Helicone vs Portkey vs LangSmith: LLM Observability Tools Compared

If you are running LLM-powered applications in production and you are not observability-platform-aware, you are flying blind. You do not know what your models are returning under load, whether latency is degrading, whether specific prompt variations are triggering hallucinations, or how much you are spending per conversation. The stakes are not abstract: a hallucinating production chatbot can ship incorrect code, wrong medical advice, or fabricated legal citations before anyone catches it.

Three platforms have emerged as the leading solutions for LLM-native observability: Helicone, Portkey, and LangSmith. Each approaches the problem differently. Each has a distinct sweet spot. This guide tears them apart feature by feature so you can pick the right one for your stack, your team, and your budget.

What These Platforms Solve

Before the comparison, let us be precise about the problem space. LLM observability is not the same as traditional API monitoring. A language model call is stateful in ways that a REST endpoint is not: context windows change size dynamically, token consumption is a cost driver, outputs can be non-deterministic even with the same inputs, and failures manifest as confident hallucinations rather than HTTP 500 errors.

Effective LLM observability covers five dimensions:

1. Request tracing — capturing every LLM call, its inputs, outputs, latency, and metadata
2. Prompt and response monitoring — tracking what is being sent to models and what comes back
3. Cost tracking — measuring spend at the request, user, or feature level
4. Evaluation and quality — detecting regressions in output quality over time
5. Gatekeeping and safety — catching PII leakage, prompt injection, and hallucinations before they reach users

The three platforms each cover these dimensions with different depth.

Helicone — The Lightweight Proxy for Request-Level Visibility

Helicone takes the simplest approach of the three: you point your LLM calls through Helicone's proxy and it captures everything automatically. No SDK required. No instrumentation code to write. You just change your base URL from api.openai.com to oai.helicone.ai (or the equivalent for your provider) and Helicone captures request logs, latency, token usage, and custom metadata.

Helicone's core strengths are its zero-integration setup and its focus on request-level transparency. It is the fastest platform to get running — most teams are live within five minutes.

Key Features

• Proxy-based tracing — works with any OpenAI-compatible API, Anthropic, Azure OpenAI, and custom endpoints
• Request logging — every call logged with full prompt, response, latency, model, and token count
• Custom properties — tag requests with user ID, session ID, feature name, or any custom metadata via HTTP headers
• Rate limiting and retry handling — built-in retry logic and rate limiting at the proxy layer
• Cache analytics — see which requests hit cache and how much that saves in tokens and cost
• Open source — the proxy is open source, so you can self-host if you do not want to use the cloud offering

Where Helicone Falls Short

Helicone is purpose-built around request tracing and does not attempt to be an evaluation or guardrails platform. If you need automated regression testing, structured output validation, or PII detection built into the platform, you will need to layer in other tooling. Helicone tells you what happened — it does not evaluate whether the output was correct.

Additionally, Helicone's integrations with external dashboards (Grafana, Datadog) require exporting data via webhook or API rather than offering native connectors out of the box.

Pricing

Helicone offers a generous free tier covering 100,000 requests per month. Paid plans start at $59/month for 1 million requests. There is no per-seat pricing — the model is purely volume-based.

Portkey — The AI Gateway with Observability Built In

Portkey positions itself as an AI gateway first and an observability platform second. Where Helicone is a logging proxy, Portkey is a full routing layer with built-in monitoring, caching, retries, and fallback logic. If you are running a multi-model stack — routing between GPT-4, Claude, and open-source models like Llama — Portkey is designed for that.

Portkey's observability is more structured than Helicone's. It uses OpenTelemetry traces as its backbone, making it naturally compatible with the broader observability ecosystem (Grafana, Jaeger, Datadog). It also ships with a pre-built dashboard that gives you cost, latency, and error-rate metrics out of the box.

Key Features

• AI gateway with fallback routing — route between multiple LLM providers with automatic failover
• OpenTelemetry-native tracing — fully OTEL-compatible, integrates with Grafana, Jaeger, and Datadog without custom exporters
• Virtual keys — manage API keys per user or feature, track spend at a granular level
• Built-in caching — semantic caching at the gateway level to reduce token costs
• Prompt management — version and manage prompts centrally with A/B testing support
• Guardrails — PII detection, content filtering, and refusal detection baked into the gateway
• Strict schema enforcement — validate structured outputs (JSON mode) against schemas
• 40+ integrations — including LangChain, LlamaIndex, AWS Bedrock, Azure OpenAI, and more

Where Portkey Falls Short

Portkey's breadth can be its drawback. For teams that just want simple request logging and do not need a gateway, the platform can feel heavyweight. The free tier is limited compared to Helicone, and some of the more advanced features (like semantic caching and detailed analytics) require paid plans. The onboarding is also more involved — you are configuring a gateway, not just changing a base URL.

Portkey's evaluation features are lighter than LangSmith's. It does not have built-in LLM-as-judge evaluation pipelines or regression test suites. Those capabilities would need to be layered in via PromptLayer, Braintrust, or internal tooling.

Pricing

Portkey's free tier covers 100,000 requests per month with basic analytics. Paid plans start at $50/month for 500,000 requests and unlock advanced analytics, caching, and priority support. The gateway routing features are available on all paid plans.

