Helicone is great for quick setup, but falls short on evals, issue tracking, and agent support. Here are the 6 best alternatives for production LLM monitoring.
César Miguelañez
Helicone is one of the easiest ways to start logging LLM calls. Change one line of code, point your API base URL at their proxy, and you're collecting traces in under 30 minutes. For teams that need cost visibility and basic request logging fast, it genuinely delivers.
But at some point, "I can see my requests" stops being enough.
Production AI teams eventually hit the same wall: they need to know why something went wrong, not just that it happened. They need evaluations that reflect real user issues, not synthetic benchmarks. They need to track multi-turn agent flows without losing context across steps. And they need a system that tells them what's about to break — not just what already did.
That's where Helicone starts to show its limits.
This page covers the six best Helicone alternatives in 2025, what each one does well, where each falls short, and which one fits your situation.Why Teams Look for Helicone Alternatives
Helicone's proxy-based architecture is its biggest strength and its biggest constraint.
The proxy adds latency. Helicone routes every LLM request through their infrastructure (Cloudflare Workers) before it reaches OpenAI, Anthropic, or whichever provider you're using. They report an average overhead of 50–80ms. For most use cases that's acceptable, but for latency-sensitive applications — real-time voice, streaming chat, high-frequency agents — it's a real cost. And it's a cost you pay on every single request, not just when something goes wrong.
Evaluations are shallow. Helicone has a "Scores" feature that lets you attach numeric ratings to requests. But there's no mechanism to auto-generate evaluations from production issues, no eval quality measurement, and no way to understand whether your eval suite actually covers the problems your users are experiencing. You're essentially building a manual scoring system from scratch.
No issue tracking. When a request fails or produces a bad output, Helicone logs it. But there's no concept of an "issue" — no state machine, no lifecycle, no way to track whether a problem was resolved or regressed. You're left correlating logs manually.
Limited agent and multi-turn support. Helicone has session tracing, but complex multi-turn agent workflows — where context spans dozens of steps across multiple models — are difficult to debug. The platform wasn't designed around agent observability as a first-class concern.
It's a monitoring layer, not an observability platform. This isn't a criticism — it's a design choice. Helicone is optimized for fast setup and cost tracking. Teams that need deeper production observability tend to outgrow it.
What to Look for in a Helicone Alternative
Before comparing tools, here's the criteria framework that matters for production teams:
Issue tracking and lifecycle management
Can the tool detect problems, create issues, track their state (open → investigating → resolved), and alert you to regressions? Or does it just log?
Evaluation quality and coverage
Does the platform auto-generate evals from real production data? Can it measure whether your eval suite actually covers your active issues? Manual scoring doesn't scale.
Agent and multi-turn support
If you're running agents with tool calls, memory, and multi-step reasoning, can the platform trace the full execution graph? Can it pinpoint which step in a 40-step agent chain caused a failure?
Architecture and latency
Proxy-based tools add latency to every request. SDK-based tools add latency only during instrumentation. For high-throughput or latency-sensitive applications, this matters.
Pricing model
Some tools charge per seat, some per trace, some per eval token. At scale, the pricing model matters as much as the base price.
The 6 Best Helicone Alternatives
Detailed Breakdown
1. Latitude — Best for Issue-Driven LLM Observability
Website: latitude.so
Latitude is built around a different premise than most LLM monitoring tools: the goal isn't just to collect traces, it's to surface what will break next.
The platform is issue-centric. When something goes wrong in production — a bad output, a hallucination, a failed tool call — you annotate it, and that annotation creates an issue. Issues have states (open, investigating, resolved) tracked end-to-end. When you fix something, Latitude tells you whether the fix actually held or whether the problem regressed.
The most technically distinctive feature is GEPA (Generative Evaluation from Production Annotations) — an algorithm that automatically generates evaluations from real production issues. Instead of writing evals against synthetic benchmarks that may not reflect what your users actually experience, GEPA derives evals from the problems you've already seen. This means your eval suite stays grounded in reality as your application evolves.
