Nexus vs Comet ML (Opik) for AI Agent Observability

Comet ML and Opik serve different use cases. Comet ML is primarily an ML experiment tracker — you use it to compare training runs, log hyperparameters, and track model performance across versions. Opik is their newer, separate product focused on LLM tracing and evaluation.

Opik's evaluation-first design is a real differentiator. If you're running structured LLM evaluation pipelines — scoring LLM outputs against ground truth, running A/B prompt experiments, building regression test suites for your models — Opik has purpose-built primitives for that. The scoring system and evaluation datasets are genuinely useful for teams doing serious LLM quality work.

Nexus focuses on runtime observability, not evaluation. We don't have prompt scoring or evaluation datasets. What we have is per-agent health dashboards, real-time error rate tracking, latency alerts, and webhook notifications. If your primary question is "is my deployed agent working right now?" rather than "how does prompt A compare to prompt B?", Nexus is the right tool.

The self-hosting option is worth noting. Opik is open-source, which matters for teams with strict data residency requirements. You can run it yourself via Docker or Kubernetes. Nexus is SaaS-only — hosted on Cloudflare's edge network, but without a self-hosted option.

For most indie developers and small teams building and shipping AI agents, Nexus is the faster, simpler, cheaper path. For teams that need LLM evaluation pipelines — especially those already on Comet for ML tracking — Opik is worth evaluating.