Google Cloud Trace vs Honeycomb
Google Cloud Trace
cloud.google.com
Honeycomb
honeycomb.ioSide by side
| Figure | Google Cloud Trace | Honeycomb |
|---|---|---|
| Price from | $0.20/mo | $150/mo |
| Free plan | — | Yes |
| Free trial | — | Yes |
| Open source | — | — |
| Web | Yes | Yes |
| Linux | Yes | — |
| iOS | — | Yes |
| Android | — | Yes |
| API | Yes | Yes |
| Self-hosted | — | Yes |
| Plans published | 1 | 3 |
| Spec score · Distributed Tracing Tools | 7.8 (#14) | 8.6 (#3) |
| Spec score · Runtime Monitoring Software | 7.6 (#30) | 8.5 (#15) |
| Spec score · Application Performance Monitoring | 7.7 (#25) | 8.5 (#6) |
| Integrations | Cloud Logging entries can be associated with traces and spans, and displayed alongside trace waterfall graphs or in Logs Explorer. | The integration directory lists technologies including AWS, OpenTelemetry, Slack, MySQL, Prometheus, ServiceNow, and GitHub Actions. |
Plans
In their own words
Google Cloud Trace
Google Cloud Trace has no write-up on Specifiction yet; its figures are above.
Honeycomb
Honeycomb is an observability platform for debugging systems, application code, and databases with structured telemetry. It supports distributed tracing, logs, metrics, and OpenTelemetry data sent over gRPC, HTTP/protobuf, or HTTP/JSON. Canvas combines AI-assisted investigations with natural-language queries, interactive exploration, and shared workspaces. Agent Timeline presents LLM calls, tool invocations, handoffs, and downstream system spans in event order. BubbleUp helps investigate outliers across traces, metrics, and logs. Listed integrations include AWS, OpenTelemetry, Slack, MySQL, Prometheus, ServiceNow, and GitHub Actions. The free plan allows up to 20M events and 100M metrics data points per month, with two triggers. Pro starts at $150/mo, with allowances of up to 750M events, 3.75B metrics data points, 100 triggers, and two SLOs. Enterprise has variable volumes and custom pricing. Honeycomb describes Free for individual projects, Pro for production teams, and Enterprise for multi-team or large-scale applications.