§ 06Eval Cloud — capabilities
Five capabilities. One platform for model reliability.
Eval Cloud covers the full operational lifecycle of AI models in production: run evals continuously, migrate safely, detect behavioral drift, understand costs, and generate compliance evidence — for any model, any provider.
Continuous Eval Execution
EVAL · CI-NATIVE, ANY MODEL, ANY PROVIDER
Define eval specs in TypeScript and YAML. Run against any model across any provider. Eval-as-CI integrates natively with GitHub Actions, GitLab CI, and Bitbucket Pipelines — eval gates block merge on regression. The dashboard surfaces pass/fail history and regression tracking across every run.
Key features
- Eval specs in TypeScript + YAML — exact-match, LLM-as-judge, semantic grading
- Eval-as-CI: GitHub Actions, GitLab CI, Bitbucket Pipelines
- Merge-block gates on regression
- Pass/fail history and baseline comparison dashboard
Providers
Anthropic · OpenAI · Google · Mistral · Cohere · any OpenAI-compatible endpoint
Migration Safety
MIGRATE · ZERO-COST RE-BASELINE ON EVERY RELEASE
Automated re-baseline whenever a provider ships a new model version. Diff reports compare old versus new across your full eval suites before you upgrade. Migration runs are VISystems-billed and do not count against your monthly quota. Safety scores gate production promotion.
Key features
- Automated re-baseline on provider model releases
- Side-by-side diff reports across eval suites
- Safety score before any upgrade is promoted
- Zero-cost migration runs — VISystems-billed
Providers
All providers — triggered automatically on model version changes
Drift Detection
MONITOR · BEHAVIORAL DRIFT, LATENCY, COST
Continuous behavioral monitoring across quality scores, latency, cost, cache patterns, and token usage. z-score anomaly detection with configurable thresholds. Alerting via email, Slack, or webhook so you know before your users do.
Key features
- Quality score, latency, cost, and token usage monitoring
- z-score anomaly detection with configurable thresholds
- Cache pattern and cache hit rate tracking
- Alerting via email, Slack, and webhook
Providers
All monitored providers — continuous polling, not batch
Cost Intelligence
COST · PER-MODEL, PER-WORKFLOW, ALL PROVIDERS
Per-model and per-workflow cost analysis across every connected provider. Cache ROI calculation identifies which prompts benefit most from caching. Batching optimization surfaces where async batch APIs cut costs. Cross-provider cost comparison informs provider strategy.
Key features
- Per-model and per-workflow cost breakdown
- Cache ROI calculation and prompt-level recommendations
- Batch vs. streaming cost optimization recommendations
- Cross-provider cost comparison for provider strategy decisions
Providers
Anthropic · OpenAI · Google · Mistral · Cohere — side-by-side comparison
Compliance Evidence
COMPLIANCE · SOC 2 · SR 11-7 · NIST AI RMF · EU AI ACT
Automated model-risk evidence packages for SOC 2 Type II, SR 11-7, NIST AI RMF, and EU AI Act review. Eval runs, model-upgrade analyses, regression alerts, provider inventory, and audit records are assembled into reviewer-ready evidence.
Key features
- Reviewer-ready evidence packages for SOC 2 Type II, SR 11-7, NIST AI RMF, EU AI Act
- Eval and migration history mapped into model-risk artifacts
- Tamper-evident audit trail with SHA-256 hash chain
- HTML and JSON evidence exports, PDF after package format stabilizes
Providers
Framework-agnostic — evidence generated from all connected providers
See how it fits your stack?
Join the waiting list for early access or reach out directly to discuss your use case.