Latitude also measures eval quality using the MCC (Matthews Correlation Coefficient) alignment metric — a statistical measure of how well your evals correlate with real human judgments. No other tool in this list does this. You can have 100 evals and still have a weak eval suite if they're all measuring the same thing or missing your most common failure modes. Latitude's eval suite metrics — including % coverage of active issues and a composite score — tell you whether your evals are actually useful.
Multi-turn agent support is strong. Latitude traces the full execution graph of complex agent workflows, including tool calls, memory reads, and model handoffs. You can see exactly which step in a multi-step agent chain produced a bad output.
Pricing:
Free: $0 — 5K traces/month, 500 trace scans, 50M eval tokens, 7-day retention
Team: $299/month — 200K traces, 20K trace scans, 500M eval tokens, 90-day retention
Enterprise: Custom
Where it fits: Teams running AI in production who need more than logs — specifically, teams who want to know what will break next, not just what broke yesterday. The $299/month Team plan is positioned for engineering teams that have moved past the "does it work?" phase and into "how do we keep it working?"
Where it's not the right fit: If you just need cost tracking and basic request logging for a side project, the free tier covers you, but Latitude's depth may be more than you need at that stage.
2. Langfuse — Best Open-Source Option
Website: langfuse.com
Langfuse is the most popular open-source LLM observability platform, with over 40,000 builders using it. It's SDK-based (no proxy), which means zero added latency to your LLM calls — a meaningful advantage over Helicone for latency-sensitive applications.
The tracing is genuinely good. Langfuse handles complex nested traces, session tracking, and multi-turn conversations well. The UI is clean and the data model is flexible enough to accommodate most LLM application architectures.
Evaluations exist but are manual. You can set up LLM-as-judge evaluators, human annotation queues, and custom scoring — but there's no auto-generation of evals from production issues. You have to decide what to evaluate and build the pipeline yourself.
There's no issue tracking. Langfuse is an observability and tracing tool, not an issue management system. When you find a problem, you're on your own for tracking it to resolution.
Pricing (Cloud):
Hobby: Free — 50K units/month, 30-day data access, 2 users
Core: $29/month — 100K units/month, 90-day data access, unlimited users
Pro: $199/month — 100K units/month, 3-year data access, high rate limits
Enterprise: $2,499/month
Self-hosting is free and well-documented — a significant advantage for teams with data residency requirements.
Where it fits: Teams that want open-source, self-hostable tracing with solid SDK integrations. Particularly good for teams that don't want to route traffic through a third-party proxy and need to keep data on their own infrastructure.
Where it's not the right fit: Teams that need automated eval generation or issue lifecycle management will need to build those workflows themselves on top of Langfuse.
3. LangSmith — Best for LangChain-Heavy Teams
Website: smith.langchain.com
LangSmith is LangChain's observability platform. If your application is built on LangChain or LangGraph, LangSmith has the deepest native integration — traces map directly to LangChain abstractions, and debugging a LangGraph agent in LangSmith is significantly easier than in any other tool.
Outside the LangChain ecosystem, the value proposition weakens. LangSmith requires SDK integration (no proxy option), and the UI can feel complex for teams not already familiar with LangChain's mental model.
Evaluations are available — you can run LLM-as-judge evals, build datasets, and set up annotation queues for human feedback. The evaluation tooling is more mature than Helicone's but still manual. There's no auto-generation from production issues.
Pricing:
Developer: Free — 5K base traces/month, 1 seat
Plus: $39/seat/month — 10K base traces/month, unlimited seats
Enterprise: Custom
Where it fits: Teams building on LangChain or LangGraph who want native tracing without extra configuration. The $39/seat pricing is reasonable for small teams.
Where it's not the right fit: Teams not using LangChain will find the integration overhead high relative to the benefit. And like most tools here, there's no issue tracking or auto-generated evals.
