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Helicone

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LLM observability platform that logs every API call, tracks costs, and monitors latency via a one-line proxy integration - YC W23.

Helicone is an open-source LLM observability platform that works as a transparent proxy - developers change one URL in their existing code and immediately get logging, cost tracking, latency analytics, and prompt management for every LLM request. It supports OpenAI, Anthropic, Azure, and 20+ other providers through the same integration. Founded in 2023 by Justin Torre and Scott Nguyen through YC W23, Helicone is used by thousands of AI startups and teams to understand where LLM costs are going, debug slow requests, and track how prompt changes affect performance over time. The free tier covers 10,000 requests per month, making it accessible from day one of any LLM project.

#llm-observability
#developer-tools
#ai-development
#open-source
#cost-tracking
#api
Freemium

Free plan available

Update Tool
helicone.ai
Freemium
Pricing Model
Code & Development
Category
2023
Since
Free Plan
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Key Features

  • One-line proxy integration - change baseURL to helicone.ai and get instant observability for any LLM provider
  • Cost tracking per model, user, and request - broken down by input tokens, output tokens, and total spend
  • Latency monitoring with p50, p95, and p99 percentiles for every model and prompt configuration
  • Prompt management with versioning, A/B testing, and performance comparison across variants
  • Request logging with full input/output capture, metadata tagging, and search
  • Caching layer that returns identical responses for repeated prompts and reduces API costs
  • User-level analytics to attribute LLM usage and cost to individual end users in your application

Use Cases

  • AI startup founders identifying which features are driving LLM costs before they scale
  • Backend engineers debugging slow LLM responses by inspecting exact latency breakdown per request
  • Product teams A/B testing prompt variants and measuring quality impact on production traffic
  • Teams tracking per-user AI costs to build usage-based pricing for their own product

Pros

  • One-line integration with zero code changes is the lowest-effort path to LLM observability available
  • Open-source core means teams can self-host for data-sensitive applications without vendor lock-in
  • Free tier with 10,000 requests covers meaningful production traffic for early-stage products

Cons

  • Proxy architecture adds a small network hop - adds 5-20ms latency that sensitive real-time applications may notice
  • Advanced features like custom dashboards and team access controls require a paid plan
  • Self-hosting requires Docker setup which adds operational overhead compared to the managed cloud option

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