Langfuse vs LangSmith vs Helicone vs Arize: AI Observability in 2026
Four ways to trace AI agents in 2026, none of them perfect. We ran 12 weeks of production traffic through each and benchmarked them on cost, depth, OTel compatibility, and self-host friction.
TL;DR — Pick Langfuse for self-hosted depth, LangSmith if you live in LangGraph, Helicone for the simplest install, Arize Phoenix for ML rigor. CallSphere standardized on Langfuse + Honeycomb after running all four side-by-side.
What goes wrong
flowchart LR
Browser["Browser / Phone"] -- "WebSocket /ws" --> LB["Load Balancer<br/>sticky session"]
LB --> Pod1["Node A · Socket.IO"]
LB --> Pod2["Node B · Socket.IO"]
Pod1 -- "pub/sub" --> Redis[("Redis cluster")]
Pod2 -- "pub/sub" --> Redis
Pod1 --> AI["AI Worker · OpenAI Realtime"]
Pod2 --> AIMost teams choose an observability vendor by reading one comparison post and clicking install. Six months in they discover they've outgrown the free tier (LangSmith), are paying per-trace at scale ($0.005 each adds up fast), can't get traces out without a vendor adapter, or — the worst — the platform doesn't capture agent execution, just LLM calls.
Voice and multi-agent workloads expose this fast. A single phone call may produce 12 LLM calls, 18 tool invocations, two sub-agent handoffs, and four retrieval searches. If your platform records 12 disconnected LLM rows you'll never debug a slow call. You need full-trajectory tracing.
How to monitor
Score each platform on five dimensions:
- Trace depth — does it capture agent reasoning, tool calls, sub-agent handoffs, RAG retrieval?
- OTel compatibility — can you export to other backends without re-instrumenting?
- Self-host story — how painful is it to run your own?
- Cost at 10M traces/mo — what's the bill?
- Eval/replay — can you re-run a trace against a new model?
| Platform | Depth | OTel | Self-host | 10M cost | Eval |
|---|---|---|---|---|---|
| LangSmith | Excellent (LangGraph) | Limited | Cloud-only paid | ~$5K/mo | Strong |
| Langfuse 2.x | Excellent | Native OTel | Docker / Helm easy | self-host: infra only | Strong |
| Helicone | API-call level | Good | Docker | ~$1.5K/mo | Basic |
| Arize Phoenix | Excellent (ML) | Native OTel | Postgres + K8s | self-host: infra only | Best ML primitives |
Helicone is a proxy — change the base URL and you're done. The trade-off is you only see what passes through the proxy, so agent reasoning that doesn't make an API call is invisible. LangSmith is unmatched for LangGraph but ties you to LangChain. Langfuse 2.x is the open-source default for teams that want depth without lock-in. Arize Phoenix wins when ML rigor (drift, embeddings, eval primitives) matters.
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CallSphere stack
We tested all four against real production traffic on our k3s cluster: 37 agents, 90+ tools, 115+ DB tables. Workload was mixed — Healthcare FastAPI :8084, Real Estate 6-container NATS pod, Sales WebSocket on PM2, and the After-hours Bull/Redis queue. We standardized on Langfuse 2.x self-hosted on the same k3s for traces, plus Honeycomb for whole-stack distributed tracing.
Why Langfuse: native OTel ingestion, full-trajectory tracing of agent loops, strong eval and prompt management, no per-trace tax. We deploy it via Helm chart on a dedicated namespace with its own Postgres replica. We send GenAI spans there, infrastructure spans to Honeycomb, and infrastructure metrics to Grafana — all from a single OTel Collector pipeline.
Customers on the $499 plan get read-only access to their tenant in our Langfuse; $1499 enterprise gets a dedicated Langfuse instance. The /affiliate program shares aggregate eval reports with agency partners.
Implementation
- Install Langfuse self-hosted on k3s.
helm repo add langfuse https://langfuse.github.io/langfuse-k8s
helm install langfuse langfuse/langfuse \
--set postgres.enabled=true \
--set ingress.enabled=true \
--set ingress.host=trace.callsphere.ai
- Point your OTel Collector at it.
exporters:
otlphttp/langfuse:
endpoint: https://trace.callsphere.ai/api/public/otel
headers:
Authorization: "Basic ${LANGFUSE_BASIC_AUTH}"
Wrap your agent loop so it emits both client and agent spans (OTel GenAI conventions). No vendor SDK needed.
Build evals as code in your repo, run on every PR, gate deploys on regression. Langfuse has a Python SDK for this; Phoenix has a richer ML eval library — pick whichever fits.
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Set retention policies. Traces are 90 days; sampled exemplars (1%) keep 1 year. PII redaction happens before export.
FAQ
Q: Can I keep two backends without doubling my instrumentation? A: Yes. Instrument once with OTel; the collector fans out to N backends. We send to Langfuse + Honeycomb from one pipeline.
Q: Is LangSmith locked to LangChain? A: Practically yes. They've added OTel ingest but the rich UI features assume LangChain types.
Q: What about Datadog? A: Datadog LLM Observability is genuinely good and natively consumes OTel GenAI semconv. We didn't pick it because we're already paying for Honeycomb.
Q: Helicone is so cheap — is there a catch? A: It's a proxy. If your agent does work outside an API call (planning, in-process retrieval), Helicone never sees it. Great for chat completions monitoring, weak for agent debugging.
Q: How do I migrate later? A: If you're on OTel-native, you change the exporter endpoint. If you're on a vendor SDK, plan for a re-instrument.
Sources
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