How it works

From workload evidence to a validated decision.

A repeatable process for engineering leaders who need confidence and platform teams who need the underlying measurements.

The evaluation path

Eight steps. One comparable scorecard.

  1. 01

    Connect Your Workload

    Bring representative prompts, production traces, structured outputs, tool calls, and end-to-end workflows through controlled, permissioned inputs.

  2. 02

    Define Success Criteria

    Set the quality, workflow, latency, throughput, memory, cost, and data-location requirements that make a candidate viable.

  3. 03

    Establish Production Baseline

    Measure the current model, harness, runtime, and infrastructure configuration using the same evaluation set and scorecard.

  4. 04

    Evaluate Model Candidates

    Test compatible models and model sizes against application behavior—not leaderboard scores in isolation.

  5. 05

    Optimize Runtime Configurations

    Explore supported quantization, inference engines, runtime parameters, hardware, and provider combinations.

  6. 06

    Validate Regressions

    Replay workflows to detect changes in answer quality, structured output, tool use, stopping behavior, reliability, and performance.

  7. 07

    Deploy the Selected Configuration

    Export a validated decision to the infrastructure you control. Skhemata does not require production traffic to be hosted by Skhemata.

  8. 08

    Continuously Evaluate Alternatives

    Re-run the evidence loop when new models, releases, runtimes, formats, GPUs, and providers become available.

Define success first

Optimize for the outcome your application needs.

A configuration is only better if it improves the metrics you value without violating hard constraints. Skhemata keeps those criteria attached to every comparison.

QualityWorkflow successTool-call correctnessLatencyThroughputVRAMInfrastructure costData-location requirements

Built for decision-makers

One evidence loop. Three perspectives.

Give engineering leaders, ML and platform teams, and release owners a shared view of what changed, what improved, and why the next configuration is worth adopting.

For engineering leadership

See defensible tradeoffs between quality, experience, infrastructure cost, operational control, and time to adopt.

For ML and platform engineers

Inspect comparable workload runs, runtime parameters, resource profiles, failure modes, and regression evidence.

For the next release

Reuse evaluation assets instead of restarting model and infrastructure research whenever the ecosystem changes.

Ready to stop guessing?

Turn runtime selection into a system.

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

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