Last9 vs Honeycomb: In-Depth Comparison 2026

Last9 and Honeycomb both aim to simplify observability for cloud native teams, but they solve the problem from different architectural starting points. Honeycomb pioneered high cardinality event analysis with its proprietary ingestion pipeline and query language. Last9 positions itself as a unified OpenTelemetry native platform that runs closer to your infrastructure.

This comparison covers pricing models, OpenTelemetry support, deployment options, APM signal depth, and real cost scenarios for teams processing 10 TB to 30 TB monthly. Both tools are assessed on what they do well and where they fall short, with sourced examples from community threads and official documentation. CubeAPM is included in this comparison as a third option for teams that need on premises deployment with full stack observability.

Quick Comparison: Last9 vs Honeycomb

	Last9	Honeycomb
Best for	Platform teams consolidating tools, OTel first workflows	High cardinality debug, distributed systems
Pricing model	$0.22/GB data ingested	Free tier, Pro $130/mo + usage
OpenTelemetry	Native from day one	Native support
On premises	Available	Available (enterprise)
APM signals	Traces, metrics, logs, RUM	Traces, events, logs
Query language	SQL based	Honeycomb Query Builder
High cardinality	Supported via ClickHouse backend	Core strength, BubbleUp
Retention	Configurable	60 days standard, longer on enterprise
Deployment	Self hosted, managed	SaaS, enterprise on prem

Last9 Overview

Last9 is an OpenTelemetry native observability platform built on ClickHouse for high cardinality storage and fast query performance. It covers APM, logs, metrics, and infrastructure monitoring in a unified interface. Last9 offers both SaaS and self hosted deployment, with data sovereignty as a core design principle.

The platform targets platform engineering teams that want to consolidate multiple observability tools into one OpenTelemetry compatible system without vendor lock in. Last9’s architecture uses ClickHouse as the underlying datastore, which allows it to handle high cardinality telemetry without the sampling penalties common in traditional APM tools.

Key features:

• OpenTelemetry native ingestion for traces, metrics, and logs
• ClickHouse backend for high cardinality queries
• Self hosted and managed deployment options
• SQL based query interface for teams familiar with relational query syntax
• Built in alerting with support for Prometheus AlertManager integrations

Deployment model:

Last9 can be deployed as a managed SaaS service or self hosted inside your VPC or data center. The self hosted model gives teams full control over data residency and eliminates public cloud egress costs when telemetry stays within the same infrastructure.

Who it fits:

Last9 fits platform teams that want a unified observability platform built on open standards, teams with data residency or compliance requirements that rule out public SaaS, and engineering organizations consolidating multiple observability tools into one OpenTelemetry native system.

Honeycomb Overview

Honeycomb is a high cardinality observability platform built for debugging distributed systems. It allows engineers to query events with arbitrary dimensions without predefined indexes or schemas. Honeycomb’s BubbleUp feature automatically surfaces anomalies by comparing high cardinality fields across event groups.

The platform was designed to replace traditional metric based monitoring with event driven observability. Instead of aggregating metrics at write time, Honeycomb stores raw events and lets engineers slice them at query time using any combination of fields.

Key features:

• High cardinality event storage with no sampling required for most use cases
• BubbleUp automatic anomaly detection across event dimensions
• OpenTelemetry native ingestion
• Honeycomb Query Builder with visual query construction
• Service map with latency and error overlays
• Distributed tracing with full trace waterfall views

Deployment model:

Honeycomb is primarily a SaaS platform. Enterprise customers can deploy Honeycomb on premises using Honeycomb Secure Tenancy, which runs inside a customer managed VPC or data center.

Who it fits:

Honeycomb fits engineering teams debugging complex distributed systems, teams that need high cardinality event analysis without sampling, and organizations moving from traditional metric based monitoring to event driven observability.

Pricing Comparison

Pricing for observability platforms often becomes the deciding factor as data volumes grow. Both Last9 and Honeycomb use ingestion based pricing, but the cost structure differs significantly in practice.

Last9 Pricing

Last9 charges $0.22 per GB of data ingested. This covers traces, metrics, logs, and any telemetry sent via OpenTelemetry. There are no separate charges for users, hosts, or query volume.

