Inference economics, reimagined.
Metrale is the inference economics platform for the GPUs you already own. It runs your models faster on the same silicon, keeps every prompt inside your perimeter, and shows your CFO what each workload costs.
Modeled at 70% or less of your current inference spend when the silicon is yours. Run the model →
Metrale Console. Demo data, recorded from the product mockup. Watch the one minute film →
Built by people who have stood up operations for
United StatesCyber Command
Naval SpecialWarfare Command
Prior roles of the founding team and core contributors. Listed for background, not as customers or endorsements. The appearance of U.S. Department of Defense visual information does not imply or constitute DoD endorsement.
Programs and partners
Program member. DGX Spark hardware provided.
Strix Halo hardware provided. MLPerf submitted on it.
Named contributor to the MLPerf edge agentic benchmark.
Fused Qwen GDN kernel merged into Transformers.
Dev Ambassadors. A recipe for every release.
SCALE by Spectral Compute One CUDA source, NVIDIA and AMD.The problem
Every inference engine gives you operator statistics. Every FinOps tool gives you a bill. Nothing connects the two, so the fastest engine in the rack still cannot say what a workload cost, which cluster stranded capacity, or whether last quarter’s upgrade paid for itself.
Every unit of work is tied to the workload, model, runtime, configuration, GPU and cluster that produced it. An operator number becomes a finance number without a spreadsheet in between.
The platform
Inference layer
Runs your models faster on the same silicon.
A compiled inference stack in Rust and CUDA. Hand tuned kernels per hardware, model and quantization, speculative decoding, prefix caching and expert parallelism across nodes. OpenAI and Anthropic compatible APIs from one 75 MB binary.
Explore Metrale Engine →Governance and control plane
Deploys, governs and repairs the fleet.
Signed recipes, release channels, canary rollouts that roll back on telemetry, GPU aware routing, autoscaling and node repair. Runs in your cloud account, on your metal, or air gapped. Never on the inference path.
Explore Metrale Control →Economics layer
Turns telemetry into accountability.
Cost per million tokens, cost per successful workload, productive GPU hours and stranded capacity, by model, cluster and business unit, measured against the baseline you ran before.
Explore Metrale Economics →Metrale began as local inference on a DGX Spark. The same pinned stack scales to datacenter GPU fleets with the qualification record, routing and observability that enterprises need in production.
The value
Measured on the box we can put our hands on. Modeled where the box is yours, with the inputs on the page.
the throughput of the matched vLLM configuration at C=128, same GB10, same checkpoint, same client.
Read the ladder →about 75 MB of Rust and CUDA. No Python or PyTorch in the request path. Signed, gated and replayable.
See the posture →to pay back the license on a 256 GPU fleet at a 1.20x uplift. Every month after is upside. Edit the inputs yourself.
Open the model →Proof, not a pitch
We publish the concurrency ladder against the matched vLLM configuration from C=1 to C=128, with every rung we lost on the way to it. The margin is widest at the top, which is where fleets of tool calling agents actually run.
unsloth/Qwen3.8-27B-NVFP4 · NVIDIA GB10 Grace Blackwell, 121.7 GB unified · mean tok/s over 3 timed reps (1 warmup discarded). ISL 128 / OSL 1,024, temperature 0. Every rung in the campaign log.
The console
Recorded from the product mockup with demo data. The shipped product will differ. The workflow will not.
Choose the model, choose the compute grid it runs on, and start getting answers from an air gapped fleet with the tokens per second and the cost per request on the same screen.
See it on your workload → Launch encrypted links to the grids you lease offsite, then queue a large project across all of them. It is sized, priced against your baseline, and placed only where its data class allows.
See it on your workload →Nodes report health, kernel versions and utilization. Canary a release to five percent, watch TTFT, roll back on a breach without waking anyone up.
See it on your workload →Chargeback by business unit, productive GPU hours, stranded capacity and the payback clock, against the baseline you ran before Metrale.
See it on your workload →Data residency, model allowlists and redaction as policy. Every response traces to a signed recipe, a kernel build and a gate record.
See it on your workload →Recognition
Every card links to the primary source.
July 2026 July 2026 July 2026 Member Why Metrale
The market is loud. Every runtime claims speed and every dashboard claims visibility. Here is what is actually different, and the question to put to anyone else.
Metrale compiles a kernel set per hardware, model and quantization instead of shipping one generic path. On the published GB10 ladder it wins every rung against the matched vLLM configuration and keeps scaling from C=64 to C=128 while the baseline flattens.
Ask for the throughput curve at C=128 on your workload, not a single stream number on theirs.
Go deeper on speed →One signed Rust binary with no Python or PyTorch in the request path. Prompts, weights and telemetry stay on hardware you own, in your cloud account, or on an air gapped network. The control plane never sits on the inference path.
Ask what is in the request path, and who audits the two hundred dependencies behind it.
