Continuous AI Runtime Intelligence

Know what your AI stack should run next.

Continuously evaluate and optimize the best model, runtime, hardware, and deployment configuration for your real workloads.

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Workload-specific Infrastructure-neutral Continuously improving
evaluation complete
ConfigurationQualityp95ThroughputVRAMCost / 1M
Model A · 70BFP8 · vLLM · H10094.8%182ms1,240 tok/s71 GB$0.42
Model B · 72BINT8 · SGLang · L40S93.9%214ms986 tok/s43 GB$0.31
Model C · 40BBF16 · TensorRT-LLM · H10092.7%168ms1,380 tok/s82 GB$0.39
The AI stack is always movingNew modelsNew runtimesNew hardwareNew providers

A better optimization loop

Stop guessing which AI stack to run.

Generic benchmarks cannot represent your production workload. Skhemata gives teams evidence for the next decision—and keeps looking for a better one.

01 / Evaluate

Benchmark against reality

Evaluate models against the prompts, traces, tool calls, structured outputs, and workflows your application actually depends on.

02 / Optimize

Search the configuration space

Compare model, harness, quantization, runtime, hardware, and provider choices in one measurable loop.

03 / Validate

Catch regressions before release

Replay representative workflows and verify quality, correctness, performance, and operational constraints before deployment.

04 / Re-evaluate

Keep improving over time

Re-run proven evaluations as new models, engines, accelerators, and providers become available.

Provider-neutral by design

Your infrastructure stays yours.

Skhemata is the intelligence and control layer above your runtime infrastructure. Production inference can stay wherever your security, residency, and operating model require.

Any model. Any runtime. Any infrastructure.

Applications / AI workloads
SkhemataContinuous AI Runtime Intelligence
Evaluation Optimization Validation Runtime intelligence
Customer infrastructureBare metal · GPU clusters · Kubernetes · private cloud
Public cloudAWS · Azure · Google Cloud
GPU cloudsCoreWeave · RunPod · Modal
Inference platformsBaseten · Fireworks · Together · compatible providers

Customer-owned hardware is a first-class deployment target.

Core workflow

One continuous evidence loop.

From real workload behavior to a validated runtime decision, then back around as the ecosystem changes.

  1. 01
    Capture workloadPrompts · traces · tools
  2. 02
    Establish baselineCurrent production stack
  3. 03
    Test alternativesModels · runtimes · hardware
  4. 04
    Compare outcomesQuality · latency · cost
  5. 05
    ValidateBehavior · regressions
  6. 06
    DeployYour infrastructure
  7. 07
    Re-evaluateAs the ecosystem changes

Comparable evidence

Quality, performance, and cost—in context.

Review candidates against the same workload and success criteria, including tool-call behavior and infrastructure constraints.

evaluation complete
Customer AI workloadSupport workflowPrompts · traces · tools
evaluate
Best-fit configurationModel A · 70B
FP8vLLMH100
ConfigurationQualityp95ThroughputVRAMCost / 1M
Model A · 70BFP8 · vLLM · H10094.8%182ms1,240 tok/s71 GB$0.42
Model B · 72BINT8 · SGLang · L40S93.9%214ms986 tok/s43 GB$0.31
Model C · 40BBF16 · TensorRT-LLM · H10092.7%168ms1,380 tok/s82 GB$0.39

Continuous optimization

Your best configuration has a shelf life.

New model releases, quantization formats, inference engines, GPU hardware, and providers can be replayed against established evaluations—without turning every change into a research project.

01Models and model releasesEvaluate against your existing workload
02Quantization formatsEvaluate against your existing workload
03Inference enginesEvaluate against your existing workload
04GPU hardwareEvaluate against your existing workload
05Cloud and inference providersEvaluate against your existing workload

Ready to stop guessing?

Find the right runtime for your workload.

Bring your production criteria. Skhemata brings the continuous evaluation loop.

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