Graylog vs ELK Stack vs CubeAPM: Log Management, Observability & Cost Trade-offs (2026)

Graylog is strongest for centralized log management, SIEM, and security analytics. Elastic Stack is strongest for search, log analytics, dashboards, and flexible observability workflows built on Elasticsearch, Kibana, and integrations. CubeAPM is strongest for OpenTelemetry-native full-stack observability with customer-hosted deployment and predictable ingest-based pricing.

As teams move deeper into cloud-native and Kubernetes-based systems, observability decisions are no longer just about collecting logs. Teams also need to think about data control, cost growth, telemetry coverage, and root-cause speed.

This guide compares Graylog, ELK Stack, and CubeAPM across deployment, MELT coverage, OpenTelemetry support, retention, sampling, pricing behavior, and debugging workflows.

Quick Comparison: Graylog vs ELK Stack vs CubeAPM

How We Evaluated These Platforms

To keep this comparison grounded and reproducible, all three platforms were evaluated against a consistent set of technical and commercial criteria.

Test Architecture Assumptions

• Kubernetes-based microservices architecture
• JVM and Node.js services with distributed tracing enabled
• Centralized log ingestion from multiple sources (applications, containers, network devices)
• 30, 125, and 250 engineer team models

Telemetry Assumptions

• Logs: 250–1,500 GB/month scaled by team size
• Traces: 20–200 million spans/month
• Metrics: Standard infrastructure and application metrics
• Retention baseline: 30–90 days for cost modeling

This comparison focuses on architectural design and pricing behavior at scale. Entry-level free-tier experiences are noted where relevant, but most meaningful cost and coverage differences emerge under real production workloads.

Architecture Philosophy and Deployment Models

The biggest difference between these three platforms is not just feature coverage. It is how the observability pipeline is put together and where the data is stored.

CubeAPM: Unified observability in the customer environment

CubeAPM is positioned as a unified observability platform that runs inside the customer’s own environment rather than sending telemetry to a typical external SaaS backend. Its docs and website describe it as OpenTelemetry-based and focused on collecting telemetry from applications and infrastructure in one platform. CubeAPM also markets itself around full-stack visibility, data staying inside the customer’s environment, and managed operations on top of a self-hosted deployment model.

Graylog: More integrated for log management

Graylog gives teams a more integrated log management experience than assembling a full Elastic pipeline themselves. The Graylog layer handles ingestion, processing, search access, and the main user experience, while MongoDB stores metadata, and the search backend is handled through OpenSearch, self-managed OpenSearch, or Graylog Data Node depending on the deployment model. That means there are still multiple components under the hood, but day-to-day interaction is centered more directly around Graylog itself.

This usually makes Graylog simpler to adopt for teams whose main operational signal is logs. Graylog also positions its Security offering around SIEM, threat intelligence, and anomaly detection, which makes it a practical choice for security and compliance-focused teams that want log management plus security workflows in the same ecosystem.

ELK Stack: Modular and flexible, but heavier to operate

Elastic describes the Elastic Stack as a group of products that work together to store, search, analyze, and visualize data. The stack includes Elasticsearch, Kibana, Beats, Logstash, and more. This gives teams strong control over ingestion, search, indexing, dashboards, and data lifecycle design.

That flexibility also creates more operational work in self-managed deployments. Teams need to deploy, secure, upgrade, scale, and tune the stack themselves. Elastic Cloud reduces some of that burden, but self-managed Elastic still requires stronger platform engineering ownership.

There is also an important licensing point. Elastic says it moved Elasticsearch and Kibana source code away from Apache 2.0 in 2021 to a dual SSPL 1.0 and Elastic License 2.0 model. Teams considering Elastic for open-source or commercial use should review the current licensing terms carefully before deciding.

Feature Evaluation

Core Focus

CubeAPM is a full-stack observability platform for teams that want logs, metrics, events, and traces in one OpenTelemetry-based system while keeping data inside their own environment. That positioning comes from CubeAPM’s own product materials. 

Graylog is built mainly for centralized log management, alerting, and security analytics. Its main strength is collecting, processing, searching, and investigating logs from many sources in one place, especially for operations, compliance, and SIEM-style use cases.

ELK Stack is best suited to teams that want deep search, analytics, and flexible pipeline control. Its main strength is Elasticsearch for large-scale search and analysis, combined with Kibana for dashboards and exploration. For teams with enough engineering capacity, it can support very customized logging and observability workflows.

MELT Coverage

CubeAPM delivers a unified MELT platform with correlated investigation across telemetry types in the same environment, based on CubeAPM’s own product messaging.

