Helicone vs Lunary: LLM Gateway Latency Monitoring

Helicone excels at providing granular, gateway-level latency metrics with a strong focus on cost attribution and caching effectiveness. Its architecture is purpose-built to sit as a proxy, capturing detailed timing breakdowns—including time-to-first-token (TTFT), model provider latency, and inter-token latency—without requiring code changes. For example, teams using Helicone's cache monitoring can directly measure the latency savings from semantic caching, often reporting a 40-60% reduction in p95 latency for repeated queries, which is critical for optimizing high-volume, cost-sensitive agent deployments.

Lunary takes a different approach by embedding latency monitoring within a broader observability and evaluation platform. Instead of acting solely as a gateway, Lunary ingests logs and traces to correlate latency data with prompt versions, user feedback, and agent behavior. This results in a richer context for debugging why a specific step was slow, linking it to a particular model, a complex tool call, or a problematic prompt template. The trade-off is that its latency data is often derived from client-side or server-side logs rather than a pure proxy, which can introduce minor clock-skew in distributed tracing.

The key trade-off: If your priority is precise, real-time gateway metrics, sub-millisecond timing accuracy, and direct cache optimization, choose Helicone. If you prioritize understanding latency in the full context of agent trajectory, prompt experimentation, and user outcomes, choose Lunary. For teams needing a pure, high-performance LLM gateway with cost-control features, Helicone is the stronger fit. For teams building a comprehensive evaluation and monitoring suite where latency is one of several key signals, Lunary provides a more integrated view.

Helicone excels at high-volume, cost-sensitive latency monitoring because of its granular caching analytics and provider-agnostic design. For example, its dashboard can break down latency into time-to-first-token (TTFT) and inter-token latency, while simultaneously calculating the exact cost savings from cache hits. This makes it the superior choice for teams whose primary bottleneck is the recurring cost and latency of identical or similar prompts in production.

Lunary takes a different approach by embedding latency data within a broader agent observability platform. Instead of just monitoring the gateway, it traces the latency of the entire agent loop, including tool calls, reasoning steps, and retrieval. This results in a trade-off: you get less granular gateway-specific caching metrics, but you gain the ability to pinpoint whether a slowdown originates at the model provider, in a slow vector database query, or within a complex tool execution.

The key trade-off: If your priority is optimizing LLM spend and reducing gateway latency through aggressive caching, choose Helicone. Its specialized metrics provide the precise data needed to tune cache policies and hold providers accountable. If you prioritize understanding end-to-end agent performance and diagnosing bottlenecks that span multiple services, choose Lunary. Its strength lies in correlating gateway latency with every other step in your agent's workflow.

Consider your team's operational focus. A platform engineering team managing a high-throughput LLM gateway as a service will find immediate value in Helicone's cost and cache analytics. An AI product team debugging a complex ReAct agent will find Lunary's full-trace correlation indispensable for identifying why a specific user session was slow, even if the model call itself was fast.