Helicone Alternatives (2026): 9 LLM Tracing and Eval Tools

Helicone describes itself as an OpenAI-compatible AI gateway with logging, observability and fallbacks built in, and you set it up by changing your base URL so every request passes through it. That design is why people like it, and it's also why leaving takes more thought than swapping one dashboard for another.

The nine tools below are listed in no particular order. None of them is linked, none is ranked, and Helicone itself isn't one of the rows, because this page is for teams on their way out.

Gateway or tracing: which half of Helicone are you replacing?

Helicone logs by sitting in the request path. You point your OpenAI-style base URL at its gateway, so routing, caching and logging all happen in one place. Most of the tools on this page work the other way round. They're tracing and evaluation platforms you wire in through an SDK or OpenTelemetry, and they watch your calls rather than carry them.

So leaving Helicone can mean replacing two jobs, not one. If you relied on its fallbacks and caching, you'll either need a tool that also offers a gateway or a separate gateway sitting in front of your new tracing tool. If you only ever opened Helicone to read logs, a tracing tool on its own may be all you need.

It's also worth checking who owns a tool today, because this corner of AI software changes hands quickly. Helicone's own homepage now says it's no longer part of Mintlify, and Langfuse says it has joined ClickHouse. Neither stops you using them, but ownership shapes a roadmap, so ask before you commit.

Tracing, evals, prompt work or routing: four starting points

If you want a gateway back. Orq.ai is the one here that pairs an AI gateway with observability and governance, and it describes itself as EU-built, which matters to some teams for data handling. It's aimed at enterprise AI rather than a weekend project.

If tracing is the main job. LangSmith comes from the LangChain team and sits next to their evaluation and deployment products, so it's the natural home if your agents are already built with LangChain. Latitude is open source and pitches what it calls self-healing agents: trace in production, find the failures, then act on them. OpenLIT is built on OpenTelemetry, so the traces you send it follow an open standard rather than one vendor's format.

If evals matter as much as logs. Braintrust splits its product into Observe, Evaluate and Discover, with production traces feeding the evals. W&B Weave comes from Weights & Biases and lists traces, evaluations, a playground and monitors as its four parts, which feels familiar if your team already tracks experiments there.

If prompts are the moving part. PromptLayer starts from prompt management, with a visual editor so people outside engineering can version and test prompts without a redeploy. Lunary pairs observability and prompt management with chatbot analytics, which helps when your product is a chat interface.

Nine Helicone replacements side by side

Tool	Main focus	Good for
Braintrust	Observability plus evals	Feeding production traces into evals to catch regressions
Langfuse	Open source tracing and evals	Self-hosted tracing and evaluation for AI agents
LangSmith	Tracing from the LangChain team	Agents built with the LangChain family of tools
Latitude	Open source agent monitoring	Finding and fixing agent failures in production
Lunary	Observability and chatbot analytics	Chat products that need user-side analytics
OpenLIT	OpenTelemetry-native, open source	Standard OpenTelemetry traces you can self-host
Orq.ai	Gateway plus governance	Keeping a gateway alongside observability and governance
PromptLayer	Prompt management first	Letting non-engineers version and test prompts
W&B Weave	Tracing from an ML platform	Teams already using Weights & Biases

Where each replacement fits

Braintrust

Best for: Teams who want tracing and evals in one place, with production traces feeding evaluations so regressions are caught before they reach users (vendor claim).

Langfuse

Best for: Engineers who want an open platform to trace and evaluate agents using production data, self-hosted with Docker, Kubernetes or plain virtual machines.

LangSmith

Best for: LangChain users who want agent tracing and real-time monitoring, with evaluation, deployment and a gateway product sold alongside it.

Latitude

Best for: Product teams running agents in production who want open source monitoring that traces runs, finds failures and helps act on them (vendor claim).

Lunary

Best for: Builders of chatbots who want observability and prompt management plus analytics showing how users experience the bot (vendor claim).

OpenLIT

Best for: Teams already on OpenTelemetry who want observability, evals, guardrails and prompt management, free to self-host under the Apache licence (vendor claim).

Orq.ai

Best for: Enterprises that want an AI gateway, observability and governance for LLM and MCP calls in one EU-built control plane (vendor claim).

PromptLayer

Best for: Product teams where domain experts edit prompts, with versioning, evals and agent tracing, and a visual editor that avoids an app redeploy (vendor claim).

W&B Weave

Best for: Machine learning teams on Weights & Biases who want traces, evaluations, a playground and monitors for AI apps and production agents.

My advice before you switch away from Helicone

Write down what you opened Helicone for over the past few weeks. If the answer is mostly logs and debugging, shortlist two tracing tools and run them side by side on the same service for a week. If you leaned on routing, fallbacks or caching, keep a gateway in your plan from day one, because a tracing tool on its own won't take over that job.

Self-hosting is the other fork. Several tools here are open source, which helps if your prompts and outputs can't leave your own servers, but it also means you're the one keeping it running. For the wider picture, my guide to LLM hosting platforms covers where the models themselves can run, and my AI agent evaluation rubric template gives you a way to score agent output once your new traces are flowing.

Questions to put to each vendor before you sign

• Does it sit in the request path, or does it only receive traces from an SDK?
• Can it read OpenTelemetry traces, so you're not locked into one format?
• Can you self-host it, and what's left out if you do?
• How do prompt versions, datasets and eval results export if you leave later?
• Who owns the company today, and has that changed recently?

Get the answers in writing. Two tools that look alike in a demo can behave very differently once they're holding a few months of your production data.

Do you make LLM observability or evaluation software? Tool makers can ask to be considered for a featured place on this list.

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Frequently asked questions

Is there an open source alternative to Helicone?

Yes, several. Some tools on this list are open source and can be self-hosted, which keeps prompts and outputs on your own servers. Check what each project leaves out of the self-hosted version before you plan around it.

Do I need an AI gateway if I switch to a tracing tool?

Only if you relied on routing, fallbacks or caching. A tracing tool watches your model calls; it doesn't carry them. If you need both jobs, keep a gateway in front or pick a platform that offers one.

What's the difference between LLM observability and LLM evaluation?

Observability shows what happened in production: traces, errors and latency. Evaluation tests outputs against cases you define, so you catch regressions before users do. Many platforms now do both, with production traces feeding the eval sets.

How hard is it to move from Helicone to another tool?

Switching the tracing itself is usually an SDK change and some configuration. The harder part is history: past logs and request data may not move across, so export what you need before you turn the old setup off.

Can I run two observability tools at the same time?

Yes, and it's a sensible way to compare them. OpenTelemetry-based setups make it easier to send the same traces to more than one place while you trial a replacement.