What LangSmith Actually Is
LangSmith is an LLM observability and evaluation platform built and maintained by LangChain. It is designed to work natively with LangChain-based applications and gives you deep tracing of multi-step chain executions: each LLM call, each tool invocation, each retrieval step, and each intermediate output is captured and displayed as a hierarchical trace. Beyond logging, LangSmith includes an evaluation framework where you can define evaluators (LLM-as-judge, rule-based, human annotation) and run them against datasets to measure prompt quality over time. The prompt playground lets you iterate on prompts against real trace data, and the annotation queue enables human review workflows for outputs that need quality scoring. LangSmith is not limited to LangChain: you can use the LangSmith SDK directly to trace custom code, but the depth of automatic tracing is greatest when using LangChain or LangGraph. It has a hosted SaaS version and an Enterprise tier for self-hosted deployments, which matters for GDPR and data residency compliance.
What Helicone Actually Is
Helicone is an LLM observability platform built around a proxy model. Instead of instrumenting your code with an SDK, you route your OpenAI, Anthropic, Cohere, or other LLM API calls through Helicone's proxy endpoint. Helicone intercepts each request, logs the inputs and outputs, records latency and token counts, and makes all of this available in a dashboard, all without changing your prompt logic or adding framework dependencies. The setup friction is minimal: change one base URL and add an API key header, and you have full request logging. Helicone also supports caching (to reduce costs on repeated prompts), rate limiting, custom metadata tagging, and basic evaluation features. The platform is provider-agnostic, which means it works regardless of which LLM provider you use or whether you use LangChain at all. Helicone is popular with teams that want immediate observability without committing to a framework or restructuring their codebase.
Integration Complexity and Setup Speed
Helicone wins on setup speed by a significant margin. For an OpenAI integration, adding Helicone is literally two lines of change: update the base URL to Helicone's proxy and add an API key header. You have full request logging within minutes. No SDK import, no decorator pattern, no changes to your chain structure. LangSmith requires adding the LangSmith SDK and setting environment variables, which is also relatively quick for simple chains. Where LangSmith's setup becomes more involved is in instrumenting custom code that does not use LangChain's standard abstractions. Wrapping custom logic in traced runs requires explicit SDK calls. For LangChain applications, tracing is automatic and the setup is fast. The key question is whether your codebase already uses LangChain. If yes, LangSmith's integration is natural. If you are making raw API calls or using another library, Helicone's proxy approach is less disruptive.
Trace Depth and Debugging Capability
LangSmith has deeper trace capability for complex multi-step AI workflows. If your product runs an agent that calls tools, retrieves documents, re-ranks results, and then generates a response, LangSmith captures each step as a nested trace node. You can see exactly which retrieval step returned which documents, which tool call took 3 seconds, and which LLM call produced the intermediate output that caused the final response to go wrong. For AI agents and complex RAG pipelines, this level of trace depth is invaluable for debugging. Helicone captures request and response at the LLM call level. If you make a single LLM API call, Helicone shows you the prompt, the completion, the token counts, and the latency. For applications with multiple LLM calls, you see each call individually but without automatic correlation into a single trace representing the logical user request. Custom metadata tagging in Helicone allows some manual correlation, but it requires deliberate effort.
Cost Tracking and Token Analytics
Both tools track token usage and cost. Helicone is particularly strong at cost analytics: the dashboard shows cost per model, cost trends over time, cost per user (via custom properties), and cost per endpoint. For teams trying to understand LLM cost at scale and identify optimisation opportunities, Helicone's cost dashboard is clean and actionable. LangSmith tracks token counts and cost within traces, which lets you see the cost breakdown for each step of a complex chain, but the cost analytics dashboard is less polished as a standalone financial monitoring tool. If your primary concern is managing LLM cost at scale rather than debugging complex agent traces, Helicone's financial analytics are more immediately useful. If you need to understand which step in a 12-step RAG pipeline is burning tokens, LangSmith's per-step cost breakdown within traces is more powerful.
Evaluation and Prompt Management
LangSmith's evaluation capabilities are substantially more developed than Helicone's. LangSmith lets you create datasets from real production traces, define evaluators (rule-based checks, embedding similarity, or LLM-as-judge scoring), and run evaluation pipelines to compare prompt versions systematically. The annotation queue allows human reviewers to score outputs and build labelled datasets. The prompt hub lets you version and manage prompts with comments and history. For teams with serious prompt engineering workflows and ongoing evaluation needs, LangSmith provides infrastructure that would otherwise require custom tooling. Helicone has added basic evaluation features but they are less mature. Helicone's strength is in operational monitoring (is the system working, is it fast, is it cheap) rather than quality evaluation (are the outputs good). For production teams where output quality is a primary concern and evaluation is a regular workflow, LangSmith is the more appropriate tool.
When Helicone Is the Right Choice
Helicone is the right choice when you need immediate observability with zero integration overhead. If your codebase makes raw OpenAI or Anthropic API calls and you do not want to add LangChain or a new SDK, Helicone works in minutes. It is also the better choice for teams monitoring cost at scale: the cost analytics dashboard and caching features can materially reduce your LLM spend. For small product teams where evaluation pipelines are not yet a priority and the primary goal is knowing when something breaks and how much it costs, Helicone's simplicity is an advantage. Helicone's provider-agnostic proxy model is useful if you are testing multiple LLM providers and want unified logging across all of them. GDPR note: Helicone logs all prompt and completion content through their infrastructure. Review their data processing terms and consider whether your prompt content includes personal data that requires a formal DPA.
Verdict
The choice between LangSmith and Helicone tracks closely with your technical stack and your monitoring maturity. If you are building with LangChain or LangGraph and need deep trace inspection for debugging complex agent workflows, plus evaluation pipelines for ongoing quality measurement, LangSmith is the purpose-built tool. The integration overhead is worth it for teams where output quality and agent reliability are critical. If you are making direct LLM API calls, want observability in place within an hour, and your primary monitoring goals are cost tracking and failure alerting, Helicone delivers more value per unit of setup effort. Many production teams end up using both: Helicone for quick financial and operational monitoring, and LangSmith for deeper evaluation work on specific prompt workflows. At SpeedMVPs, we configure LLM monitoring as part of every AI MVP delivery, selecting the tool based on the client's stack and operational requirements.