LangSmith vs Langfuse vs Helicone vs Arize: LLM Observability Platforms Compared

How Each Platform Works

LangSmith

LangSmith is the observability and evaluation platform built by LangChain, the company behind the LangChain orchestration framework and LangGraph agent framework. Its primary advantage is the depth of its integration with the LangChain ecosystem. If your application is built on LangChain or LangGraph, LangSmith captures traces with zero additional instrumentation code, because the framework emits spans natively. Every chain invocation, retrieval call, tool execution, and model call is automatically traced with full content capture, token counts, and timing.

Beyond tracing, LangSmith provides a prompt management system (LangSmith Hub) for versioning and sharing prompts, an evaluation framework for running test suites against datasets, an annotation queue for human review of traces, and online evaluation rules that score production traces automatically. The platform also supports playground-style prompt testing where you can take a production trace, modify the prompt, and re-run it against the same inputs to see how the output changes.

LangSmith's pricing is seat-based, which means cost scales with team size rather than usage volume. This is advantageous for high-volume applications with small teams and disadvantageous for large organizations with many developers who need access but generate modest trace volume. LangSmith is cloud-hosted by default, with a self-hosted option available for enterprise customers.

The trade-off with LangSmith is framework lock-in. While it does accept traces from non-LangChain applications through its REST API and Python/TypeScript SDKs, the experience is significantly richer when you are using LangChain or LangGraph. Teams that use other orchestration frameworks (LlamaIndex, Semantic Kernel, Haystack) or call model APIs directly will find that competing platforms offer a more framework-neutral experience.

Langfuse

Langfuse is an open-source LLM observability platform that positions itself as the vendor-neutral alternative. Its core architecture is framework-agnostic: Langfuse provides SDKs for Python and TypeScript that work with any LLM framework or direct API calls, plus an OpenTelemetry integration that accepts standard GenAI traces from any OpenTelemetry-instrumented application. Auto-instrumentation libraries exist for LangChain, LlamaIndex, OpenAI SDK, Anthropic SDK, Vercel AI SDK, and several others.

Langfuse's feature set covers tracing with full content capture, cost tracking with per-model pricing configuration, prompt management with versioning and deployment, evaluation through both automated scorers and manual annotation, and dashboards with filtering and drill-down. The evaluation system supports LLM-as-a-judge evaluators, custom scoring functions, and human annotation workflows. Langfuse also provides a datasets feature for building and running offline evaluation suites against stored traces.

The defining characteristic of Langfuse is its deployment model. The platform is fully open-source under an MIT-like license, and you can self-host it with Docker Compose or Kubernetes. This makes it the default choice for teams with data sovereignty requirements (healthcare, finance, government) who cannot send traces to a third-party cloud. Langfuse also offers a managed cloud version for teams that prefer not to operate the infrastructure. Pricing on the cloud version is usage-based, counting traces, observations (spans), and scores.

The trade-off with Langfuse is that breadth comes at the cost of depth. Because it supports every framework equally, it does not have the deep, automatic instrumentation that LangSmith provides for LangChain. You get traces from any framework, but the level of detail depends on how well the auto-instrumentation library for that specific framework has been implemented, and some libraries are more mature than others.

Helicone

Helicone takes a fundamentally different architectural approach. Instead of requiring you to instrument your application code with an SDK, Helicone operates as a proxy that sits between your application and the LLM provider. You change one line in your configuration, pointing your API base URL from the provider's endpoint to Helicone's proxy URL, and Helicone intercepts every request, logs the full prompt and completion, records token counts and latency, computes cost, and forwards the request to the actual provider. This means you can add observability to an existing application in under a minute without modifying any application code.

The proxy architecture gives Helicone several unique advantages. Setup is the simplest of any platform. It works with any programming language or framework because it operates at the HTTP level. It can add features like prompt caching (Helicone caches identical requests to avoid duplicate API calls), rate limiting, and request retries at the proxy layer, which are operational features beyond pure observability. And because it sees every request as structured HTTP traffic, it can parse and index prompts, completions, and metadata without needing to understand your application's internal architecture.

Helicone's observability features include request logging with full content, cost tracking with automatic model pricing, latency monitoring, user tracking (by passing user IDs in headers), and basic evaluation through custom properties and scores. The dashboard provides filtering, sorting, and aggregation across all logged requests.

The trade-off with Helicone is pipeline-level visibility. Because it operates at the HTTP proxy level, it sees individual LLM API calls but not the broader context of your application pipeline. It does not model the relationship between a retrieval call, a prompt assembly step, and a generation call as a connected trace, because it only sees the generation call. For simple applications that make one or two LLM calls per request, this is fine. For complex pipelines with multiple retrieval steps, conditional branching, and tool calls, you lose the pipeline-level view that trace-based platforms provide. Helicone has been adding SDK-based tracing capabilities to address this gap, but the proxy remains its core architecture.

Arize Phoenix

Arize Phoenix is the open-source LLM observability tool from Arize AI, a company that built its reputation on traditional ML observability (monitoring model performance, detecting data drift, and explaining predictions). Phoenix inherits the rigor and depth of Arize's ML observability tooling and applies it to LLM applications, which gives it a distinctive strength in evaluation and analysis workflows.