Cost scenario for 10 TB monthly:

• Data ingestion: 10,000 GB × $0.22 = $2,200
• Data transfer: No external egress if self hosted
• Infrastructure: Self hosted deployment requires infrastructure costs (compute, storage). Managed deployment includes infrastructure in the $0.22/GB rate.
• Users: No per seat fees
• Total managed: $2,200/month

This estimate models Last9’s managed deployment. A self hosted deployment may have lower ingestion costs but requires infrastructure provisioning. Verify current rates at [Last9 pricing page](https://last9.io/pricing).

Honeycomb Pricing

Honeycomb offers a free tier with 20 GB monthly ingestion and 60 days retention. The Pro plan starts at $130/month and includes 200 GB ingestion, with additional data charged at usage based rates. Enterprise pricing is custom and includes features like SSO, custom retention, and on premises deployment.

Honeycomb’s pricing model changed in recent years. The platform moved from per column pricing to a simpler ingestion based model, but the exact per GB rate beyond the Pro plan bundle is not publicly listed and requires a sales conversation for higher volumes.

Cost scenario for 10 TB monthly (estimated):

Honeycomb does not publish exact per GB pricing beyond the Pro plan bundle. Based on community reports and comparable platforms, ingestion at scale can range from $0.30 to $0.60 per GB depending on contract terms.

• Data ingestion (estimated at $0.40/GB): 10,000 GB × $0.40 = $4,000
• Data transfer: Included in SaaS model, but egress from your cloud to Honeycomb incurs AWS/GCP fees (~$0.10/GB for cross region)
• Infrastructure: Managed by Honeycomb
• Users: No per seat fees
• Total SaaS estimated: $4,000 to $6,000/month

Pricing beyond the Pro tier is not publicly listed. Verify current rates at [Honeycomb pricing page](https://www.honeycomb.io/pricing).

Pricing Verdict

Last9 offers more pricing transparency with a flat $0.22/GB rate published on their site. Honeycomb’s pricing is less transparent beyond the entry tiers, which makes cost modeling harder for teams processing high data volumes. For teams with data sovereignty requirements, Last9’s self hosted option eliminates external egress costs entirely, while Honeycomb’s SaaS model incurs AWS or GCP egress fees when sending telemetry across regions.

Feature by Feature Comparison

OpenTelemetry Support

Both platforms support OpenTelemetry natively, but the depth of integration differs.

Last9:

Last9 is built natively on OpenTelemetry from the start. It ingests traces, metrics, and logs via the OpenTelemetry Collector and stores them in a unified ClickHouse backend. The platform uses OTel semantic conventions for resource attributes, spans, and metrics, which means telemetry remains portable if you migrate to another OTel compatible platform.

Last9’s architecture treats OpenTelemetry as the default ingestion path, not a bolt on. This makes it easier to adopt OpenTelemetry instrumentation libraries across your stack without vendor specific agents.

Honeycomb:

Honeycomb added native OpenTelemetry support in 2020 and has since adopted OTel as its primary ingestion method. The platform accepts traces, metrics, and logs via the OpenTelemetry Collector. Honeycomb’s instrumentation libraries (Beelines) still exist but are being phased out in favor of OpenTelemetry SDKs.

Honeycomb’s OTel integration is strong, but the platform still uses some proprietary conventions for event fields and annotations. This can create minor compatibility issues if you later migrate to another OTel backend.

Verdict:

Both platforms support OpenTelemetry natively. Last9’s architecture is more tightly aligned with OTel semantic conventions, making telemetry fully portable. Honeycomb’s OTel support is mature but retains some proprietary conventions that could complicate future migrations.

Deployment Model

Last9:

Last9 supports both self hosted and managed deployment. The self hosted option runs inside your VPC or data center, giving full control over data residency and eliminating external egress costs. The managed option runs on Last9’s infrastructure but still uses OpenTelemetry ingestion, so telemetry data is not locked into a proprietary format.

Self hosted Last9 deployments require infrastructure provisioning (Kubernetes cluster, ClickHouse storage, compute resources), but the platform provides Helm charts and Terraform modules to simplify setup.

Honeycomb:

Honeycomb is primarily a SaaS platform. Enterprise customers can deploy Honeycomb Secure Tenancy, which runs inside a customer managed VPC. This option is available only on enterprise contracts and requires a sales conversation to access.

Honeycomb’s SaaS model simplifies operations but requires sending telemetry data outside your infrastructure. For teams with strict data residency requirements, this can be a blocker unless they qualify for and budget for Secure Tenancy.

