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Helicone
Apache-2.0Self-host Helicone for private LLM observability, gateway routing, and prompt tracking under Apache-2.0, replacing Helicone Pro on your own terms.
Open-source alternative to — compare it against:
Overview
Healthcare and financial applications sending customer prompts to external model providers face compliance rules that prohibit exporting telemetry to third-party dashboards. Helicone deploys an AI proxy and observability stack directly onto internal infrastructure, capturing request traces, token expenses, and latency metrics on self-managed storage. The platform intercepts provider calls through a base-URL change, giving engineers prompt visibility and session governance without relinquishing data sovereignty.
Why Helicone instead of Helicone Pro
Helicone Pro and commercial alternatives like Portkey operate hosted control planes that ingest API keys, prompt variables, and completions on vendor servers. For teams subject to HIPAA or strict confidentiality rules, allowing raw model interactions to leave the VPC introduces regulatory risk. Open-source Helicone keeps the proxying and ingestion lifecycle inside private subnets. Provider credentials, prompt templates, customer sessions, and evaluation runs reside in local PostgreSQL and ClickHouse instances that never transmit data to external endpoints.
The trade-off centers on operational maintenance. Helicone Pro provides a managed pipeline with tuned ClickHouse clustering, automated schema migrations, and a billing portal for charging end-users. Portkey similarly bundles managed guardrails and enterprise governance out of the box. Self-hosting Helicone requires DevOps engineers to provision storage for request payloads, manage ClickHouse retention, and isolate proxy ports. Yet because Helicone uses identical SDKs and headers across distributions, migrating between Helicone Pro and a self-hosted instance requires altering only an endpoint URL and an authentication secret.
What it does well
- Intercepts OpenAI, Anthropic, Gemini, and Azure calls through a base-URL swap or async worker without application code modifications.
- Stores metadata, latency percentiles, and token consumption counts in ClickHouse, enabling rapid analytical queries across millions of model calls.
- Organizes LLM interactions into hierarchical sessions and agent traces to diagnose multi-step reasoning failures and tool-call errors.
- Tracks prompt versions alongside performance metrics, allowing developers to test draft prompts against past production inputs before releasing updates.
- Configures automatic failover across different model providers when an upstream service encounters rate limits or outages.