Graylog is strongest on logs. It supports ingestion, enrichment, routing, alerting, and security investigation well, but native trace- and metrics-centered observability is not its main design focus.

ELK Stack provides broad coverage across logs, metrics, traces, uptime, synthetics, and user experience monitoring. It can cover the main observability signals well, but teams usually get the most from it by adopting the wider Elastic observability ecosystem rather than just the original logging stack alone. 

OpenTelemetry Support

OpenTelemetry has become a common vendor-neutral standard for collecting traces, metrics, and logs. A platform’s OTel support affects instrumentation portability, migration risk, and long-term lock-in.

Sampling Strategy

CubeAPM employs smart sampling as part of its OpenTelemetry-native observability model. The goal is to reduce low-value telemetry while still keeping the traces that matter most for troubleshooting. This makes it a strong fit for teams that want better control over ingestion costs without losing visibility into important production issues.

Graylog is different because its strength is log filtering, routing, and pipeline processing rather than trace-aware sampling. Teams can use stream rules and pipelines to decide which logs should be indexed, routed, or archived based on severity, source, or content. That makes Graylog useful for controlling log volume and storage costs, but it is not designed as a trace-sampling platform for distributed tracing workflows.

ELK Stack supports both head-based and tail-based sampling. Head-based sampling makes the decision at the start of a request, which helps reduce data volume early. Tail-based sampling waits until the full trace is visible, so slow requests, failed transactions, and unusual traces can be kept more reliably. This gives teams more flexibility when they want to balance cost control with deeper debugging coverage.

Real-World Debugging Scenario: Intermittent API Latency Spike

A payment service is intermittently spiking from 120ms to over 2 seconds during peak traffic. The team receives an alert and begins an investigation.

Using CubeAPM

CubeAPM uses smart sampling as a way to keep high-value traces while reducing lower-value telemetry volume. In this kind of incident, the team can inspect a slow trace, review span-level details, and move across related logs and infrastructure metrics inside the same platform, while keeping telemetry in the customer’s own environment. This gives CubeAPM a stronger unified workflow for teams that want correlated troubleshooting without relying on an external SaaS backend. Claims about very long retention or no extra retention cost should be tied directly to CubeAPM’s own pricing or product pages rather than stated as a neutral fact.

Using Graylog

The investigation starts in Graylog’s search interface, where the engineer filters logs by service name and time window to find errors or unusual messages around the latency spike. Streams, pipelines, and extractors can help structure, enrich, and route log data for easier investigation.

This makes Graylog effective for log-based troubleshooting, but its current OpenTelemetry gRPC input supports log data only. Metrics and traces sent over OTLP/gRPC are not ingested by that input. If the team needs trace-level visibility into the exact downstream database call or span causing the slowdown, it will usually need a separate tracing or APM tool.

Using Elastic Stack

The engineer can open Kibana and use Discover to search logs by service name, time range, and error patterns around the spike. If Elastic APM is deployed, the team can move into APM views to inspect transactions and spans, including database-query spans. Elastic documents that root spans map to APM transactions, while child spans such as database queries map to APM spans.

Pricing Behavior at Scale

Pricing differences tend to be modest at low volumes and material at scale. Understanding how each model behaves as telemetry grows is essential for total cost of ownership projections.

Disclaimer: The figures below are directional estimates based on standardized telemetry assumptions across logs, metrics, and traces. They are not vendor quotes. ELK Stack self-hosted figures reflect estimated infrastructure cost only and do not include internal engineering time. Elastic Cloud pricing is usage-based and may add separate ingest and retention charges depending on the deployment model. Graylog public pricing starts at annual plan minimums, while larger deployments typically require custom quotes. 

ELK Stack self-hosted costs are primarily infrastructure (compute, storage, network) and ongoing engineering time. Graylog Enterprise starts at $15,000/year, with Graylog Security from $18,000/year. CubeAPM’s flat $0.15/GB ingestion pricing includes all platform capabilities, APM, infrastructure monitoring, logs, traces, with no per-user or per-host fees.

Key Pricing Dynamics to Watch

CubeAPM uses flat per-GB pricing at $0.15/GB, with no per-user or per-host fees. Its pricing is easier to forecast as telemetry grows because cost mainly scales with data volume, not seats, hosts, or separate modules. Smart sampling can also help reduce lower-value trace volume while keeping useful traces for incident investigation.

Graylog has a free open-source option, while Graylog Enterprise starts at $15,000/year and Graylog Security starts at $18,000/year. Its paid model focuses on active data, with features like Data Lake, selective retrieval, and data tiering to help teams keep lower-priority logs outside high-cost active storage while still making them available for investigation.

