API gateway and observability layer for LLM usage analytics. This analytics & monitoring provides comprehensive solutions for businesses looking to optimize their operations.
A simple dashboard that monitors your AI API usage — see costs, latency, and errors at a glance.
Helicone is an LLM observability platform built around a proxy-based architecture — you route your LLM API calls through Helicone's gateway, and it captures every request and response with zero code changes beyond swapping a base URL. This design choice is both its greatest strength and its defining constraint.
The proxy approach means integration is genuinely trivial. Change your OpenAI base URL from api.openai.com to oai.helicone.ai, add your Helicone API key as a header, and every request is instantly logged with latency, token counts, costs, and response content. No SDK to install, no decorators to add, no framework-specific integration to configure. For teams using the OpenAI SDK directly, you're operational in under five minutes.
Helicone's cost analytics are its standout feature. The dashboard shows real-time spend across models, users, and custom properties. You can set budget alerts, track cost per user or per feature, and identify which parts of your application consume the most tokens. For teams managing LLM costs across multiple products or teams, this visibility is immediately valuable.
The platform also offers request caching at the gateway level. Identical requests return cached responses, which can dramatically reduce costs for applications with repetitive queries. Rate limiting and retry logic are similarly handled at the proxy layer, offloading operational concerns from your application code.
Helicone added support for custom properties (key-value pairs attached to requests via headers), which enables segmenting analytics by user, feature, environment, or any custom dimension. This is more flexible than it sounds — you can build surprisingly detailed usage dashboards using just header-based properties.
The limitation of the proxy architecture becomes apparent with complex agent workflows. Since Helicone sees individual LLM requests passing through the gateway, it doesn't natively understand multi-step agent traces, retrieval pipelines, or tool call chains. You get per-request observability, not workflow-level tracing. Helicone has been adding session and trace grouping features, but these are newer and less mature than dedicated tracing platforms like Langfuse or Phoenix.
For teams whose primary concern is cost management, usage analytics, and operational controls (caching, rate limiting, retries) with minimal integration effort, Helicone is excellent. For teams needing deep agent workflow tracing and evaluation capabilities, it works better as a complement to a tracing tool rather than a replacement.
Was this helpful?
Helicone stands out for its incredibly simple integration — a single-line proxy setup that requires no SDK or code changes. The cost tracking and rate limiting features are practical for production LLM applications. However, the feature set is narrower than LangSmith or Langfuse, lacking deep evaluation and prompt management capabilities. Best for teams wanting lightweight observability without committing to a full platform.
All LLM requests are captured by routing through Helicone's gateway. Supports OpenAI, Anthropic, Azure OpenAI, Google, and custom model endpoints. Logs include full request/response bodies, latency, token counts, and computed costs.
Use Case:
Adding complete LLM request logging to an existing production application in under 5 minutes by changing only the API base URL.
Real-time cost tracking dashboard with breakdowns by model, user, custom property, and time period. Supports budget alerts that notify when spend exceeds configurable thresholds per day, week, or month.
Use Case:
Tracking that your GPT-4 usage spiked 3x this week because a new feature accidentally uses it instead of GPT-4o-mini.
Identical requests return cached responses from Helicone's cache layer, controlled via cache headers. Supports bucket-based caching with configurable TTL and cache-hit rate monitoring.
Use Case:
Reducing costs by 40% on a FAQ chatbot where many users ask similar questions that generate identical API calls.
Attach arbitrary key-value metadata to requests via HTTP headers (Helicone-Property-*). Properties flow through to analytics, enabling segmentation by user, feature, environment, experiment, or any custom dimension.
Use Case:
Segmenting LLM costs by product feature to determine which features are most expensive to operate and which need optimization.
Configurable rate limits per user or API key enforced at the gateway level. Automatic retry with exponential backoff for failed requests, preventing application-level retry storms.
Use Case:
Preventing a single power user from consuming your entire OpenAI rate limit while ensuring failed requests are retried gracefully.
