Who this is for
- Platform and infrastructure engineers shipping agentic systems to production.
- AI and ML leads choosing between SaaS and self-hosted observability.
- CTOs budgeting for token cost, latency and reliability at scale.
Buyer's guide
AI agent observability: compare the tools, then choose.
A practical guide for engineering teams who need to monitor, debug and trust AI agents in production. The five signals every agent stack must surface, side-by-side tradeoffs of the leading platforms, and how to decide between SaaS and self-hosted.
One email with the download link. No spam, no sequences.
trace · support-agent-v3
run 8f2c… · 4 steps · 5.6k tokens
- agent.planDuration: 412 ms1.2k
- tool.search_docsDuration: 1.86 s0.4k
- llm.synthesizeDuration: 2.31 s3.8k
- tool.write_ticketDuration: 308 ms0.2k
- eval.groundednessDuration: —score 0.82
The five signals every agent stack must surface
If your stack cannot show these, you are guessing. Use them as the scoring axes for any vendor you shortlist.
Multi-step
Traces
Full reasoning path of every agent run, step by step.
chains, not single requests
$ / run
Token cost
Per run, per user, per model — before the bill surprises you.
attributed to the caller
I/O
Tool calls
Inputs, outputs, retries and failures of every tool the agent hits.
captured on each call
Scores
Evals
Heuristics, LLM-as-judge and human review on real production traffic.
tied back to traces
p95
Latency
Where the seconds go: model, tool, retry or your own orchestration.
per step, not per request
What you'll evaluate
- The five signals every agent stack must surface.
- Side-by-side tradeoffs of Langfuse, LangSmith, Helicone, Arize Phoenix and self-hosted options.
- A shortlist of integrations that matter for B2B buyers.
The shortlist, side by side
A starting map of the platforms most teams end up comparing. Score them against your own volume, data residency and eval needs.
Langfuse
- Posture
- OSS-first
- Tracing
- Full agent traces
- Evals
- Built-in + LLM-as-judge
- OpenTelemetry
- Supported
- Hosting
- Cloud or self-hosted
LangSmith
- Posture
- Commercial
- Tracing
- Deep LangChain-native
- Evals
- Datasets + human review
- OpenTelemetry
- Partial
- Hosting
- Cloud, enterprise self-host
Helicone
- Posture
- OSS-first
- Tracing
- Proxy-level logging
- Evals
- Lightweight
- OpenTelemetry
- Supported
- Hosting
- Cloud or self-hosted
Arize Phoenix
- Posture
- OSS-first
- Tracing
- OpenTelemetry-native
- Evals
- Strong eval tooling
- OpenTelemetry
- Native
- Hosting
- Local or self-hosted
Self-hosted stack
- Posture
- DIY
- Tracing
- What you instrument
- Evals
- You build it
- OpenTelemetry
- Your choice
- Hosting
- Your infrastructure
Related guides
Deeper comparisons and how-tos for the buyer doing the shortlist work.
Langfuse vs LangSmith
Two of the most adopted platforms. How they differ on tracing, evals, OSS posture and price.
Self-hosted LLM observability
When the cloud is off the table: data residency, OSS options, and what you give up.
LLM eval platforms compared
Heuristic evals, LLM-as-judge and human-in-the-loop — which tool supports which.
Phoenix vs Langfuse
Two OSS-first stacks. Where Phoenix's OpenTelemetry roots matter, and where Langfuse's velocity wins.
FAQs
The questions engineering teams ask before they pick a stack.
What is AI agent observability?
The practice of monitoring LLM-based agents across their full reasoning path: prompts, completions, tool calls, intermediate steps, token usage, latency and output quality. It differs from classic APM because the unit of work is a multi-step chain of LLM calls, not a single request.
Do I need a dedicated tool, or can I extend Datadog or New Relic?
You can extend, but most teams add a purpose-built layer — Langfuse, LangSmith, Arize Phoenix or Helicone — because agent runs carry semantics that generic APM tools do not model: prompts, completions, tool I/O and evaluation scores.
How do I evaluate vendors without a six-month POC?
Run a one-week trace of a real production agent through each shortlist candidate. Score on time-to-first-trace, OpenTelemetry compatibility, evals support, PII redaction and pricing at your volume. Send us your shortlist and we will send back a side-by-side scorecard.
Get the comparison sheet
A one-page comparison of the leading AI agent observability tools — what to trace, what to alert on, and how to keep token costs under control. No spam, no sequences.