Elastic Cloud Hosted starts at $99/month for the Standard plan. Higher tiers include Gold, Platinum, and Enterprise, with pricing increasing based on resources, cloud provider, region, and configuration. For Elastic Stack self-managed deployments, teams mainly pay for infrastructure, storage, support/subscription if used, and the engineering time needed to operate the stack well.

Data Retention

CubeAPM: CubeAPM offers unlimited data retention, which is useful for teams that need long lookback windows for debugging, compliance, trend analysis, and slow-moving production issues. Logs, metrics, and traces do not have to be shortened because of a fixed SaaS retention tier. This helps teams investigate older incidents, compare current failures with past patterns, and keep operational history available for longer.

Graylog: Graylog retention is configurable. Its documentation shows that new index sets can use a Time Size Optimizing window of about 30 to 40 days by default. Longer retention can be handled through archive, Data Lake, and tiering features depending on the plan and deployment. This gives teams flexibility, but the actual retention period still depends on how Graylog is configured.

Elastic Stack: Elastic Stack does not have one fixed retention period. Teams manage retention through lifecycle policies and data tiers such as hot, warm, cold, frozen, and delete. In practice, retention may be 7 days, 30 days, 90 days, or much longer depending on storage, lifecycle policy design, and budget. This gives Elastic Stack strong flexibility, but retention planning stays with the team.

Best-Fit Scenarios and Trade-offs

CubeAPM

Best for: Engineering teams running Kubernetes-based microservices that need full OpenTelemetry-native observability with stronger data control, predictable ingestion-based pricing, and deployment inside their own cloud or on-premises environment. It is especially relevant for teams that want unified visibility across APM, logs, and infrastructure while keeping telemetry in their own environment.

• Strengths: Full MELT coverage across logs, metrics, traces, and infrastructure visibility; OpenTelemetry-based APM with AI-based sampling; deployment inside the customer’s cloud with no traces or logs sent out; unlimited retention; predictable pricing with no per-user or per-host fees.
• Limitations: Not suited for teams that want a fully off-prem SaaS-only observability model. Also focused on observability, not SIEM or cloud security management.

Graylog

Best for: Teams that need centralized log management and SIEM capabilities, especially for IT operations, security analytics, and DevOps environments where logs are the main operational signal. Graylog is also positioned for flexible deployment across on-prem, hybrid, or cloud environments.

• Strengths: Integrated log management experience with fewer moving parts than a fully self-assembled ELK deployment; strong search and analysis for log-heavy workflows; active-data-oriented pricing model; flexible deployment across on-prem, hybrid, or cloud; security and compliance workflows through Graylog Security.
• Limitations: Stronger for logs than trace-level debugging. Larger self-managed environments still require operational ownership. Graylog’s current OpenTelemetry gRPC input supports log data only, not metrics or traces. 

ELK Stack

Best for: Teams with strong DevOps or platform engineering capacity that need deep search customization, flexible pipeline design, and full control over how data is ingested, stored, and visualized. It is a strong fit for organizations comfortable operating a modular self-managed stack or using Elastic Cloud for a managed deployment model.

• Strengths: Highly customizable pipeline and data model; Elasticsearch provides powerful full-text search and analytics; Kibana supports exploration and visualization; Elastic Observability extends the stack into APM, infrastructure monitoring, and broader observability workflows.
• Limitations: Self-managed deployments can create higher operational overhead because teams must manage scaling, upgrades, security, storage, and lifecycle policies. Long-term costs include infrastructure, storage, engineering time, and paid support or subscriptions if needed. Elastic also changed Elasticsearch and Kibana licensing in 2021 from Apache 2.0 to SSPL 1.0 and Elastic License 2.0, so teams should review licensing before using it in commercial or open-source-sensitive contexts. 

Decision Framework

Teams evaluating these three platforms typically prioritize one of the following needs. The table below maps common requirements to the most likely architectural fit, along with the key trade-off to evaluate.

Conclusion

The right platform depends on three things: what your primary operational signal is, where your data needs to live, and how the pricing model behaves as telemetry grows. Choose Graylog if log management and SIEM are your core workflows. Choose ELK if you need deep search customization and have the engineering capacity to manage a multi-component stack. Choose CubeAPM if full-stack OpenTelemetry-native observability, data sovereignty, and predictable ingestion-based pricing inside your own environment are the priority.

None of these platforms is universally superior. Each makes deliberate trade-offs. The best choice is the one that fits how your team actually works.

Disclaimer: The information in this article reflects the latest details available at the time of publication and may change as technologies and products evolve. Pricing figures are estimates based on publicly available or documented information.

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