Comparison
Nexus vs DeepEval for AI Agent Observability
DeepEval is an open-source Python framework for LLM unit testing — write pytest-style evaluation suites that score model outputs on faithfulness, contextual relevance, hallucination, and more. Nexus is a real-time production observability platform: live span timelines, per-agent health dashboards, LLM cost attribution, and webhook alerts. These tools solve different problems — here's when each is the right call.
TL;DR
Choose Nexus if…
- You need real-time visibility into live agent runs — spans as they happen
- You want LLM cost tracking, token usage, and latency per trace
- You need to debug agent failures with a full span waterfall
- You want per-agent health dashboards and error rate trends over time
- Webhook and email alerts on error spikes are important to you
Choose DeepEval if…
- You want to write pytest-style test cases that evaluate LLM output quality
- You need hallucination, faithfulness, and contextual relevance metrics in CI
- You want to catch prompt regressions before they reach production
- You're comparing model versions or retrieval strategies offline
- LLM output correctness in a test harness is your primary concern
Feature comparison
| Feature | Nexus | DeepEval |
|---|---|---|
| Primary use case | Real-time AI agent observability | Offline LLM unit testing and CI evaluation |
| Execution model | ✓ Real-time — spans ingest as they happen | Batch — runs as a pytest test suite in CI |
| LLM quality metrics | ✗ Not applicable | ✓ Faithfulness, hallucination, contextual relevance, G-Eval |
| LLM cost tracking | ✓ Per-trace and per-agent cost visibility | ✗ Not a core feature |
| Trace timeline view | ✓ Live span waterfall with timing | No trace UI — results appear in pytest output |
| Agent health dashboard | ✓ Per-agent error rates, 7d trends | ✗ No agent-level health concept |
| CI/CD integration | ✗ Not applicable (runtime tool) | ✓ Native pytest plugin — fails build on eval regressions |
| Webhook / email alerts | ✓ Included on Pro plan | ✗ Not a core feature |
| TypeScript SDK | ✓ First-class TypeScript support | Python only |
| RAG retrieval metrics | ✗ Not applicable | ✓ Contextual precision, recall, RAGAS metrics |
| Infrastructure overhead | None — fully managed SaaS | Runs locally — no server needed |
| Setup time | 5 min — one API call to start tracing | pip install deepeval + write test cases per metric |
| Pricing | Free tier + $9/mo Pro (flat rate) | Free (OSS) — Confident AI cloud available separately |
The honest take
DeepEval is a strong open-source framework for teams who want CI-gated LLM quality evaluation. You write test cases using built-in metrics — faithfulness, hallucination detection, contextual precision, contextual recall, G-Eval — and DeepEval runs them in a pytest suite. If an LLM output drops below your threshold for hallucination or relevance, the build fails. That is exactly the right tool for iterating on prompts or comparing retrieval strategies during development.
Nexus is built for production. Once your agent is deployed, you need to know what it is doing right now: which traces are failing, how long each LLM call took, what the cost per run is, and whether error rates are trending up. Nexus ingests structured spans in real time, gives you a nested waterfall for each trace, and alerts you via webhook or email when something goes wrong. None of that exists in a pytest-based eval framework.
The distinction is pre-production vs post-production. DeepEval answers “does my pipeline meet quality thresholds?” before you ship. Nexus answers “what is my agent doing, and why did it fail?” after you ship. They are almost entirely complementary — the two tools serve adjacent parts of the LLM development lifecycle with almost no overlap.
Teams building production AI products often use both: DeepEval in CI to gate quality before deploy, Nexus in production to monitor runtime health. If you are choosing only one, ask yourself which question you're answering. Offline LLM quality gates during development — DeepEval. Real-time production agent health — Nexus.
Monitor your agents in real time
Production AI agent observability. Free tier, no credit card required. Start tracing in 5 minutes — full span waterfall, LLM cost tracking, and per-agent health dashboards included.