Content moderation filters that can flag or block requests containing sensitive content before they reach the LLM provider. Includes configurable rules and logging of filtered requests.
Use Case:
Blocking prompt injection attempts and logging them for security review without modifying application code.
Free
month
$20.00/month
month
Contact sales
Ready to get started with Helicone?
View Pricing Options →Teams that need immediate LLM cost visibility and spend management across multiple models and providers without writing integration code
Applications with repetitive query patterns where gateway-level caching can meaningfully reduce API costs
Organizations that want rate limiting, retry logic, and content moderation applied at the gateway layer without application changes
Multi-product teams that need to attribute LLM costs to specific features, users, or business units
Helicone works with these platforms and services:
We believe in transparent reviews. Here's what Helicone doesn't handle well:
Typically 20-50ms per request. For most applications this is negligible since LLM calls themselves take 500ms-30s. For latency-critical applications making many sequential calls, the overhead can compound and become noticeable.
Helicone has added session tracking that groups related requests together, but it's primarily designed around individual request observability. For deep multi-step agent tracing with parent-child relationships and custom spans, dedicated tracing tools like Langfuse provide more detail.
Helicone caches the complete response. For cached hits with streaming enabled, it replays the full response as a stream. Cache keys are based on the full request body by default, but you can configure custom cache keys using bucket IDs.
Yes, Helicone is open-source and can be self-hosted. The self-hosted version requires running the proxy gateway, a Supabase backend for storage, and ClickHouse for analytics. It's more operationally complex than the cloud version but gives you full data control.
OpenAI's dashboard shows aggregate usage by API key. Helicone provides per-request logging, custom property segmentation, caching, rate limiting, and cross-provider analytics. If you use multiple LLM providers or need per-user cost tracking, Helicone adds substantial value.
Weekly insights on the latest AI tools, features, and trends delivered to your inbox.
People who use this tool also find these helpful
Open-source LLM observability and evaluation platform built on OpenTelemetry. Self-host it free with no feature gates, or use Arize's managed cloud.
AI observability platform with Loop agent that automatically generates better prompts, scorers, and datasets to optimize LLM applications in production.
Enterprise-grade monitoring for AI agents and LLM applications built on Datadog's infrastructure platform. Provides end-to-end tracing, cost tracking, quality evaluations, and security detection across multi-agent workflows.
LLMOps platform for prompt engineering, evaluation, and optimization with collaborative workflows for AI product development teams.
Open-source LLM engineering platform for traces, prompts, and metrics.
Tracing, evaluation, and observability for LLM apps and agents.
See how Helicone compares to CrewAI and other alternatives
View Full Comparison →AI Agent Builders
CrewAI is an open-source Python framework for orchestrating autonomous AI agents that collaborate as a team to accomplish complex tasks. You define agents with specific roles, goals, and tools, then organize them into crews with defined workflows. Agents can delegate work to each other, share context, and execute multi-step processes like market research, content creation, or data analysis. CrewAI supports sequential and parallel task execution, integrates with popular LLMs, and provides memory systems for agent learning. It's one of the most popular multi-agent frameworks with a large community and extensive documentation.
Agent Frameworks
Open-source multi-agent framework from Microsoft Research with asynchronous architecture, AutoGen Studio GUI, and OpenTelemetry observability. Now part of the unified Microsoft Agent Framework alongside Semantic Kernel.
AI Agent Builders
Graph-based stateful orchestration runtime for agent loops.
AI Agent Builders
SDK for building AI agents with planners, memory, and connectors. - Enhanced AI-powered platform providing advanced capabilities for modern development and business workflows. Features comprehensive tooling, integrations, and scalable architecture designed for professional teams and enterprise environments.
No reviews yet. Be the first to share your experience!
Get started with Helicone and see if it's the right fit for your needs.
Get Started →Take our 60-second quiz to get personalized tool recommendations
Find Your Perfect AI Stack →Explore 20 ready-to-deploy AI agent templates for sales, support, dev, research, and operations.
Browse Agent Templates →