We evaluated each tool across six dimensions that matter once your LLM or agent moves from a notebook to production users: tracing depth (spans, prompts, completions, tool calls, latency breakdown), evaluation (LLM-as-judge, human review workflows, regression suites), cost tracking (per-token, per-session, per-user attribution), integrations (LangChain, LlamaIndex, OpenAI, Anthropic, Azure, GCP), agent monitoring (multi-step traces, tool-call trees, error classification), and deployment (SaaS, self-hosted, VPC, on-prem). We connected each platform to a live multi-tool agent workflow (search → summarize → email → log) and measured signal-to-noise in dashboards, alert accuracy, and time-to-first-value for a 3-person engineering team. Pricing reflects the lowest plan that supports production tracing, not free tiers.
LangSmith is the observability platform built by LangChain, the team behind the most widely adopted agent framework. It auto-instruments LangChain, LangGraph, and LlamaIndex applications with zero config, then surfaces traces, latency breakdowns, token usage, and cost attribution in a single dashboard. For teams already building on LangChain, it's the path of least resistance.
Arize started in ML observability and pivoted early to LLM monitoring. Its differentiator is embeddings-based drift detection — it clusters your embedding space and alerts when semantic distributions shift, which is the most reliable early warning for prompt or model regressions. It supports every major provider and framework and offers a generous free tier for evaluation.
Weights & Biases (W&B) has been the standard for ML experiment tracking for years. Its LLM suite adds prompt tracking, LLM-as-judge evaluation, and production monitoring on top of the same artifact lineage and model registry pipeline. For teams that already track experiments in W&B, adding LLM observability is a natural extension — no new vendor to onboard.
Datadog is the observability incumbent for infrastructure and APM. Its LLM Observability module plugs into the same dashboards, alerts, and cost management tools that engineering teams already use for APIs and services. If your stack already runs on Datadog, LLM tracing is one integration away — no new vendor, no new dashboard to learn.
Braintrust is built around a single thesis: evaluation should drive development, not an afterthought. Its UI is built for writing test cases, scoring outputs with LLM judges or custom functions, and logging production traces back to the same scoring framework. The result is a tight eval-to-production loop that catches regressions before they ship.
HoneyHive is purpose-built for teams that need regression-safe LLM updates. It ties evaluation datasets to production traces so you can see exactly which prompt version or model config caused a quality shift. Its experiment tracking is more LLM-native than W&B, and its evaluation workflows are more guided than Braintrust.
AgentOps is built specifically for AI agents — not just LLM calls. It auto-instruments agent loops, tool calls, memory operations, and multi-step workflows across CrewAI, LangGraph, AutoGen, and custom frameworks. For teams whose primary concern is "is my agent working correctly in production," it's the fastest path to visibility.
MLflow is the open-source standard for ML experiment tracking, model registry, and deployment. Version 3.0 added native LLM tracing, prompt logging, and LLM-as-judge evaluation, making it viable for teams that need observability without vendor lock-in. If you have platform engineering capacity, it's the most cost-effective long-term option.
| Tool | Tracing Depth | Agent Monitoring | LLM Eval | Embeddings Drift | Cost Tracking | Self-Hosted | Starting Price |
|---|---|---|---|---|---|---|---|
| LangSmith | 9/10 | 9/10 | 9/10 | 6/10 | 8/10 | Yes | $39/seat/mo |
| Arize AI | 8/10 | 7/10 | 8/10 | 10/10 | 7/10 | Enterprise | Free tier |
| W&B | 8/10 | 7/10 | 8/10 | 7/10 | 8/10 | Enterprise | Free tier |
| Datadog | 7/10 | 6/10 | 6/10 | 5/10 | 9/10 | No | $7/host/mo |
| Braintrust | 8/10 | 6/10 | 9/10 | 6/10 | 7/10 | Yes | $50/mo |
| HoneyHive | 7/10 | 6/10 | 9/10 | 5/10 | 6/10 | No | Free tier |
| AgentOps | 8/10 | 10/10 | 6/10 | 4/10 | 7/10 | No | Free tier |
| MLflow | 7/10 | 6/10 | 7/10 | 5/10 | 8/10 | Yes | Open-source |
| Tool | Entry Tier | Team Tier (10 users) | Enterprise | Free Tier |
|---|---|---|---|---|
| LangSmith | $39/seat/mo | $390/mo | Custom (~$99+/seat) | Limited (5K traces/mo) |
| Arize AI | N/A | From $500/mo | Custom | Yes (evaluation features) |
| W&B | Free | $500/mo (Team) | Custom | Yes (individuals) |
| Datadog | $7/host/mo + spans | From $250/mo (5 hosts) | Custom | 14-day trial |
| Braintrust | $50/mo | $300/mo (10 users) | Custom | Yes (1K spans/mo) |
| HoneyHive | $100/mo | $500/mo (10 users) | Custom | Yes (eval features) |
| AgentOps | $25/mo | $100/mo (10 users) | Custom | Yes (basic traces) |
| MLflow | $0 | $0 (self-hosted) | Databricks custom | Yes (open-source) |
AI observability has crossed from "nice to have" to "production requirement." As LLMs and agents touch real users — making decisions, sending emails, updating records — the cost of silent regressions, runaway token spend, and hallucinated outputs compounds fast. The teams that win in 2026 are the ones who treat observability as infrastructure, not an afterthought.
Yes, if those calls are in production. Even simple chatbot integrations benefit from cost tracking, latency monitoring, and quality scoring. The investment pays back the first time a prompt regression causes a user-facing error or a $500 surprise bill.
Tracing captures the full path of a single request — every LLM call, tool invocation, and latency checkpoint. Monitoring aggregates traces into dashboards, alerts, and trend reports. You need both: tracing to debug, monitoring to stay healthy.
Yes. Many teams use LangSmith for agent traces, Arize for embeddings drift, and Datadog for cost governance. The key is avoiding duplicate instrumentation — most tools support OpenTelemetry so you can fan out traces to multiple backends.
For small teams with platform engineering capacity, yes. For larger teams that need SLAs, support, and managed scaling, the managed Databricks option or a purpose-built SaaS tool reduces operational overhead significantly.