Models that stay up under load.
We deploy and serve AI models at production scale, sub second latency, multi region failover, autoscaling that doesn't waste GPU hours, and the SLOs your product team can actually commit to.
Production serving
done right.
A demo on a single GPU is a Tuesday. Serving the same model at 10K RPS, p99 under 500ms, with rolling deploys and rollback, that's the actual job.
Low Latency Inference
Quantization, speculative decoding, KV cache optimization, and token streaming, squeezing every millisecond out of the serving path.
Multi Region Deployment
Active active across regions, with health aware routing and failover that doesn't drop in flight conversations.
Autoscaling Done Right
Predictive autoscaling on real signals, request queue depth, token throughput, GPU utilization, not just CPU.
Smart Model Routing
Dynamic routing across model tiers, frontier model for hard queries, cheaper models for the rest, saving cost without losing quality.
Edge & On Device
For ultra low latency or sovereign use cases, model deployment at the edge, on customer premises, or on device.
Safe Rollouts
Canary deploys, shadow traffic, blue green cutovers, and instant rollback, model updates that don't take down production.
When serving becomes the problem.
Nobody budgets for serving until it hurts. The model worked, the demo landed, and then real traffic arrived. These are the versions of that call we hear most.
The Latency Budget Product Cannot Hit
Voice agents, live assistants, and interactive features have a physics problem: the user is waiting. We tune serving paths to p95 budgets with token streaming, speculative decoding, and quantization, and the most demanding consumer of this work is our own speech AI practice, where the entire round trip has half a second to happen.
The GPU Bill From Naive Serving
Real production teams run 40 to 65 percent GPU utilization, and unoptimized deployments run far worse: single-request serving, idle capacity provisioned for peak, and frontier models answering questions a model a tenth the size handles fine. Fixing the serving layer is usually the single largest line in an AI cost optimization engagement, and it requires no retraining and no product change.
Serving Tuned and Custom Models
API providers serve their models; yours are your problem. We productionize fine-tuned models and custom builds, including multi-adapter serving where dozens of LoRA behaviors ride one base model and get hot-swapped per request, the architecture that makes per-customer AI economically sane.
Sovereign, On-Prem and Edge Serving
When the model must live inside your perimeter, the serving stack comes with it: full inference infrastructure on sovereign or air-gapped deployments, and edge or on-device serving where the latency budget or the data cannot cross a network at all.
Agent Workloads That Multiply Everything
Agentic systems change the serving math: one user action fans out into planning calls, tool calls, and verification calls, multiplying token volume several-fold over chat. Serving for agents means designing for that fan-out, burst concurrency, long contexts, and routing, before the traffic arrives, because an agent product discovering its serving limits in production discovers them all at once.
Capacity-Plan Against 50 Percent, Not 90.
Vendor break-even models assume near-full GPU utilization; real teams sustain 40 to 65 percent because traffic is peaky and batching has limits. Every sizing decision we make starts from your measured workload profile, not an optimistic spreadsheet, which is why our first stage is profiling and why our capacity recommendations sometimes disappoint hardware vendors.
Routing Is the Biggest Lever on the Board.
Most traffic does not need the biggest model. Dynamic routing, frontier models for the hard 5 to 15 percent of queries, small fast models for the rest, cuts blended inference cost 60 to 80 percent in typical mixed workloads while holding quality where users can see it. It is the highest-ROI change in serving, and it is pure architecture.
From notebook to battle tested infra.
We treat model serving like any other production system, with load testing, observability, and on call rotations from day one.
Workload Profiling
We characterize your traffic, request size, peakiness, latency budget, so the architecture matches reality, not a hypothesis.
Stack Selection
vLLM, TGI, Triton, SGLang, we pick the serving stack that maps cleanly to your model, hardware, and operational team.
Load & Chaos Testing
Synthetic load that matches your worst case real traffic. We break the system on staging so it doesn't break in production.
Production & SLOs
Cutover with monitoring on latency, availability, and quality SLOs, and on call playbooks your team can run alone.
Serving economics, sizing reality, and the operations bill.
Serving is where AI budgets actually live: inference is roughly 80 percent of production AI spend. Here are the numbers that decide it.
