Obseria vs Honeycomb

Feature comparison

Obseria as a Honeycomb alternative: the short answer

Honeycomb shaped how many engineers think about observability. Its core idea is simple and powerful: send rich, high-cardinality events, keep every attribute queryable and debug production by asking new questions instead of reading pre-built dashboards. Obseria shares that conviction about high-cardinality data and OpenTelemetry, but packages it as a broader platform that also covers infrastructure, digital experience, application security and business observability. If you are evaluating a Honeycomb alternative, the key question is whether you need a best-in-class debugging tool for distributed systems or one platform that replaces several. This comparison is based on Honeycomb's public pricing page, website and support documentation as of October 2026.

Pricing model: event allowances vs. per-signal metering

Honeycomb sells plans. According to its pricing page, the Free plan includes up to 20 million events and 100 million metrics data points per month. Pro starts at a monthly fee for teams with a production application, with up to 750 million events and billions of metrics data points per month, 100 triggers, two SLOs, SSO and support. Enterprise is custom-priced, starts with an annual event allowance, and adds a service map, Refinery support, AWS PrivateLink, the SLO Reporting API, Private Cloud support and more SLOs. Honeycomb's telemetry pipeline is priced separately per GB. Seats are unlimited.

The event model is easy to reason about: one span or one structured log line counts as one event, regardless of how many attributes it carries. That makes wide, attribute-rich events attractive, which fits Honeycomb's debugging philosophy.

Obseria also counts events rather than bytes, but meters every signal directly instead of selling allowances:

• Logs, spans and RUM web events: $0.060 per million ingested and $0.540 per million stored, with 30-day retention.
• Metric data points: $0.020 per million ingested and $0.180 per million stored, with 13-month retention.
• Synthetic API check runs: $0.20 per thousand; Obseria Intelligence agent credits: $0.60 per credit.
• Filtered data only costs ingestion. Every feature, including SLOs, alerting, SSO/RBAC, API, CLI and MCP server, is included for unlimited users.

Plans with allowances give predictable invoices within a band; pure metering avoids paying for unused headroom and removes feature limits such as the number of SLOs. Which suits you depends on how steady your event volume is.

A practical way to compare is to take one month of real usage: the number of events and metrics data points you send to Honeycomb today, split into spans, logs and metrics. Multiply the span and log count by Obseria's ingest and storage rates, add metric data points at the metric rate, and subtract whatever you would filter in Obseria Pipeline, which then only incurs the ingest price. Keep in mind that the two platforms retain data for different periods, so compare storage cost and retention together rather than in isolation.

Data model and debugging workflow

Honeycomb's strength is interactive, exploratory analysis. Its query builder, heatmaps and BubbleUp, which compares an anomalous selection against the baseline across all attributes, make it fast to find which customer, build or endpoint is behind a latency spike. Honeycomb describes its foundation as high-cardinality and event-based, queried without pre-aggregation. Logs, metrics and frontend observability have been added around this tracing core. Honeycomb documents a standard 60-day retention window for event data.

Obseria Lake is an index-free lakehouse with schema-on-read. Logs, metrics, spans, profiles, RUM events and security signals sit in the same store, so a single query can join a slow span with the matching container metrics and log lines. OQL works across all signals, alongside PromQL and SQL:

fetch spans
| filter service == "checkout" and duration > 2s
| summarize count(), by:{customer.tier, k8s.node.name}

Obseria TopoGraph adds an automatically discovered topology of services and infrastructure, which helps when the question is not only which attribute correlates with a problem, but which dependency caused it.

OpenTelemetry and sampling

Both vendors are strong OpenTelemetry advocates. Honeycomb recommends OpenTelemetry SDKs for instrumentation and accepts OTLP natively. For cost control at high volume it offers Refinery, an open-source tail-based sampling proxy, which Enterprise customers can also use as a managed service.

Obseria is OpenTelemetry-native: OTLP is the primary ingest protocol, semantic conventions are the data model, and data can be exported via OTLP at any time. Tail-based sampling is built in, and Obseria Pipeline filters, enriches and masks PII at ingest. Teams with existing OTel SDKs typically see their first trace within 15 minutes. Because both platforms speak OTLP, switching or dual-shipping is mostly an exporter configuration change.

One difference worth testing is how each platform handles signals beyond traces. If your team already ships infrastructure metrics and logs to another backend, check whether consolidating them next to your traces shortens investigations in practice, or whether a specialized tracing tool plus your existing stack works well enough.

AI-assisted investigations

Honeycomb has invested heavily in AI workflows. Canvas runs investigations, for example when an SLO starts burning, the Honeycomb MCP server gives coding agents access to production context, and anomaly detection and agent observability for LLM applications are part of the platform.

Obseria Intelligence combines causal, predictive and generative AI. Agentic root-cause investigations use the TopoGraph and all signals in Obseria Lake and return a chain of evidence that engineers can verify. Obseria also ships an MCP server, and AI Observability covers LLMs, agents and RAG pipelines with token, cost, quality and guardrail tracking. Customer data is never used to train AI models.

Data residency and compliance

Honeycomb offers a US instance on AWS us-east-1 and an EU instance on AWS eu-west-1 in Ireland, selectable at sign-up, and Private Cloud support on Enterprise. Its security documentation lists its certifications and attestations.

Obseria stores data in the EU (Frankfurt, Dublin), the UK (London) or in your own cloud account (BYOC). It is certified to ISO 27001, 27017 and 27018, SOC 2 Type II, BSI C5 for the EU region and TISAX, and supports DORA, NIS2 and GDPR with automatic PII masking, field-level access, audit logs and a 99.95% SLA.

Migrating from Honeycomb to Obseria

1. Add a second OTLP exporter: in your OpenTelemetry Collector or Refinery setup, send the same traces and events to Obseria during evaluation.
2. Recreate boards, triggers and SLOs: rebuild the queries your team runs most in OQL and compare results on the same incidents.
3. Extend coverage: add the Obseria Agent for infrastructure, profiles and logs that previously lived in other tools.

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Which tool fits you?

Switching in three steps

1. Dual-ship OTLP. Add Obseria as a second exporter in your Collector or sampling pipeline and compare query results on real incidents.
2. Rebuild queries and SLOs. Translate frequently used boards, triggers and SLOs into OQL and validate alert behavior side by side.
3. Broaden coverage. Deploy the Obseria Agent to add infrastructure metrics, logs and profiles next to your traces.