Run your own models in production, without burning cash on idle GPUs.

Sometimes, and it depends almost entirely on how steady your traffic is. A GPU is billed by the hour whether it serves one request or ten thousand, so self-hosting wins on sustained, predictable load and loses badly on spiky or low-volume workloads, where a per-token API is usually cheaper. The honest break-even sits somewhere in the middle, and it moves as models get cheaper. We model it on your actual traffic before recommending either — and we will tell you when the answer is to stay on a commercial API.

Start from the memory the model needs, not from the fastest card available. Model weights, KV cache, and concurrency all consume VRAM, and running out of it is the most common cause of production inference failures. Quantization can cut that requirement substantially, often with little measurable quality loss, which frequently moves a workload onto a much cheaper instance class. We size the card to the model and the concurrency you actually expect, then verify it under load rather than trusting the estimate.

Scale the capacity to zero when nothing is using it, and keep only what your latency budget genuinely requires warm. Idle GPU time is the single largest source of waste in self-hosted inference, and most teams discover they are paying around the clock for a workload that is busy a few hours a day. The trade-off is cold-start latency, which can be reduced with preloaded models, warm pools, and cached image layers. Where the workload cannot tolerate any cold start, spot and reserved capacity usually cut the bill instead.

Yes — that is one of the main reasons teams self-host. Running open-weight models on infrastructure you control means prompts and responses never leave your environment, which matters when contracts, GDPR, or sector rules make sending customer data to an external provider impossible. It also means you own the logging decision, including choosing not to log prompts at all. We deploy inference on private networking with no public GPU endpoints and set the retention policy deliberately rather than by default.

Usually time-to-first-token, not total generation speed. Users perceive a streamed response as fast if the first token arrives quickly, even when the full answer takes several seconds. Slow first tokens typically come from queueing under concurrency, cold starts, oversized prompts, or retrieval steps sitting in front of the model. We measure at p95 rather than the median, because the median hides exactly the requests that make people complain.

We operate the infrastructure that inference runs on — GPU capacity, serving, scaling, routing, retrieval pipelines, monitoring, security, and cost control. We do not train models, and we do not replace your product engineers. In practice we work alongside the team building the feature, owning everything from the endpoint down so they can stay focused on what the model is asked to do.

It depends on the workload, and the honest answer is that the choice matters less than the configuration. vLLM suits high-concurrency text generation with continuous batching; Ollama and llama.cpp suit small, low-traffic, or CPU-bound deployments; Triton suits mixed model types in one serving layer. We pick against your concurrency, model mix, and latency target, and expose an OpenAI-compatible endpoint either way so your application code is not tied to the decision.

The system should fail over to a commercial API rather than return errors to users. Spot capacity is cheap precisely because it can be reclaimed, so any serious self-hosted deployment needs a documented fallback path, request queueing that degrades gracefully under load, and alerting that fires before users notice. We build that path in and rehearse it, because a failover that has never been tested is not a failover.