Helicone AI

Intercepts and logs all LLM API calls (OpenAI, Anthropic, Cohere, etc.) by acting as a proxy layer or via SDK integration, capturing request/response payloads, latency, token usage, and cost metadata. Supports both synchronous and asynchronous request patterns with minimal overhead through non-blocking instrumentation that doesn't block the main application thread.

Aggregates logged LLM API calls into dashboards showing latency percentiles, error rates, token usage trends, and cost per model/provider. Implements threshold-based alerting rules that trigger notifications (email, Slack, webhooks) when metrics exceed defined bounds, with configurable alert windows and aggregation intervals to reduce noise.

Enables deployment of Helicone as a self-hosted instance on private infrastructure (Kubernetes, Docker, VMs) with full data residency and no external API calls. Supports air-gapped deployments, custom authentication (LDAP, SAML), and integration with on-premise LLM endpoints, with all logs and metrics stored in customer-controlled databases.

Provides language-specific SDKs (Python, Node.js, Go, Java, etc.) that integrate with Helicone's proxy and logging infrastructure, handling automatic request instrumentation, trace ID propagation, and metadata attachment. SDKs support both synchronous and asynchronous patterns and integrate with popular LLM libraries (OpenAI Python client, LangChain, etc.) via drop-in replacements or decorators.

Detects identical or semantically similar LLM requests and returns cached responses instead of making redundant API calls, reducing latency and cost. Uses exact-match hashing on request payloads (prompt, model, parameters) with optional semantic similarity matching via embeddings, and stores cache entries with TTL-based expiration and provider-specific cache invalidation rules.

Applies configurable rules to filter or block LLM requests based on content patterns, prompt injection detection, or policy violations before they reach the API. Uses regex patterns, keyword matching, and optional ML-based classifiers to detect malicious prompts, PII exposure, or policy-violating content, with the ability to log violations and trigger alerts without blocking legitimate requests.

Tracks sequences of LLM API calls within a single user request or workflow by assigning unique trace IDs and correlating logs across multiple calls. Captures parent-child relationships between requests (e.g., initial prompt → function call → follow-up LLM call) and visualizes the full execution graph, enabling root-cause analysis of failures in multi-step LLM workflows.

Aggregates LLM API costs across providers, models, and time periods, and generates optimization recommendations based on usage patterns. Analyzes token efficiency, model selection, and caching opportunities, then suggests switching to cheaper models, enabling caching for high-frequency queries, or batching requests to reduce per-call overhead.

Extracts structured data from LLM responses using configurable parsers (JSON, regex, custom functions) and aggregates metrics across requests. Enables tracking of domain-specific KPIs like sentiment scores, entity extraction accuracy, or business metric extraction from LLM outputs, with support for time-series aggregation and custom dashboards.

Groups LLM API calls by user, session, or custom dimensions (e.g., feature flag, A/B test variant) and computes per-user/session metrics like total cost, token usage, error rate, and latency. Enables cohort analysis to compare LLM performance across user segments, with support for custom user attributes and session metadata.

Emits webhook events for LLM API calls, errors, and alerts in real-time, allowing downstream systems to react immediately. Supports filtering events by type (request, response, error, alert), with guaranteed delivery, retry logic, and payload signing for security. Enables integration with external systems like data warehouses, notification platforms, or custom workflows.

Provides a unified API interface that abstracts differences between LLM providers (OpenAI, Anthropic, Cohere, custom endpoints) and routes requests based on configurable rules. Supports automatic failover to backup providers, load balancing across multiple endpoints, and provider-specific parameter normalization to handle API differences transparently.