Comparison

Nexus vs Jaeger for AI Agent Observability

Jaeger is a CNCF graduated open-source distributed tracing platform — self-hosted, battle-tested on microservices at scale, and free to run. Nexus is an AI-first managed observability platform with LLM cost tracking, agent health dashboards, and zero infrastructure to operate. Here's when each is the right call.

TL;DR

Choose Nexus if…

  • You're building AI agents and need LLM-specific span attributes (model, tokens, cost)
  • You want zero infrastructure — no Kubernetes, no storage backend, no ops burden
  • You want per-agent health dashboards and LLM cost visibility out of the box
  • Free tier + $9/mo flat is your pricing ceiling
  • You need to be tracing in 5 minutes, not 5 hours

Choose Jaeger if…

  • You already run a Kubernetes-based microservices platform with OTel instrumentation
  • Data sovereignty or on-prem deployment is a hard requirement
  • You need high-volume distributed tracing across dozens of services at zero license cost
  • Your team manages infrastructure and Cassandra/Elasticsearch backends are already in-house
  • You want CNCF-graduated, community-vetted, vendor-neutral tooling

Feature comparison

Feature Nexus Jaeger
OTel compatibility ✓ Ingests OTel spans via REST API ✓ Native OTLP support (gRPC + HTTP)
AI-specific span attributes ✓ Model, tokens, cost, agent ID built-in Generic trace spans — no AI-first schema
LLM cost tracking ✓ Per-trace and per-agent cost visibility ✗ Not supported
Agent health dashboard ✓ Per-agent error rates, 7d trends Service dependency graph — not agent-aware
Self-hosting ✗ Hosted only (Cloudflare edge) ✓ Self-hosted only (Kubernetes, Docker)
Infrastructure overhead None — zero ops burden Requires Jaeger collector + storage backend (Cassandra/ES/Badger)
Managed cloud option ✓ Fully managed (Cloudflare Workers) ✗ OSS only — you run the infrastructure
Webhook / email alerts ✓ Included on Pro plan Requires external alerting integration (Prometheus, Grafana)
Setup time 5 min — one API call to start tracing 1–4 hrs — Kubernetes manifests + storage backend + OTel collector config
Pricing Free tier + $9/mo Pro (flat rate) Free (open source) — you pay infra costs

The honest take

Jaeger is one of the most mature distributed tracing tools in the CNCF ecosystem — graduated status, years of production use at Uber and across the industry, and a vibrant open-source community. If you're operating a microservices platform on Kubernetes with dozens of services and dedicated platform engineers, Jaeger is a proven choice. You own every byte of your trace data, and at scale the infrastructure cost can be lower than any SaaS alternative.

Nexus is built for a different problem: AI agent observability. Where Jaeger stores generic distributed trace spans, Nexus understands AI-specific context — which model was called, how many tokens were consumed, what the LLM call cost, and which agent originated the trace. That context is baked into the data schema and surfaced in dashboards designed for agent developers building LLM-powered systems, not platform engineers debugging service latency.

The biggest practical difference is operational overhead. Jaeger requires running a collector, choosing and operating a storage backend (Cassandra, Elasticsearch, or Badger), and wiring up your OTel SDK to point at your cluster. For a solo developer or small team shipping an AI product, that infrastructure burden is often unjustified. Nexus gives you a live tracing dashboard in five minutes with a single API call — no Kubernetes required.

That said, Jaeger and Nexus can coexist. Many teams already running Jaeger for service-level tracing add Nexus specifically for their AI agent layer — letting Jaeger handle infrastructure-level distributed traces while Nexus provides AI-specific visibility: per-agent error rates, LLM cost attribution, token usage trends, and agent health dashboards over rolling 7-day windows.

Try Nexus free

AI-first agent observability. Free tier, no credit card required. Works alongside Jaeger — instrument your agent layer in 5 minutes.