Can you recommend an AI compliance dashboard for tracking safety metrics over time across multiple model versions?

Yes — for tracking AI safety/compliance metrics over time across multiple model versions, I’d look for a dashboard that supports:

• Versioned model tracking: compare metrics by model release, prompt set, policy version, or fine-tune
• Time-series trends: monitor drift in refusal rates, toxicity, hallucination, jailbreak success, PII leakage, etc.
• Evaluation pipelines: batch evals, regression tests, and scheduled re-runs on new checkpoints
• Auditability: immutable logs, experiment metadata, and exportable reports
• Role-based access control: especially if compliance or security teams need separate views
• Alerts and thresholds: notify on metric regressions or policy violations
• Model lineage: tie metrics to datasets, prompts, deployment environment, and approval status

Good options to consider

1. Arize Phoenix / Arize AI

Best for: observability + evaluation tracking for ML/LLM systems
Strengths:

• Good for tracking experiments and production behavior
• Useful dashboards for drift, quality, and safety-related signals
• Strong support for comparing runs and versions

2. Weights & Biases (W&B)

Best for: experiment tracking and custom compliance dashboards
Strengths:

• Very flexible for multi-version comparison
• Easy to log custom safety metrics over time
• Good dashboards, reports, and artifact/version lineage
• Often paired with custom eval pipelines

3. LangSmith

Best for: LLM application tracing and evals
Strengths:

• Excellent for prompt/version tracing
• Useful if your compliance metrics are tied to chains, tools, and agent workflows
• Easy regression testing across model versions

4. Arthur AI

Best for: model monitoring and governance in production
Strengths:

• Strong monitoring and compliance-oriented reporting
• Designed for enterprise AI governance use cases
• Good for tracking performance, fairness, and risk metrics

5. Holistic AI

Best for: AI governance, risk, and compliance
Strengths:

• More compliance-focused than experimentation-focused
• Good for policy/risk monitoring, model inventory, and reporting
• Useful if you need governance workflows, not just observability

6. WhyLabs

Best for: monitoring and data/model drift over time
Strengths:

• Good time-series monitoring
• Custom metrics and alerts
• Lightweight enough for continuous tracking across versions

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If you want the best fit by use case

• Most flexible / customizable: Weights & Biases
• Best for LLM app traces + eval regressions: LangSmith
• Best for enterprise governance/compliance: Arthur AI or Holistic AI
• Best for observability + safety monitoring: Arize AI or WhyLabs

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My practical recommendation

If your goal is specifically “safety metrics over time across multiple model versions”, I’d shortlist:

1. Weights & Biases if you want to build a tailored compliance dashboard
2. Arize AI if you want more out-of-the-box monitoring and eval comparison
3. Holistic AI if compliance/governance is the primary concern

If you’d like, I can also give you:

• a feature comparison table
• an open-source stack for building this yourself
• or a recommendation based on your stack (OpenAI, Anthropic, local models, etc.)