Phoenix provides tracing with full content capture, an evaluation framework with built-in evaluators for hallucination detection, QA correctness, relevance, and toxicity, embedding visualization for understanding retrieval quality, and integration with notebook-based workflows for interactive analysis. The evaluation system is particularly deep: Phoenix provides pre-built evaluation templates that use an LLM judge to score traces, supports custom evaluation criteria, and includes statistical analysis of evaluation results that helps distinguish real quality changes from random variation.

Phoenix integrates natively with OpenTelemetry and provides auto-instrumentation for the OpenAI SDK, Anthropic SDK, LlamaIndex, LangChain, AWS Bedrock, and the OpenAI Agents SDK. It can run as a local server during development (launched with a single command in a Python notebook) or as a deployed service in production, which makes it unusually convenient for interactive debugging sessions during development.

The trade-off with Phoenix is that it is more of an analysis and evaluation tool than a full production monitoring platform. Its alerting, dashboarding, and team collaboration features are less mature than those of LangSmith or Langfuse. Teams that need production alerting, role-based access control, and polished dashboards for non-technical stakeholders may find Phoenix's interface more developer-oriented than they need. Phoenix is best paired with a separate metrics and alerting system for production monitoring, while using Phoenix for deep analysis, evaluation, and debugging.

Feature Comparison

Tracing

All four platforms support tracing, but the depth and automation vary. LangSmith provides the most automatic tracing for LangChain and LangGraph applications, capturing every internal operation without any instrumentation code. Langfuse provides broad auto-instrumentation across many frameworks through its integration ecosystem, with coverage depth varying by framework. Arize Phoenix provides solid auto-instrumentation through OpenTelemetry, particularly strong for LlamaIndex and the OpenAI SDKs. Helicone provides request-level tracing through its proxy, with SDK-based multi-step tracing as a newer addition. For teams that need to trace complex multi-step pipelines across multiple services, Langfuse and LangSmith currently offer the most mature multi-span tracing.

Evaluation

Evaluation capability is where the platforms diverge most. Arize Phoenix has the deepest evaluation primitives, inherited from traditional ML observability, with statistical rigor in how it reports and compares evaluation results. LangSmith provides a full evaluation workflow with datasets, test runs, and comparison views, tightly integrated with the LangChain ecosystem. Langfuse provides evaluation through scoring functions, LLM-as-a-judge evaluators, and annotation queues, with the advantage that all evaluation data is stored alongside traces. Helicone has the most basic evaluation capabilities, offering custom scores that can be attached to requests but without the built-in evaluator templates and statistical analysis of the other platforms.

Cost Tracking

All four platforms compute cost from token counts and model pricing. Helicone has a natural advantage here because its proxy sees every API call and its exact token usage without any instrumentation effort. LangSmith, Langfuse, and Phoenix all track cost through their tracing infrastructure, which requires instrumentation but provides the additional context of pipeline-level cost breakdown (how much of the cost came from retrieval versus generation versus tool calls). For pure cost visibility with minimal effort, Helicone is the easiest path. For cost visibility integrated with pipeline-level analysis, the SDK-based platforms provide more context.

Deployment Model

Langfuse and Arize Phoenix are fully open-source and can be self-hosted, which makes them the only options for teams that cannot send data to a third-party cloud. LangSmith is cloud-hosted by default with a self-hosted enterprise option. Helicone is primarily a cloud service with an open-source proxy component. For data sovereignty, regulated industries, and air-gapped environments, Langfuse and Phoenix are the clear choices.

Which Platform Fits Which Team

If your application is built on LangChain or LangGraph and you want the richest out-of-the-box experience with minimal configuration, LangSmith is the natural choice. Its framework integration depth is unmatched for that specific ecosystem, and the prompt management, evaluation, and annotation features are well integrated into a single platform. The cost is seat-based, which favors small teams with high traffic.

If you need framework-neutral observability, self-hosting capability, or vendor independence, Langfuse is the strongest option. Its open-source model, broad framework support, and OpenTelemetry compatibility make it the safest long-term bet for teams that want to avoid lock-in. The usage-based pricing on the cloud version means cost scales with actual usage rather than team size.

If you want the fastest possible setup with no code changes, Helicone is the right choice. The proxy architecture gets you cost tracking, request logging, and basic observability in minutes. This is ideal for prototyping, for applications with simple pipelines (one or two LLM calls per request), and for teams that want immediate visibility while they evaluate more comprehensive platforms for the long term.

If your team is evaluation-heavy, research-oriented, or working in notebooks, Arize Phoenix provides the deepest analysis and evaluation capabilities. Its ML observability heritage shows in the statistical rigor of its evaluation framework and the quality of its embedding visualization tools. Pair it with a production monitoring system for alerting and dashboards, and use Phoenix for the deep analysis work.

Many production teams use two of these platforms together. A common combination is Helicone for immediate cost visibility and request logging plus Langfuse or LangSmith for full pipeline tracing and evaluation. Another common pattern is Phoenix for development-time analysis and evaluation plus Langfuse for production monitoring and alerting. The interoperability provided by OpenTelemetry makes these combinations increasingly practical, because the same instrumentation can feed multiple backends.