traffic, and open-source models served fast with vLLM. Dezvo designs and runs AI infrastructure that matches the workload instead of the hype.
Most products should start on hosted APIs (Anthropic, OpenAI, Google): zero infrastructure, frontier quality, pay-per-token. Self-hosting open-source models on your own GPUs wins in three cases — sustained high volume where per-token API costs exceed hardware costs, data-residency or privacy rules that forbid external APIs, and latency-critical or fine-tuned workloads that need dedicated serving.
The mistake is treating this as ideology. It's arithmetic: tokens per day, GPU utilisation, and ops headcount. Dezvo runs that arithmetic with you first — and about half the time we recommend staying on APIs with better routing and caching instead of buying GPUs.
Right-sized GPU selection (L4 to H100), spot and reserved capacity strategy, and utilisation monitoring — because idle GPUs are the most expensive kind.
AWS, GCP, Azure, or cost-effective providers like Hetzner — VPC isolation, IAM, and networking designed with the same rigour as your app infra.
Containerised model serving with GPU scheduling, autoscaling on queue depth, health checks, and zero-downtime model swaps.
Open-source models (Llama, Mistral, Qwen) served via vLLM or TGI with continuous batching, quantisation where quality allows, and OpenAI-compatible endpoints your code already speaks.