When teams compare LangSmith with the rest of the field, the real test is often day-to-day behavior. Can you iterate on prompts without losing track of what changed, can you evaluate quality in a way that holds up in production, and can you spot regressions quickly with dashboards and alerts?
Others switch for a myriad of reasons, from security and compliance concerns in the wake of a LangChain security incident potentially exposing things like API keys, wanting a platform that is truly framework-agnostic, or wanting to switch to something open source for their team to build upon. This guide, culled from docs and other sources, is built to help.
Where tools differ in practice
Arize Phoenix and Arize AX. Phoenix is open source and ties together tracing, evaluation, and monitoring so a prompt change in the playground becomes a dataset run you can compare later. AX carries the same loop into a managed service with dashboards, alert routing, RBAC, and SLAs. Skim capabilities in the Phoenix docs and the AX overview.
LangSmith. Arent' we switching? Feels like they should be here just to be fair! See the LangSmith docs.
LangFuse. LangFuse offers an MIT-licensed path with a straightforward prompt workflow and analytics. Its custom dashboards guide shows the kinds of views teams build first.
Datadog LLM Observability. If your org already lives in Datadog, the LLM module adds spans, metrics, and out-of-the-box evaluators inside the same platform. The product page explains how traces and costs surface.
MLflow GenAI. MLflow provides a code-first evaluation and monitoring path that fits notebook-heavy teams; start with the GenAI evaluate and monitor guide.
Why evaluations matter
Most LLM features degrade in subtle ways. A small prompt tweak that improves one dataset can hurt another, and a model upgrade that helps average accuracy can introduce edge-case failures. A good evaluation loop keeps you honest.
Think in three tiers. First, assertions that always run, such as guardrail checks for toxicity or PII, give you a floor of safety. Second, task-specific scorers, such as answer faithfulness and retrieval accuracy, give you signal that aligns with product metrics. Third, human review, done in small, well-sampled batches, calibrates your automated scorers so you trust them over time.
What good looks like: every playground trial can be turned into a dataset run, each run attaches to a specific prompt and model version, scorers are recorded with their parameters, and results roll up to a view that compares experiments week over week. Phoenix and AX ship with evaluator templates and RAG cookbooks so teams can score hallucination, faithfulness, and retrieval quality without scaffolding. LangSmith blends built-in and custom evaluators bound to datasets. LangFuse supports LLM-as-a-judge and labeling flows. MLflow leans into code and scorer composition.
Why tracing matters
Evaluations explain outcomes, tracing explains causes. Without traces you can see that a prompt failed, but not which tool call, retrieval step, or function output pushed it off course. Tracing gives you a timeline with inputs, outputs, tokens, and latencies so you can root-cause problems quickly.
Two patterns pay off. First, replay, where any span can be sent back into a playground to reproduce and tweak. Second, session context, where multi-turn interactions are grouped so you can see how state, memory, and tools evolve. Phoenix supports span replay and sessions, AX adds agent flow views and connects traces to dashboards and alerts. LangSmith’s nested spans and experiment comparison support step-by-step inspection. Datadog centralizes traces next to infra metrics. MLflow focuses on tracing inside notebooks.
RAG analysis that moves the needle
RAG systems fail for specific reasons, usually retrieval quality, reranking, context assembly, or prompt instructions. You want component-level metrics that separate these paths, not just an overall accuracy score. Phoenix and AX expose end-to-end and per-component views that make it clear whether the candidate set is weak or the prompt is mishandling a good context window. LangSmith and LangFuse rely on dataset-driven scoring with LLM judges and custom scorers. MLflow encourages tracing and eval notebooks so you can probe retrieval alongside latency.
Dashboards, cost, and alerts
Quality is only useful if the right person sees it at the right time. AX includes token and latency dashboards, cost tracking, a custom metrics API, and routing to Slack or PagerDuty. Datadog covers this inside its platform, which helps when you already centralize logs and metrics there. Phoenix makes it easy to export annotated spans if you prefer BI tooling or notebooks.
At-a-glance feature matrix
| Feature | Arize AX | Phoenix | LangSmith | LangFuse | Datadog LLM Obs | MLflow GenAI |
|---|---|---|---|---|---|---|
| Hierarchical tracing with tool calls | Yes | Yes | Yes | Yes | Yes | Yes |
| Prompt playground UI | Yes | Yes | Yes | Yes | No UI | Code-first |
| Span replay into playground | Yes | Yes | Partial | Side-by-side on datasets | No | Code-first |
| Built-in evaluators | Yes | Yes | Yes | Yes | Yes | Yes |
| Custom code evaluators | Yes | Yes | Yes | Yes | Via SDK | Yes |
| Datasets and experiments | Yes | Yes | Yes | Yes | Partial | Yes |
| RAG component analytics | Yes | Yes | Custom or judge | Judge or custom | Partial | Code-first |
| Production monitors and alerts | Yes | OSS monitors | Limited | Limited | Yes | Limited |
| Dashboards and widgets | Yes | Grafana or notebooks | Basic charts | Custom dashboards | Full | Notebooks |
| Cost and token tracking | Yes | Token counts | Tokens and cost in runs | Usage metrics | Cost views | Token counts |
| Agent flow visualization | Yes | Yes | Trace tree | Trace tree | Trace tree | Trace tree |
Putting it to work
A practical rollout looks like this. Start with a small, stable dataset that reflects the user flows you care about, add a handful of scorers that match product goals, and wire a single alert for a simple threshold, for example faithfulness below a chosen score. Instrument tracing early so every evaluation datapoint has context. When results are stable, expand the dataset and add regression views. As volume grows, decide where managed dashboards and alerting save you time, which is where AX usually steps in, while Phoenix remains a reliable self-hosted path.
Migration without drama
If you are already on LangSmith, export or forward traces and land them in Phoenix or AX. Keep dataset identifiers stable so offline scores match online monitors. For steps and quickstarts, see the vendor docs when you set this up in your environment.
Bottom line
For teams that want a durable loop from prompt to evaluation to monitoring, Arize AX covers the most ground in one place and preserves an open-source on-ramp through Phoenix. LangSmith remains a smooth option for LangChain-first builds, LangFuse is a solid OSS choice with a growing feature set, Datadog fits platform-standard orgs, and MLflow suits notebook-driven teams that value code-first control.
If you want a longer buyer companion, Arize’s top LangSmith alternatives is actually a pretty fair and helpful reference.
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