Helicone AI

Helicone is an open-source LLM observability and AI gateway platform: request logs, monitoring alerts, policy routing, multi-dimensional segmentation and HQL analysis, access with one line of base_url modification, used by the fastest-growing AI companies.

AI Developer Tools#Open Source#Observability#Log

Updated: 2026-10-26

What is Helicone AI?

Helicone defines itself as "the LLMOps platform behind reliable AI applications" and mainly does two things: routing and monitoring. The company claims that the world's fastest-growing AI companies use it to route, debug and analyze their applications. It has a lightweight integration method—usually, you only need to change one line of the base_url to connect. It also offers a 7-day free trial without a credit card, making it very low-threshold to get started. It packages the most commonly used capabilities in LLMOps into out-of-the-box services, eliminating the repetitive work of building logs and gateways from scratch. For AI product teams, it solves the problem that "model launch is just the beginning"—costs, latency and error rates all require continuous monitoring.

Who is it for?

AI application teams that need to see "who is using the product, how much they are spending, and where errors occur" after launch, especially products that are billed to customers or require usage governance; engineering teams with multi-model routing needs; startup projects that have wanted observability from day one; and teams that need to position "observability" as part of their product competitiveness and demonstrate usage transparency to customers.

Key features

Request Logs

Records the input, output and metadata of each LLM call, allowing backtracking by request when issues occur to quickly locate the root cause.

Monitoring and Alerting

Rate limiting, usage monitoring, and anomaly alerts. Detect abnormalities in usage or error rates in real time to prevent out-of-control situations.

AI Gateway

Routes requests based on policies, supports caching and failback, and balances stability and cost efficiency.

Multi-dimensional Segmentation

Segments data by Segment, Session, and User dimensions, facilitating analysis grouped by customers, versions, or experiments.

HQL Queries

Perform custom analysis on logs in a SQL-like manner, enabling flexible drill-down without waiting for pre-made reports.

Prompts & Datasets

Built-in Playground for accumulating datasets to iterate on prompts, turning real-world feedback into improvement material.

Cost Tracking

Request-level token and cost breakdowns, with cost anomalies visible at a glance.

Log Retrieval and Replay

Locate historical calls by request to facilitate debugging and auditing.

How to use Helicone AI

The access cost is extremely low.
It is recommended to first enable logs and cost dashboards to establish a baseline, then gradually add alerts and routing strategies.
enable advanced capabilities such as caching and rollback only after the traffic and demand stabilize.
A gradual approach is more stable than configuring everything at once.
Consolidate HQL into commonly used query templates for the team, so new members can quickly get started with analysis.
export data backups regularly to prevent historical analysis from being affected by changes in the platform's data retention policy.

Things to know

Free 7-day trial with no credit card required, but pay-as-you-go pricing applies afterward. Estimate costs before scaling up to avoid making observability itself an expense.

Logs are processed through the platform. If the data is sensitive, you need to confirm the data processing and retention policies, or evaluate the self-hosted solution.

Before enabling caching in a production environment, confirm response isolation between different users to avoid cross-contamination of cached data.

The gateway layer is a single point of failure for traffic, so after integration, you need to pay attention to its own availability and failover capabilities.

The log fields of different model providers may not be completely consistent; ensure unified metrics when analyzing across providers.