Helicone vs Traceloop: Best Tools for Monitoring LLMs

If you've ever tried deploying an LLM-powered application—or if you're planning to—you'll quickly realize that observability is not optional.

LLMs can be unpredictable, sometimes generating unexpected responses, failing silently, or consuming more resources than anticipated. To maintain performance, debug issues, and optimize costs, you need a robust observability tool.

Helicone and OpenLLMetry by Traceloop are two of the top open-source tools for LLM observability. Each offers distinct features and integrations designed to help developers monitor, analyze, and refine their AI applications.

This article evaluates both tools based on their strengths, integrations, and performance to help you decide which is the best fit for your LLM workflows.

Helicone vs. Traceloop At a Glance

Platform

LLM Monitoring

Security, Compliance, and Privacy

TL;DR

• Traceloop is built for code-first users needing a standard toolset for evaluation and tracing with minimal need for third-party integrations and customizations. It also integrates more easily with OpenTelemetry.
• Helicone offers more end-to-end observability features like deeper analytics, security features, and flexible integrations for tracing, monitoring, and cost management. Available via code or the UI.

Helicone: Powerful Observability With a Developer-Friendly Experience

Best for: Simple integration, detailed cost tracking, and advanced prompt experimentation.

Helicone is an open-source observability platform designed to help developers monitor, debug, and optimize LLM applications.

It provides real-time tracking, logging, and advanced analytics to improve the performance and cost-efficiency of AI-powered workflows.

Key Features

• 1-Line Integration: Helicone's simple integration via proxy or async logging allows developers to use any LLM API providers and switch between them without changing code. See docs.

• Built-in Caching: Reduces API costs and latency by caching frequent requests.

• Prompt Management: Easily version and test prompts with Helicone's prompt management features.

• Experiments & Evals: Test prompt variations against historical conversations, then evaluate prompts with LLM-as-judge or custom Python evaluators—all via the UI.

• Alerting & Webhooks: Get notified about cost anomalies, request failures, and system issues.

• Comprehensive Analytics: Provides detailed insights into user interactions, performance metrics, and cost analysis.

Why Developers Choose Helicone

• Ease of Use: The minimal setup process accelerates deployment and reduces integration overhead—also makes it easier for non-coders to get involved.

• Cost Efficiency: Supports features like caching and detailed cost tracking out-of-the-box help in optimizing expenses associated with LLM usage.

• Enhanced Security: Built-in protections ensure the robustness of your LLM applications against common vulnerabilities.

• Broad Compatibility: Works with OpenAI, Anthropic, Google Gemini, xAI, and more. Supports various vector databases and AI providers.

Sample Integration

Here's how simple it is to integrate Helicone with OpenAI:

import OpenAI from "openai";

const openai = new OpenAI({
  apiKey: process.env.OPENAI_API_KEY,
  baseURL: "https://oai.helicone.ai/v1",
  defaultHeaders: {
    "Helicone-Auth": `Bearer ${process.env.HELICONE_API_KEY}`,
  },
});

Traceloop: Comprehensive Code-First Observability

Best for: Code-first teams needing a capable toolset for evaluation and tracing.

Traceloop is an open-source observability tool built by Traceloop that specializes in execution tracing for LLM applications.

It focuses mainly on evaluation, and aims to help teams detect hallucinations, incorrect answers, and sensitive information leaks automatically.

Key Features

• Execution Tracing: Provides detailed tracing for every request, aiding in debugging and performance monitoring.

• Prompt Management: Offers a prompt registry and management tools to track prompt versions and changes and simplify deployment.

• Gradual Rollouts: Supports phased deployment of model and prompt updates to minimize potential disruptions.

• Hub: A Rust-based Proxy serving as a centralized tracing solution across multiple LLM provider calls.

Why Developers Choose Traceloop

• In-Depth Tracing: Execution tracing for every request facilitates thorough debugging and performance analysis.

• Powerful Pre-built Evaluation: Robust built-in quality assessment tools.

• Flexible Integration: Supports multiple programming languages and integrates with various observability platforms.

Comparing Features & Integrations

Here's a table comparing Helicone and Traceloop across a few key areas:

Which LLM Observability Platform is Right for You?

Both platforms offer valuable observability features, but they cater to different users and workflows.

• Choose Helicone if you need a fast, easy-to-integrate solution with built-in security, caching, and robust cost tracking.
• Choose Traceloop if you need a standard set of pre-built evaluation tools and are comfortable with SDK-based integrations.
• Choose Helicone if you need an observability tool that can be used by non-engineering teams too and has advanced features like alerting and webhook automation for better observability.
• Choose Traceloop if you’re already using OpenTelemetry, as it provides standard OpenTelemetry instrumentations for a more seamless integration.

Feel free to try out both tools before making a decision!

You might also like:

• Getting Started with Helicone (Docs)

• Helicone vs. Weights and Biases

• Helicone vs. Arize Phoenix

Frequently Asked Questions (FAQs)

1. Can I self-host Helicone and Traceloop?

Yes, both Helicone and Traceloop offer self-hosting options, allowing you to manage data on your infrastructure.

2. Which tool is easier to integrate?

Helicone provides a one-line integration via proxy or async logging, making it significantly easier to set up compared to Traceloop’s SDK-based approach.

3. Does Traceloop have built-in caching?

No, Traceloop does not have built-in caching capabilities but Helicone does, which helps reduce API costs and latency.

4. Which platform provides better security features?

Helicone integrates Prompt Armor to protect against prompt injections and adversarial attacks, whereas Traceloop does not offer any special out-of-the-box security features.

5. Can both tools track costs associated with LLM usage?

Yes. But Helicone provides detailed cost tracking and analytics, while Traceloop's cost-tracking capabilitites are more limited.

5. Which tool requires less code to use?

Helicone. Traceloop is built for code-first users, while Helicone provides an intuitive UI that allows non-coders to use most of its features—yet allowing the use of code for more fine-grained control where necessary.