Verdict:

Last9 offers more flexible deployment options at all pricing tiers. Honeycomb’s on premises option is enterprise only, making it less accessible for mid market teams with compliance requirements.

High Cardinality Support

High cardinality refers to the ability to query telemetry data across dimensions with many unique values, such as user IDs, request IDs, or Kubernetes pod names. Traditional metric systems struggle with high cardinality because they aggregate data at write time, which creates combinatorial explosions in storage.

Last9:

Last9 uses ClickHouse as its storage backend, which is designed for high cardinality analytical queries. The platform stores traces, metrics, and logs in a columnar format, allowing engineers to filter and aggregate across any dimension without predefined indexes. Last9’s architecture does not impose cardinality limits on metric labels or trace attributes.

This makes Last9 suitable for querying traces by high cardinality fields like request IDs, session IDs, or user attributes without sampling penalties.

Honeycomb:

Honeycomb was built from the ground up to handle high cardinality event data. The platform’s core value proposition is the ability to query events across arbitrary dimensions in real time. Honeycomb’s BubbleUp feature automatically surfaces anomalies by comparing high cardinality fields across event groups, which helps engineers find root causes faster in complex distributed systems.

Honeycomb does not impose cardinality limits on event fields. Engineers can send events with hundreds of fields and query them interactively without predefined schemas.

Verdict:

Both platforms handle high cardinality well. Honeycomb’s BubbleUp feature gives it an edge for interactive debugging sessions where engineers need to explore unknown unknowns. Last9’s ClickHouse backend provides similar query flexibility but requires more manual query construction.

Query Language and UX

Last9:

Last9 uses SQL for querying telemetry data. This is familiar to engineers with relational database experience but can feel verbose for exploratory debugging. The platform provides a query builder UI for common queries, but complex queries often require writing SQL directly.

Last9’s SQL interface makes it easier to integrate observability queries with existing data pipelines and BI tools, but it lacks the interactive exploration UX that event driven platforms offer.

Honeycomb:

Honeycomb’s Query Builder is a visual interface for constructing queries without writing code. Engineers can select dimensions to group by, choose aggregation functions, and apply filters using dropdowns and autocomplete. The interface updates query results in real time as filters are added or removed.

Honeycomb’s UX is optimized for interactive debugging. The platform encourages engineers to explore telemetry data by slicing it across different dimensions until they find the root cause of an issue. This workflow is faster than writing SQL queries but less portable if you later migrate to another platform.

Verdict:

Honeycomb’s Query Builder is faster for interactive debugging and requires less SQL knowledge. Last9’s SQL interface is more flexible for complex queries and integrates better with existing data tooling, but it has a steeper learning curve for engineers unfamiliar with SQL.

Alerting

Last9:

Last9 includes built in alerting with support for Prometheus AlertManager integrations. Alerts can be configured using SQL queries or metric thresholds. The platform supports routing alerts to Slack, PagerDuty, email, and webhooks.

Last9’s alerting is functional but less mature than dedicated alerting platforms. Teams with complex alerting workflows may need to integrate with external tools like Grafana or PagerDuty for advanced routing and escalation policies.

Honeycomb:

Honeycomb offers alerting based on query results. Engineers can create alerts from any query in the Query Builder, setting thresholds on counts, percentiles, or aggregations. Alerts can be routed to Slack, PagerDuty, email, or webhooks.

Honeycomb’s alerting includes anomaly detection using historical data comparisons. The platform can automatically alert when a query result deviates significantly from its baseline, which reduces the need for manual threshold tuning.

Verdict:

Honeycomb’s anomaly detection gives it an edge for teams that want automatic threshold tuning. Last9’s Prometheus integration makes it easier to reuse existing alerting rules, but the platform lacks built in anomaly detection.

Retention

Last9:

Last9 offers configurable retention based on storage capacity. Teams running self hosted deployments control retention by allocating ClickHouse storage. Managed deployments offer retention tiers based on pricing plans.

Because Last9 uses ClickHouse, extending retention is a matter of adding storage capacity, not renegotiating contract terms. This makes it easier to retain telemetry data for compliance or long term analysis without hitting artificial limits.