4. Braintrust — Best for Evaluation-First Teams
Website: braintrustdata.com
Braintrust is the most evaluation-focused tool on this list. If your primary concern is running rigorous offline evals — building datasets, running experiments, comparing model versions — Braintrust is purpose-built for that workflow.
The platform supports LLM-as-judge scoring, custom code scorers, human annotation, and experiment tracking. The "Loop" agent can autonomously run evaluations and iterate on prompts. For teams doing systematic prompt engineering and model comparison, it's a strong choice.
Where Braintrust is weaker: production monitoring. It's primarily an offline evaluation platform. Real-time monitoring, issue tracking, and the kind of "what's breaking right now in production" visibility that Helicone provides are not Braintrust's focus.
Pricing:
Starter: Free — 1 GB processed data, 10K scores, 14-day retention
Pro: $249/month — 5 GB processed data, 50K scores, 30-day retention
Enterprise: Custom
Where it fits: Teams that have a clear eval-first culture and want a dedicated platform for running experiments and measuring model quality offline. Good complement to a separate production monitoring tool.
Where it's not the right fit: Teams looking for a single platform that handles both production monitoring and evaluation. Braintrust doesn't replace a monitoring layer.
5. Arize AI — Best for Enterprise ML + LLM Observability
Website: arize.com
Arize started as an ML observability platform and has expanded into LLM monitoring. If you're running both traditional ML models and LLM applications, Arize gives you a unified platform — a meaningful advantage for larger ML teams that don't want to manage separate tools.
The evaluation capabilities are advanced, including online evals, custom metrics, and monitors. The tracing supports agent graphs and multi-agent workflows. The platform is OpenTelemetry-compatible, which makes integration with existing observability stacks straightforward.
The tradeoff is complexity. Arize is built for enterprise ML teams, and the setup and configuration reflect that. For teams that just want LLM monitoring, it can feel like more platform than they need.
Pricing:
Phoenix (self-hosted, open-source): Free
AX Free (SaaS): Free — 25K spans/month, 15-day retention
AX Pro: $50/month — 50K spans/month, 30-day retention
AX Enterprise: Custom
Where it fits: Enterprise teams running a mix of traditional ML and LLM applications who want a single observability platform. Also good for teams with existing OpenTelemetry infrastructure.
Where it's not the right fit: Smaller teams or pure-LLM teams who don't need the ML observability layer. The complexity overhead isn't worth it if you're only monitoring LLM applications.
6. Weights & Biases (W&B) — Best for ML Experiment Tracking + LLM Monitoring
Website: wandb.ai
W&B is the dominant platform for ML experiment tracking, and it has added LLM tracing and evaluation capabilities. If your team already uses W&B for model training experiments, adding LLM monitoring is a natural extension — you get a unified view of your ML development lifecycle.
The LLM-specific features include tracing, evaluation scoring, and prompt management. The experiment tracking is best-in-class for comparing model versions and training runs. The integration with the broader ML ecosystem (datasets, artifacts, model registry) is unmatched.
The LLM observability features are less mature than dedicated LLM tools. W&B is primarily an ML development platform that has added LLM support, not an LLM-first observability platform. Production monitoring depth is limited compared to Langfuse or Latitude.
Pricing:
Free: $0 — unlimited personal use (no corporate use)
Pro: Starts at $60/month — unlimited teams, team-based access controls
Enterprise: Custom
Where it fits: Teams already using W&B for ML training who want to extend their existing tooling to cover LLM applications. Strong for teams doing active model development alongside LLM deployment.
Where it's not the right fit: Teams that don't have an existing W&B investment and are looking for a dedicated LLM observability platform. The LLM features alone don't justify the switch.
Recommendation by Use Case
You need quick setup and cost tracking, and Helicone is working fine:
Stay on Helicone. It's genuinely good at what it does. The proxy setup is fast, cost tracking is accurate, and caching can reduce API costs by 20–30%. If you're early-stage and just need visibility into spend and basic request logs, don't over-engineer it.
