Shortlist Lunary for a shared workflow around traces, conversations, and prompts; Langfuse for an open-source AI engineering platform; or Helicone when the gateway is the center of your stack.
Teams evaluating a move from LangSmith
Lunary
Evaluate when engineers and product colleagues need to inspect the same conversation, find its underlying runs, and improve the prompt.
Langfuse
Evaluate when open-source deployment, tracing, and dataset experiments are central purchasing requirements.
Helicone
Evaluate when you also want to consolidate provider access through an AI gateway.
At a glance
| Candidate | Start your evaluation here | Verify before choosing |
|---|---|---|
| Lunary | Conversation → trace → prompt review | Your framework, data controls, and team workflow |
| Langfuse | Tracing → datasets → experiments | Deployment operations and edition requirements |
| Helicone | Gateway → requests → provider operations | Routing needs and custom tool visibility |
| Keep LangSmith | Existing traces → evaluations → prompts | Whether a configuration change solves the actual problem |
Start with the reason you are switching
A change of observability tool is worthwhile when it removes a recurring obstacle: an awkward review workflow, infrastructure constraints, incomplete traces, or a cost model that no longer fits. Write down the last three incidents your team struggled to resolve. Use those incidents as the evaluation script. LangSmith already supports tracing, evaluation, and prompt engineering, so a feature checklist alone is unlikely to reveal the difference that matters.
- Separate an instrumentation problem from a product limitation.
- Include the person who investigates customer reports in the trial.
- Estimate migration and ongoing operations alongside subscription cost.
Choose alternatives by the job they will do
Lunary combines application traces with conversation and prompt workflows. Langfuse documents tracing, evaluations, prompt management, and self-hosting. Helicone offers provider access through its gateway and observability around those requests. These categories overlap: do not assume that a gateway cannot log custom operations or that an open-source product automatically has every enterprise feature in its core license.
Run the same incident in every candidate
Choose a failed agent run containing a model call, a tool error, and a retry. Give every candidate the same identifiers and safe payload fields. Measure how long it takes a teammate to find the failure without help from the person who added the SDK. Then test a prompt revision against saved examples. A successful trial ends with a reproducible explanation of the failure and a reviewable fix, not a dashboard full of requests.
- Verify parent-child relationships and streaming completion.
- Check error visibility, token attribution, and user or session filters.
- Ask whether the same workflow is available on the plan you would buy.
Make switching reversible
Keep the existing integration while you test one bounded workload in the alternative. Preserve source run IDs in a mapping file and export the historical examples you actually need. Historical archives, evaluation datasets, and newly instrumented production traffic are different deliverables. Do not assume that exporting traces recreates prompts, permissions, or experiments in the destination.
Common questions
Does LangSmith require LangChain?
No. LangSmith documents framework-independent tracing. Evaluate alternatives on your actual workflow rather than assuming a framework restriction.
Which alternative is cheapest?
There is no universal answer. Compare billable volume, retention, seats, required features, evaluator model usage, and self-hosting operations using the current plans and your own traffic.
Sources & methodology
Lunary publishes this guide. We compare documented workflows and explain where each approach fits; this is not an independent benchmark or a hands-on product rating. Features, limits, and commercial terms can change. Check the linked vendor documentation before deciding.