LangSmith — The Evaluation-First Platform for LangChain Users

LangSmith is built by the team behind LangChain, and it shows. If your application is built on LangChain, LangSmith is the most deeply integrated option — you get tracing that understands LangChain chains, nodes, and tools at a semantic level, not just as HTTP calls.

Where Helicone and Portkey are primarily observability tools with some evaluation features, LangSmith is fundamentally an evaluation platform with observability bolted on. Its standout capabilities are in monitoring output quality over time, running automated regression tests against prompt changes, and measuring retrieval quality in RAG pipelines.

Key Features

• Deep LangChain integration — traces understand chain topology, tool calls, intermediate steps, and retrieval nodes
• Automated evaluation — LLM-as-judge, heuristic evaluators, and regression test suites that run against every prompt or retrieval change
• RAG evaluation — measure retrieval precision, answer faithfulness, and answer relevance with built-in RAGAS metrics
• Dataset management — create and manage test datasets with expected outputs for regression testing
• Online evaluation — run evaluators on production traffic in real time, not just offline test runs
• Collaborative debugging — share trace links with teammates, annotate runs, and leave comments
• LangChain SDK native — zero-configuration tracing for LangChain Python and JS applications

Where LangSmith Falls Short

LangSmith is the weakest choice if you are not using LangChain. Its integration story for non-LangChain applications is present (OpenAI API compatible tracing, LangChain Expressions parser) but not as frictionless as Helicone's proxy approach. Teams running custom LLM stacks or serving models via vLLM will find LangSmith less natural to adopt.

LangSmith also lacks the gateway features that make Portkey compelling for multi-model routing and failover. You cannot use LangSmith as an intelligent routing layer out of the box.

The pricing is also the least transparent of the three — while there is a free tier with limited traces per month, the enterprise pricing is custom and requires a sales conversation.

Pricing

LangSmith's free tier covers 5,000 traced runs per month with basic evaluation. The Plus plan at $39/month per seat unlocks unlimited traces, full evaluation capabilities, and dataset management. Enterprise pricing is custom.

Head-to-Head Comparison

The table below summarizes how the three platforms stack up across the dimensions that matter most for production LLM applications.

Choosing the Right Platform

There is no single right answer for every team. Here is a framework for the decision:

Start with Helicone if...

You want the fastest path to visibility without changing your application architecture. If you are running a single-model application (or just using OpenAI's API), Helicone's proxy swap takes five minutes and gives you full request logs, token counts, latency tracking, and cache analytics. It is also the best choice if you want to self-host — the open-source proxy means you retain full control of your data. Read our vLLM production monitoring guide for how to pair Helicone with self-hosted model serving.

Choose Portkey if...

You are running a multi-model stack and you want observability plus routing in one platform. If you are routing between GPT-4, Claude, and a self-hosted Llama endpoint with automatic fallback on failures, Portkey's gateway model is purpose-built for that. Its OpenTelemetry-native tracing also makes it the best fit if you are already invested in the Grafana or Jaeger ecosystem and want LLM traces flowing into the same dashboards as your microservices.

Choose LangSmith if...

You are building on LangChain and you care deeply about output quality and regression detection. LangSmith's evaluation framework — particularly its LLM-as-judge evaluator, RAGAS retrieval metrics, and online evaluation on production traffic — is deeper than what either Helicone or Portkey offers. If you are building a RAG system and you need to measure whether retrieval quality is degrading as your vector database grows, LangSmith's RAG evaluation is the most mature offering in this comparison. Pair it with our RAG observability guide for a complete picture.

Can You Use More Than One?

Yes — and many production teams do. The common pattern is using Portkey as the gateway and routing layer while also exporting traces to LangSmith for evaluation, or pairing Helicone's request logging with an external evaluation tool like Braintrust or Promptfoo. These platforms are not mutually exclusive: Helicone gives you the cheapest, fastest request logging; Portkey gives you the most robust gateway infrastructure; LangSmith gives you the deepest evaluation story.

The trade-off is operational complexity. Each additional platform is another system to configure, monitor, and pay for. For most teams, starting with one platform and growing into a second as needs mature is the right approach.

What About Guardrails and Hallucination Detection?

None of these three platforms has a comprehensive hallucination detection system built in. Hallucination detection requires a different approach: structured output validation, ground-truth comparison, or attribution tracking against a retrieval corpus. For a complete production hallucination monitoring stack, you will need to layer in dedicated tooling alongside whichever observability platform you choose. Our complete guide to hallucination detection in production covers the four-layer detection architecture used by teams running AI in high-stakes environments.

Conclusion

The LLM observability landscape is still young, and each of these three platforms is converging on the same problem space from a different angle. Helicone wins on simplicity and speed-to-value. Portkey wins for teams that need a production-grade AI gateway with observability baked in. LangSmith wins for LangChain-centric teams that need deep evaluation and regression testing. If you are just starting out and want to understand what is happening inside your LLM calls this week, Helicone is the lowest-friction place to start. If you are scaling a multi-model production system, Portkey is purpose-built for that. If you care more about output quality than cost per token, LangSmith's evaluation framework is the most mature option available.

Whichever platform you choose, the important thing is to start instrumenting now. You cannot optimize what you cannot measure — and in production LLM systems, the cost of flying blind is measured in user trust, API spend, and the occasional confident hallucination shipped to a customer.