8 LLM observability platforms compared for 2026

When you line up LLM observability platforms compared for 2026, the field has matured from scattered logging scripts into full tracing backends that speak OpenTelemetry and attribute cost to individual spans. The right choice depends on whether you need self-hosting, tight eval loops, or just a drop-in proxy that captures every token.

1. LangSmith

LangSmith is the default for teams already on LangChain, but it works with any Python or JS client via explicit run collectors. You create a project, set LANGCHAIN_TRACING_V2=true, and the SDK ships traces to the hosted backend. The data model centers on “runs” — each LLM call, retriever, or tool is a run with inputs, outputs, and metadata.

The strength is the integrated evaluation harness. You can define datasets and scorers, then run regression tests in CI. For custom instrumentation outside LangChain, use the @traceable decorator:

from langsmith import traceable

@traceable(name="summarize")
def summarize(text: str) -> str:
    # call your model
    return client.chat.completions.create(...)

Weakness: vendor lock-in on trace format and pricing that scales with volume. Self-hosting is not available; you must use their cloud.

2. Langfuse

Langfuse is open-source, self-hostable, and ships with an OpenTelemetry-compatible ingestion path. It mirrors LangSmith’s run model but stores everything in Postgres/ClickHouse, so you own the data. The SDK supports Python, JS, and a lightweight prompt management layer.

A practical pattern is to wrap your gateway calls. If you route through n4n.ai, the gateway’s per-token usage metering emits OpenAI-compatible logs that Langfuse can ingest via the OTel collector. You get trace waterfalls with token counts per provider.

from langfuse import Langfuse
lf = Langfuse()
trace = lf.trace(name="chat")
trace.span(name="openai-call", input=..., output=...)

Langfuse’s eval is younger than LangSmith’s but supports custom scorers and human feedback loops. For most startups, the self-host angle alone justifies the operational overhead.

3. Helicone

Helicone is the fastest path to observability if you already use the OpenAI SDK. You change the base URL to https://api.helicone.ai/v1 and add a provider key header; no code changes inside your call sites. It captures latency, token usage, and model-level errors, and supports virtual keys for per-customer metering.

import openai
openai.base_url = "https://api.helicone.ai/v1"
openai.default_headers = {"Helicone-Auth": "Bearer <key>"}

Helicone is hosted-first but offers a self-hosted Docker compose. Its caching proxy can sit in front of multiple providers, though it does not yet do automatic fallback across providers. For pure OpenAI-compatible traffic, it’s the lowest-friction option among LLM observability platforms compared.

4. Arize Phoenix

Phoenix is an open-source LLM tracing tool built on OpenTelemetry standards. It runs as a local Flask app or a container, and you point OTel exporters at it. The differentiator is deep embedding analysis: it computes vector drift, token distribution, and prompt similarity out of the box.

Instrumentation uses the openinference instrumentation libraries:

from openinference.instrumentation.openai import OpenAIInstrumentor
OpenAIInstrumentor().instrument()

Phoenix is strong for research teams debugging retrieval quality, but its UI is less polished for production SaaS workflows. You’ll likely pipe its data into Arize’s commercial cloud for long-term retention.

5. Weights & Biases Weave

Weave is W&B’s LLM layer, offering experiment tracking, asset versioning, and trace timelines. It integrates with the existing W&B ecosystem, so if your ML team already uses artifacts, this is natural. You call weave.init("project") and decorate functions.

import weave
weave.init("llm-prod")
@weave.op()
def generate(prompt: str):
    return model.invoke(prompt)

Weave excels at comparing prompt versions across runs and linking traces to training data. The downside is the heavy client dependency and a UI that assumes familiarity with W&B concepts. Among LLM observability platforms compared, it’s the most “MLOps-native.”

6. OpenLLMetry (Traceloop)

OpenLLMetry is a vendor-neutral OpenTelemetry distribution for LLMs. It auto-instruments OpenAI, Anthropic, LangChain, and more, exporting to any OTel collector (Jaeger, Tempo, Datadog). You keep full ownership of the pipeline.

from traceloop.sdk import Traceloop
Traceloop.init(app_name="api")

Because it’s just OTel, you can slice traces in existing APM dashboards. The tradeoff: no built-in eval or cost analytics — you assemble those from raw spans. For platform engineers who already run observability stacks, this is the cleanest integration.

7. AgentOps

AgentOps targets agentic workflows — AutoGen, CrewAI, LangGraph. It captures not just LLM calls but agent state transitions, tool usage, and session replays. The dashboard reconstructs multi-agent conversations as a timeline.

import agentops
agentops.init("<key>")

It provides “agent scorecards” measuring goal completion and token efficiency per session. Self-hosting is not offered; it’s a hosted service with a free tier. If your system is a single chained call, AgentOps is overkill; for orchestrated agents, it’s purpose-built.

8. Datadog LLM Observability

Datadog added LLM monitoring to its APM suite, letting you trace LLM spans alongside backend services. You install the DD agent and use the ddtrace library with the LLM integration enabled. It correlates model latency with infrastructure metrics — useful if you already pay for Datadog.

from ddtrace.llmobs import LLMObs
LLMObs.enable()

Strengths: unified dashboards, SLOs, and anomaly detection on token spend. Limitations: cost is tied to overall Datadog billing, and the LLM-specific features lag dedicated tools in eval flexibility. Still, for enterprises standardizing on one observability vendor, it simplifies the stack.

Synthesis

Here’s a quick comparison of the eight:

The LLM observability platforms compared here split into three camps: self-hosted OTel-based (Langfuse, Phoenix, OpenLLMetry), hosted convenience (LangSmith, Helicone, Weave, AgentOps, Datadog), and agent-specialized (AgentOps). Pick based on data residency and whether you need eval or just traces.