Anchor numbers for 2026: H100-class GPUs rent for roughly $2.50 to $3.50 an hour on specialist clouds, meaningfully more on hyperscalers, and a well-optimized single H100 serving a 70B-class model handles about 30 to 50 concurrent users at sub-2-second latency. Self-hosted 70B inference lands around a cent-and-a-half per thousand tokens all-in, typically 60 to 85 percent below equivalent API pricing at sustained volume. The qualifier that keeps those savings honest: they exist above roughly 500 million to a billion tokens a month of steady load. Below that, APIs win and we say so.
The utilization number decides it. Against hyperscaler on-demand pricing, owned hardware breaks even around 50 to 83 percent sustained utilization. Against specialist GPU clouds, cloud often wins even near full utilization. And real production teams sustain 40 to 65 percent, which is why our default recommendation is specialist cloud GPUs for most workloads, owned hardware only for sustained high-volume or sovereignty mandates, and hyperscalers when your enterprise agreements or data gravity make them free-ish in practice. The serving stack we build is portable across all three, deployed through the same cloud and DevOps foundation as the rest of your infrastructure, so the hardware decision stays reversible.
Order-of-magnitude gains are normal, which surprises buyers who assume the model is the bottleneck. Continuous batching alone delivers several-fold to order-of-magnitude throughput gains over naive request-at-a-time serving. Quantization cuts memory footprint two- to four-fold, letting bigger models fit smaller hardware or the same hardware serve more. Speculative decoding cuts latency two- to three-fold on suitable workloads. Stacked, the same GPUs routinely serve five to ten times the traffic they did before we arrived, which is why we profile and optimize before anyone is allowed to buy more hardware.
Fewer than the vendor sizing sheet says, usually. Sizing follows from four measured numbers, request rate, token lengths in and out, peak-to-average ratio, and your latency budget, not from model size alone. A 7B-class model serves hundreds of concurrent users per GPU; a 70B serves tens; and routing shifts most traffic to the small end of that range. Our workload profiling stage exists to replace the sizing guess with those four measurements, and the answer it produces is frequently half the hardware the initial plan assumed.
The honest staffing line: plan 0.5 to 1 FTE per production GPU cluster for driver and firmware churn, hardware failures, stack upgrades, and on-call, and that person is often the largest cost in a self-hosted deployment. Our handover ships runbooks, alerting, and playbooks your platform team can operate alone, and for teams that do not want the pager, we run the serving layer on retainer. What we do not do is hand over a system whose operating cost was never mentioned, because a serving stack without an owner degrades on a schedule you can predict.
You own everything, and the stack is deliberately built on open, portable components, vLLM, Triton, and their peers, running in your accounts, defined as code in your repositories. Serving is a layer where proprietary platforms quietly charge rent forever, and the exit cost only becomes visible when you try to leave. Ours is designed for the opposite test: any competent platform engineer could take the repo and run it tomorrow, and the configs move between clouds and on-prem without a rewrite. We keep the retainer by answering pages fast, not by holding the keys.
Send the traffic profile, or the invoice that made you look for this page. We will tell you what the same workload should cost, and profiling it is free.
Questions about
Model Deployment & Serving
vLLM, Triton, TGI, SGLang for LLMs; ONNX Runtime, TorchServe, BentoML for traditional ML. We pick based on the model, hardware, and your team's familiarity.
Yes. Quantization, KV cache reuse, prefix caching, dynamic batching, and speculative decoding can deliver 2/5x throughput improvements without touching weights.
AWS, GCP, Azure, Oracle, and bare metal on prem. NVIDIA H100 / A100 / L40S, plus AMD MI300 and inference optimized CPUs where the workload allows.
99.95% availability and sub 500ms p95 are routine for most LLM workloads. Sub second voice and sub 100ms classification are achievable on right sized hardware.
Shadow traffic, canary, blue green, with eval gates between each stage. Every deploy can be rolled back in seconds, and we test rollback before launch.
Stop experimenting.
Start deploying AI that works.
Book a free discovery call. Share your traffic profile and SLO targets, we'll tell you what's possible and what it costs.
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