Workload Evaluation
Use customer-specific prompts, traces, structured output, tool calls, and multi-step workflows—not generic benchmarks alone.
PromptsTracesTool callsWorkflows
Features
Runtime decisions built on your workload.
Skhemata continuously evaluates the model-to-infrastructure stack, giving ML and platform teams comparable evidence without taking ownership of production inference.
Capabilities
Intelligence across the entire runtime configuration.
Define what success means once, then compare viable alternatives across every layer that affects the outcome.
Model Intelligence
Compare compatible open-weight models and sizes according to the quality, behavior, and operating profile your application requires.
Open weightsModel sizesTask fit
Harness Validation
Measure prompting, tool selection, function arguments, structured output, retries, stopping behavior, and workflow completion.
CorrectnessRetriesCompletion
Runtime Optimization
Compare and tune supported engines such as vLLM and compatible future runtimes using real workload evidence.
vLLMParametersLatency
Quantization
Evaluate suitable formats—including BF16, FP8, INT8, INT4, AWQ, and GPTQ—where the selected model and runtime support them.
Quality deltaMemoryThroughput
Hardware Intelligence
Compare supported accelerator and topology choices using measurable workload characteristics rather than generalized rankings.
VRAMLatencyUtilization
Provider Neutrality
Work across customer infrastructure, public clouds, GPU providers, and managed inference endpoints without locking evidence to one vendor.
Customer-ownedCloudManaged
Continuous Optimization
Replay reviewed workload evaluations as production changes and new model or runtime options become viable.
ReplayCompareValidate
API-first evidence
Connect Skhemata to your production runtime.
Send provider-neutral runtime traces from your application, retain only the content your privacy policy permits, and promote reviewed failures into deterministic evaluation cases.
- Workload-scoped, revocable ingestion keys
- Idempotent trace delivery
- Metadata-only or explicit content capture
- Evidence that feeds future candidate evaluations
POST /api/ingest/traces
curl -X POST https://api.skhemata.com/api/ingest/traces \
-H 'Authorization: Bearer skh_ingest_••••' \
-H 'Content-Type: application/json' \
-d '{
"external_trace_id": "request-123",
"success": true,
"latency_ms": 842,
"input_tokens": 128,
"output_tokens": 42
}'A common scorecard
Measure the configuration, not just the model.
Quality and workflow success belong next to latency, tokens, runtime behavior, and infrastructure location.
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
Infrastructure control
An intelligence layer—not a hosting requirement.
Keep execution in customer-owned infrastructure or use controlled managed evaluation compute. Skhemata helps select and validate the configuration above it.
Data-location and deployment constraints are part of the decision.
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.
Ready to stop guessing?
Evaluate the whole runtime stack.
Bring your production criteria. Skhemata brings the continuous evaluation loop.