You need production observability with issue tracking and auto-generated evals:
Use Latitude. It's the only tool on this list that closes the loop between production issues and evaluations — automatically generating evals from real problems, measuring eval quality, and tracking issues through resolution. If you're asking "what will break next?", Latitude is built to answer that question.
You need open-source, self-hosted tracing with no proxy overhead:
Use Langfuse. The self-hosting story is mature, the SDK integrations are broad, and the pricing is the most accessible on this list for production use. You'll need to build your own eval pipeline, but the tracing foundation is solid.
Your stack is built on LangChain or LangGraph:
Use LangSmith. The native integration depth is worth it if you're already in the LangChain ecosystem. Don't fight the tooling.
Your primary concern is rigorous offline evaluation and experiment tracking:
Use Braintrust. It's the most evaluation-focused platform on this list. Pair it with a separate production monitoring tool if you need real-time visibility.
You're an enterprise team running both traditional ML and LLM applications:
Use Arize AI. The unified ML + LLM observability platform is a real advantage at scale, and the OpenTelemetry compatibility makes it easier to integrate with existing infrastructure.
You're already using W&B for ML training:
Extend to W&B's LLM features. The unified development lifecycle view is worth more than switching to a dedicated LLM tool if you're already invested in the platform.
The Bottom Line
Helicone is a good starting point. It's fast to set up, honest about what it does, and genuinely useful for cost tracking and basic monitoring. The proxy architecture is a real tradeoff, but for many teams it's an acceptable one.
The question is what you need beyond that.
If you need to understand why things go wrong, track issues through resolution, and build evaluations that actually reflect your production failure modes — not synthetic benchmarks — then you need a platform designed around that workflow. That's what Latitude is built for.
Regression testing tells you what broke in the past. Latitude tells you what will break next.
Frequently Asked Questions
What is Helicone and what does it do?
Helicone is an open-source LLM observability platform that works as a proxy between your application and LLM providers like OpenAI and Anthropic. It provides request logging, cost tracking, caching, rate limiting, and basic prompt management. Setup requires changing one line of code (your API base URL). It's particularly strong for teams that need fast setup and cost visibility.
What are the main limitations of Helicone?
Helicone's proxy-based architecture adds 50–80ms of latency to every LLM request. It has limited evaluation capabilities (manual scoring only, no auto-generated evals), no issue tracking or lifecycle management, and limited support for complex multi-turn agent workflows. It's a monitoring layer rather than a full observability platform.
How much does Helicone cost?
Helicone's Hobby plan is free with 10,000 requests/month and 7-day data retention. The Pro plan is $79/month with unlimited seats and 1-month retention. The Team plan is $799/month with 5 organizations and SOC-2/HIPAA compliance. Enterprise pricing is custom.
What's the best Helicone alternative for teams running AI agents?
Latitude has the strongest multi-turn agent support among the alternatives listed here. It traces full agent execution graphs including tool calls, memory reads, and model handoffs, and can pinpoint which step in a complex agent chain produced a failure. Langfuse and LangSmith also have solid agent tracing capabilities.
What's the best open-source Helicone alternative?
Langfuse is the most mature open-source option, with 40,000+ builders using it and a well-documented self-hosting path. Arize Phoenix is also open-source and free to self-host. Both avoid the proxy architecture, adding zero latency to LLM requests.
Which Helicone alternative has the best evaluation capabilities?
Latitude is the only platform that auto-generates evaluations from real production issues using its GEPA algorithm, and the only one that measures eval quality with the MCC alignment metric. For teams that want rigorous offline evaluation workflows, Braintrust is the most evaluation-focused option. LangSmith and Langfuse both support manual LLM-as-judge evals.
Is Helicone suitable for production use?
Helicone works in production and has processed over 2 billion LLM interactions. The main considerations for production use are the proxy latency (50–80ms per request), the limited evaluation depth, and the absence of issue tracking. Teams with latency-sensitive applications or those needing deep observability often move to SDK-based alternatives as they scale.