Go deeper on security →Every response traces to a signed recipe, a kernel build and a gate record. Every GPU hour is attributed to a model, a cluster and a business unit. Governance is a ledger finance can sign, not a dashboard operators tolerate.
Ask what a workload cost last Tuesday, by business unit. A tokens per second chart is not an answer.
Go deeper on governance →Only Metrale
Nobody installs a governance layer for its own sake. Metrale gets into the fleet by making the same GPUs produce more inference.
Once the engine owns the request path, every kernel, cache and queue decision is observed at the source instead of inferred from a proxy.
With source data in hand, cost per workload, stranded capacity and payback stop being estimates and become a ledger.
One architectural decision, made at the start. Own the request path, then measure what it is worth. Everything on this page is downstream of that.
Getting there
Metrale runs beside vLLM, SGLang or llama.cpp on day one. Nothing is turned off and nothing is migrated until your own numbers say so.
Week one
One binary per node, one signed recipe per model, next to what you run today. A side by side ladder against your current engine, on your hardware, in week one. The economics baseline starts recording the same day.
Months one to six
Production traffic shifts one model family at a time. The control plane takes over rollout, canary, rollback and fleet policy. Economics reports every cluster against what it cost before, by business unit.
At renewal
Some customers keep the old engine for one model family. Others move off it entirely. You make that call having run both, on your own receipts rather than a vendor timeline.
From the fleet
“Night and day compared to the 10 minute torch.compile cycle. Startup in about 15 seconds and it just stays coherent in an agentic loop.”ronald_15496, Discord, #general
“Testing Atlas on a DGX Spark in an agentic workflow for over an hour. Super impressed. Spark is actually awesome with Atlas.”PersonWhoThinks, r/LocalLLaMA
“I had grown tired of the usual stack and was hoping for something like this. Really surprised and impressed. So glad I bought a Spark.”tetsuro59, Discord, #general
Quotes are verbatim. Atlas was the engine's name until the September 2026 rebrand to Metrale.
Questions
Short answers. Each one is backed by something on this site or in the repository.
Metrale is an inference economics platform for GPUs you own or rent by the hour. Metrale Engine runs open models faster on the same silicon, Metrale Control deploys and governs the fleet, and Metrale Economics turns the telemetry into cost per workload, chargeback and payback. The engine is open source under AGPL-3.0. The platform is licensed per GPU.
No. Metrale deploys beside your current engine and takes traffic one model family at a time. Many teams keep the old engine for a family we do not ship a recipe for yet. You decide what moves, on your own side by side numbers.
One binary per node and one signed recipe per model. A single box runs in minutes from one install command. A fleet pilot has a side by side ladder against your current engine in week one. Production cutover is workload by workload over the following weeks, at your pace.
You do. Prompts, weights, outputs and telemetry stay on hardware you own, in your cloud account, or on an air gapped network. The control plane manages configuration, licensing, versions and aggregate metrics, and it never sits on the request path. In a bring your own cloud deployment no inference request leaves your account.
NVIDIA DGX Spark (GB10) is verified today, and AMD Strix Halo (gfx1151) runs the same CUDA source compiled through SCALE, with both submitted to MLPerf Inference v6.1. Hopper and Blackwell datacenter targets are in active bring up with receipts in the changelog. Expert parallelism across two nodes ships as recipes and a three node topology is being wired up.
Yes. The engine is one binary with no runtime download and no Python environment to resolve. Recipes, models and kernels are delivered as signed artifacts and installed from local media. Telemetry can stay entirely inside the network and export on your schedule, or never.
Every model on this site maps to a recipe in the atlas-recipes repository, which is the single source of truth, so the site cannot list a model without one. Qwen leads with the most recipes, alongside Gemma, Nemotron, Mistral, MiniMax and DeepSeek. Bring your own weights and we scope the bring up.
An image ships only after the serve matrix passes. Every model boots, stays coherent under greedy determinism with no token leakage and reliable tool calls, and holds throughput within ten percent of its committed baseline. A release that ships slower than its baseline fails the gate. Every number on the benchmarks page is generated from a record in the repository.
Per GPU per year for the Enterprise Edition, with volume tiers as the fleet grows, and a per box license for workstation and edge deployments. Support and forward deployed engineering are priced separately. The Community Edition is free under AGPL-3.0. The pricing page lists the proposed sheet and a payback model with editable inputs.
Because agentic systems do not send one request at a time. A fleet of tool calling agents sharing a context bus arrives as many concurrent streams, so an engine is judged where the requests pile up. On the published ladder Metrale keeps gaining throughput from C=64 to C=128 while the matched vLLM configuration does not, and an engine that flattens under load caps how many agents a box can run.
Ask the rest in a working session, or read the deployment guide ↗.
Next step
Book a working session. We run the ladder on your workload, on your hardware, and hand you the receipt.