Honeycomb
Honeycomb gives engineering teams a shared view of production with high-cardinality observability across traces, metrics, and logs.
Founded
2016Honeycomb gives engineering teams a shared view of production with high-cardinality observability across traces, metrics, and logs.
Honeycomb stores observability data as wide events with unlimited cardinality and custom fields, letting engineers query billions of data points in seconds without pre-indexing. Event-volume pricing with burst protection avoids host counts, indexed-field fees, and peak-usage penalties common in legacy monitoring tools.
BubbleUp surfaces hidden outliers across any attribute while event-based SLOs carry full debugging context into alerts. OpenTelemetry-native instrumentation across dozens of languages avoids proprietary agent lock-in, and long default retention keeps debugging data available.
Source: honeycomb.io
Distributed systems and AI workloads keep producing unfamiliar failure modes that legacy monitoring tools built for predictable stacks cannot explain. Engineering teams need investigation workflows that handle unknown unknowns across microservices, containers, and generative models without losing context.
Honeycomb raised a 50M dollar Series D in April 2023, bringing total funding near 150M dollars, and directed the capital toward geographic expansion and OpenTelemetry investment. Recent work on agent timelines, AI-assisted investigation, and enterprise features positions the platform for the shift toward agentic software.
Source: honeycomb.io
Honeycomb prices by event volume rather than seats, servers, or custom fields, with a free tier covering 20M events per month and Pro plans starting at 150 dollars monthly. Enterprise tiers use custom volume pricing with added capacity available on demand and unlimited seats, querying, and fields.
The model includes burst protection so unexpected traffic spikes do not become surprise bills, and long retention keeps debugging data queryable. A dedicated telemetry pipeline tier adds sampling, shaping, and routing controls for teams managing large log and trace volumes.
Source: honeycomb.io
Honeycomb treats structured log events as the building block of observability, deriving logs, metrics, and traces from the same high-cardinality data instead of storing separate signals. Canvas workspaces combine automated investigation, natural-language querying, and collaboration for debugging production issues.
Agent Timeline orders model calls, tool invocations, and agent handoffs for LLM applications while SLOs, BubbleUp anomaly detection, and a telemetry pipeline round out the suite. Teams ingest data through OpenTelemetry, explore billions of events in seconds, and pay by event volume.
Source: honeycomb.io