Honeycomb:

Honeycomb includes 60 days retention on the Pro plan. Enterprise customers can negotiate longer retention periods, but this increases costs significantly. Honeycomb’s pricing model makes long term retention expensive because storage is bundled with ingestion.

Teams that need to retain telemetry data for more than 60 days often have to archive it outside Honeycomb, which adds operational complexity.

Verdict:

Last9’s configurable retention is more cost effective for teams that need long term telemetry storage. Honeycomb’s 60 day standard limit works for most debugging workflows but becomes a constraint for compliance or long term trend analysis.

Who Should Choose Last9

Last9 is the better choice for:

• Platform engineering teams consolidating multiple observability tools into one OpenTelemetry native system
• Organizations with data residency or compliance requirements that rule out public SaaS platforms
• Teams that prefer SQL based query interfaces and want to integrate observability queries with existing data pipelines
• Engineering teams that need cost effective long term retention without artificial limits
• Organizations that want to eliminate external egress costs by running observability infrastructure inside their own VPC

Who Should Choose Honeycomb

Honeycomb is the better choice for:

• Engineering teams debugging complex distributed systems with high cardinality event data
• Organizations that want interactive exploration UX without writing SQL queries
• Teams that need automatic anomaly detection with BubbleUp and historical comparisons
• Engineering teams moving from traditional metric based monitoring to event driven observability
• Organizations comfortable with SaaS deployment and willing to send telemetry outside their infrastructure

Verdict

Last9 and Honeycomb both solve observability for cloud native teams, but they target different workflows and deployment models. Honeycomb excels at interactive debugging with high cardinality event analysis, automatic anomaly detection, and a developer friendly query builder. Its SaaS first model simplifies operations but requires sending telemetry outside your infrastructure, which can be a blocker for regulated industries.

Last9 offers more deployment flexibility with self hosted and managed options, OpenTelemetry native architecture that avoids vendor lock in, and SQL based queries that integrate with existing data tooling. Its pricing is more transparent and cost effective for teams processing high data volumes with long term retention needs.

For teams with data sovereignty requirements or those consolidating multiple observability tools, Last9 is the more practical choice. For teams that prioritize interactive debugging UX and automatic anomaly detection over deployment control, Honeycomb delivers a superior experience.

CubeAPM: A Third Option for On Premises Full Stack Observability

CubeAPM runs entirely inside your infrastructure and delivers full stack observability covering APM, distributed tracing, log management, infrastructure monitoring, RUM, synthetic monitoring, and error tracking in one unified platform. Unlike Honeycomb’s SaaS model and Last9’s managed option, CubeAPM is self hosted by default but fully managed by the CubeAPM team, eliminating Day 2 operations burden.

Key differences:

• Deployment: CubeAPM runs on your infrastructure (VPC, on premises, or hybrid cloud). Telemetry data never leaves your environment, which solves data residency and compliance requirements without requiring enterprise contracts.
• Pricing: CubeAPM charges $0.15 per GB for all telemetry data ingested (traces, metrics, logs, RUM). There are no per seat fees, per host fees, or separate charges for retention. This makes it 60 to 70% cheaper than enterprise APM tools at scale.
• OpenTelemetry: CubeAPM is OpenTelemetry native from day one. It works with OpenTelemetry Collectors, Prometheus agents, and proprietary agents from Datadog or New Relic, making incremental migration possible without a hard cutover.
• Retention: CubeAPM includes unlimited retention with no additional cost. Teams can store telemetry data for compliance, long term trend analysis, or capacity planning without hitting artificial limits or renegotiating contracts.
• Support: CubeAPM provides direct engineering support via Slack and WhatsApp channels, not ticket queues. Customers report response times in minutes during incidents, not days.

Who it fits:

CubeAPM fits platform engineering teams that need full stack observability inside their own infrastructure, organizations with data residency or compliance requirements (HIPAA, GDPR, SOC 2), teams consolidating multiple observability tools to reduce vendor sprawl, and engineering organizations that want predictable pricing without per seat or per host fees.

CubeAPM is not a fit for teams that prefer fully managed SaaS with zero operational involvement or organizations that need autonomous AIOps features like automatic root cause analysis.

Frequently Asked Questions

Disclaimer: The information in this article reflects the latest details available at the time of publication and may change as technologies and products evolve. Features, pricing, and plan limits can change over time. Always verify the latest information directly with the vendor before making purchasing or deployment decisions.