What Helicone Does
Helicone solves the visibility problem that every LLM application encounters: you are spending money on AI requests but cannot easily see where it goes, how fast responses are, or which users consume the most tokens. By operating as a proxy between your application and LLM providers, Helicone captures every request and response with full metadata without requiring SDK changes or code instrumentation.
Integration and Cost Tracking
Integration is remarkably simple. For OpenAI, you change the base URL from api.openai.com to oai.helicone.ai and add your Helicone API key as a header. Your existing code, SDKs, and error handling continue working exactly as before. This proxy approach means adoption takes minutes rather than the hours required by observability tools that need decorator or callback instrumentation throughout your codebase.
Cost tracking provides real-time visibility into spending across providers, models, and features. Dashboards show daily, weekly, and monthly cost trends with breakdowns by model, endpoint, and custom properties you define. For teams managing budgets across multiple AI features or multiple team members, this granularity prevents the surprise bills that catch organizations off guard.
Request Logging and Caching
Request logging captures full inputs and outputs for every LLM call, enabling debugging and quality review. You can search, filter, and replay requests to understand why a specific interaction produced unexpected results. For compliance-sensitive applications, this complete audit trail satisfies requirements that informal logging cannot meet.
Caching reduces costs by serving identical repeat requests from Helicone's cache rather than forwarding them to the LLM provider. For applications with common queries — FAQ bots, template-based generation, or classification tasks with repeated inputs — caching can reduce costs significantly without any code changes beyond enabling the feature.
Rate Limiting and Self-Hosting
Rate limiting and user tracking enable you to control per-user or per-feature consumption. Set limits on requests per minute or tokens per day for specific users or API keys. This prevents individual users or features from consuming disproportionate resources, which is particularly important for multi-tenant SaaS applications with AI features.
The open-source platform can be self-hosted for organizations with data privacy requirements. The self-hosted version provides the same proxy and logging capabilities, keeping all request data on your infrastructure. The managed cloud option eliminates operational overhead for teams that prefer convenience over self-hosting.
Analytics and Alternatives
Analytics go beyond simple logging to provide actionable insights. Latency percentile distributions show response time patterns. Token usage trends reveal whether prompts are becoming more verbose over time. Model comparison views help evaluate whether cheaper models produce acceptable quality for specific use cases.
Compared to Langfuse, Helicone is simpler to adopt but less deep in evaluation capabilities. Langfuse provides prompt versioning, evaluation pipelines, and dataset management that Helicone does not. Compared to Portkey, Helicone focuses on observability while Portkey adds active request routing with failover and load balancing. Many teams use Helicone alongside these tools.
The Bottom Line
Helicone is the right first step for any team that needs visibility into their LLM usage. The proxy integration approach means you can have complete cost tracking and request logging working within minutes. For teams that later need deeper evaluation or active routing, Helicone complements rather than conflicts with more specialized tools.
