PrimaLabs joins NVIDIA and U.S. national laboratories on the DOE Genesis Mission Read the announcement
Application-specific adaptive inference
PrimaLabs builds and operates a dedicated inference stack around each workload. Open models, served on sovereign terms, tuned continuously in production. National lab grade adaptive AI, now for enterprise.
Live request traffic arrives in the rhythm of the selected workload, enters a circuit built from the PrimaLabs dot-trace mark, and leaves as a single, steady, optimized stream. Selecting a workload above changes the arriving traffic pattern and the circuit adapts around it.
Your data, models, and IP stay yours. Never stored, never training anyone else's product.
A stack engineered around your unique workloads, tuned continuously in production.
Securely on your infrastructure: US-based, private cloud, or fully on-prem.
Performance, cost, and evolution stay in your hands, never a provider's roadmap.
The comparison
Shared endpoints are tuned once for a public benchmark, then frozen for every customer. Every product on them pays for that compromise in speed. A stack engineered around one workload starts faster and keeps improving as traffic grows.
Ownership
Protect your data, models, and IP. Optimize for your unique workloads, deploy securely on your infrastructure, and keep control of performance, cost, and evolution.
Models
Open weights, portable by design. The models can move, so the business is never hostage to a provider's pricing or roadmap.
Data
Zero data retention, end to end. Prompts, outputs, and the proprietary workflow around them are never stored and never train anyone else's product.
Deployment
Serverless, dedicated, private cloud, or fully on-prem. The stack runs where the business and its regulators require, and moves when they do.
Sovereign by design. Control performance, cost, and evolution. Live in minutes: same models, same SLA, no code changes.
One stack, rendered in four places
Why it compounds
The stack watches real traffic, learns what this specific workload needs, and ships an improvement only when it measures faster with identical outputs. Generic endpoints are tuned once. This one never stops.
Reads the live workload in real time, so every decision comes from real traffic, not a synthetic test.
Carries every win forward. The stack gets faster with use, without ever retaining the data it learns from.
Ships changes under the SLA, without touching the model or the product. Nothing reaches production on a hunch.
Generic endpoint
Flat. Tuned once, then frozen
PrimaLabs custom stack
Compounds. The stack learns the workload and improves with volume
Built for AI-native workloads
Agents fire thousands of near-identical requests in tight loops. The stack is built to answer them instantly, so the agent feels native, not throttled.
Multi-step agents chain tool calls, plans, and long contexts. The stack keeps every step fast and reuses what the workload repeats, so the whole chain finishes sooner.
When the model is the product, model speed is the product experience. The stack holds interactive latency through bursts and scale.
Proof
The workload runs on the current provider and on the PrimaLabs stack, side by side. Throughput, latency, and cost are measured and put in writing before any commitment. Lower cost is what the numbers produce.
Head-to-head evaluation
Measured on the customer workload
For two decades, the national labs perfected one discipline: extracting maximum useful work from fixed hardware under hard power and thermal limits. That is exactly the problem token cost now faces. PrimaLabs is that discipline applied, by the team behind ORBIT and the largest AI models ever trained on Frontier, the world's first exascale supercomputer.
Company news, partnerships, and inference engineering notes from the PrimaLabs team.
Book a demo
Share the workload profile: traffic shape, models in use, latency target, and deployment constraints. PrimaLabs runs the head-to-head and returns the numbers.
