Comparison
Nexus vs OpenLLMetry for AI Agent Observability
OpenLLMetry is an open-source OpenTelemetry instrumentation library for LLMs — it patches LangChain, LlamaIndex, and OpenAI so you get spans with zero code changes. But you still need somewhere to store and query those traces. Here's how Nexus and OpenLLMetry relate to each other.
TL;DR
Choose Nexus if you…
- ✓ Want a hosted backend with zero infrastructure to manage
- ✓ Want explicit, readable tracing code rather than monkey-patching
- ✓ Need email alerts, webhook notifications, and latency thresholds out of the box
- ✓ Want predictable $9/mo pricing with no per-event volume charges
Choose OpenLLMetry if you…
- ✓ Already run OpenTelemetry collectors across your services
- ✓ Want auto-instrumentation with zero SDK calls added to your code
- ✓ Need to route trace data to multiple backends simultaneously
- ✓ Require full data sovereignty with self-managed OTel infrastructure
Are they alternatives or complements?
OpenLLMetry is an instrumentation library — it adds OTel spans to your LLM calls. It does not provide a backend to store, visualize, or alert on those traces. You still need something like Grafana Tempo, Jaeger, Honeycomb, or Traceloop Cloud to receive the spans.
Nexus is a complete observability platform — SDK + backend + dashboard + alerting. If you use OpenLLMetry for auto-instrumentation but want Nexus as the backend, that’s a valid combination: OpenLLMetry emits OTel spans, and Nexus can receive them. If you want a simpler path without OTel infrastructure, use the Nexus SDK directly and skip OpenLLMetry entirely.
Feature Comparison
| Feature | Nexus | OpenLLMetry |
|---|---|---|
| Hosted backend (no infra) | ✓ Fully hosted SaaS | — SDK only; backend required |
| Trace & span storage | ✓ Included | — Via OTel-compatible backend |
| Dashboard / UI | ✓ Included | — Via backend UI (Grafana, Jaeger, etc.) |
| Email alerts on failure | ✓ Pro tier | — Via alertmanager |
| Webhook notifications | ✓ Pro tier | — Not built in |
| Auto-instrumentation | — Explicit SDK calls | ✓ Zero-code monkey-patching |
| OTel-native format | — Nexus format | ✓ Core feature |
| Route to multiple backends | — | ✓ Any OTel exporter |
| Self-hosted option | — | ✓ Full OTel stack |
| Python SDK | ✓ Open-source | ✓ Open-source |
| TypeScript SDK | ✓ Open-source | ✓ Open-source |
| Setup time | 2 min (3 lines of code) | 5–20 min (OTel setup required) |
| Pricing | $0–$9/mo flat | Free (+ backend infra cost) |
When Nexus is the better choice
Nexus wins when you want to be operational in 2 minutes. Install the SDK, create a free account, get an API key, and you have traces flowing with explicit span context you control. There’s no OTel collector to configure, no Grafana dashboard to build, no Prometheus alerting rules to write.
Nexus also wins on agent-specific features: the dashboard shows per-agent error rates, span waterfalls, and token cost tracking in a format designed for LLM agent workflows — not generic distributed tracing. Email and webhook alerts require one setting toggle, not a separate alertmanager deployment.
When OpenLLMetry is the better choice
OpenLLMetry wins when you already run OTel infrastructure and want LLM spans to flow into the same pipeline as the rest of your services. If your platform team maintains Grafana Tempo, Honeycomb, or Jaeger, adding OpenLLMetry means your AI agent traces land in the same tool as your API traces — unified search, unified alerting, unified retention.
OpenLLMetry also wins on auto-instrumentation scope: it patches LangChain, LlamaIndex, OpenAI, Anthropic, and dozens of other frameworks automatically. If you want span coverage without touching your agent code, OpenLLMetry gets you there. Nexus requires explicit SDK calls — more control, more work.
Try Nexus in 2 minutes
pip install nexus-client
import os
from nexus_client import NexusClient
nexus = NexusClient(api_key=os.environ["NEXUS_API_KEY"], agent_id="my-agent")
trace = nexus.start_trace(name="agent-run")
span = trace.add_span(name="llm-call", input={"prompt": "..."})
# ... your LLM call ...
span.end(status="ok", output={"tokens": 150})
trace.end(status="success")