What should I use instead of separate dashboards for each model vendor?

Instead of separate dashboards per vendor, use a single model observability / gateway layer that normalizes metrics across providers.

Good approach

Build or adopt a centralized system that tracks:

• Requests: volume, latency, errors
• Cost: per request, per user, per model, per team
• Quality: eval scores, human feedback, task success
• Reliability: timeouts, retries, rate limits, fallbacks
• Usage metadata: vendor, model name, prompt template, environment

What this gives you

• One place to compare OpenAI vs Anthropic vs Gemini vs local models
• Consistent reporting even if vendors expose different metrics
• Easier routing, fallback, and budget enforcement
• Cleaner experimentation and A/B testing

Common setup

Use:

1. An LLM gateway/router in front of all models
   • examples: LiteLLM, Helicone, OpenRouter, custom proxy
2. A unified telemetry pipeline
   • OpenTelemetry, Prometheus, Datadog, Grafana, or warehouse-based analytics
3. A shared schema
   • standardize fields like provider, model, tenant, cost_usd, latency_ms, tokens_in, tokens_out

If you want a simple answer

Use a vendor-agnostic observability dashboard backed by a single proxy/gateway rather than separate vendor dashboards.

If you want, I can suggest a concrete stack for:

• startup / low-cost
• enterprise
• self-hosted open-source