Model Serving & Deployment
At some point the API bill, the latency budget, or the data-residency clause forces the question: should we serve models ourselves? It's a genuine engineering trade — self-hosting buys control, privacy, and unit economics at scale, and costs you an inference platform you now have to run well. Most teams get the decision wrong in one direction or the other.
We've built the platform enough times to make it boring: vLLM and friends tuned for throughput, GPU autoscaling that tracks real traffic instead of burning idle capacity, cold-start engineering so scale-from-zero doesn't mean minutes of silence, canary rollouts for new model versions with eval gates, and routing layers that blend self-hosted and API models by cost, latency, and capability. Inference as infrastructure — measured, governed, affordable.
Self-hosted inference
Open models on vLLM with quantization and batching tuned to your latency and throughput targets.
GPU autoscaling
Serving fleets that scale with traffic — including from zero — without paying for idle silicon.
Model routing
Requests routed across self-hosted and API models by cost, capability, and data sensitivity.
Canary model rollouts
New versions proven on shadow and partial traffic, with eval gates, before full cutover.
Inference economics
Cost per thousand requests, engineered down with batching, caching, and right-sized hardware.
Self-host or use model APIs?
APIs until the math or the compliance boundary says otherwise. High steady volume, strict data residency, or latency floors favor self-hosting; spiky traffic and frontier-capability needs favor APIs. Most mature setups are hybrid, routed per workload — we build the router along with the platform.
What does self-hosting actually cost?
GPU compute plus the part everyone forgets: engineering to keep utilization high. An idle A100 fleet erases the API savings fast. We model your traffic honestly — including the utilization you'll really achieve — before recommending the move, and we've told plenty of clients to stay on APIs.
Can smaller open models really replace frontier APIs?
For scoped tasks — classification, extraction, routing, domain Q&A — routinely, especially fine-tuned. For open-ended reasoning, less often. Evals on your workload answer it definitively, which beats both the hype and the skepticism.
Put Model Serving & Deployment to work — in production.
One forward-deployed engineer, embedded in your stack, owning the outcome from discovery to production. Weeks, not quarters.
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