The key difference between Grafana, Datadog, and CubeAPM is scope and control. Grafana focuses on visualization and dashboards, Datadog delivers an all-in-one SaaS for monitoring and security, while CubeAPM unifies MELT observability—Metrics, Events, Logs, and Traces—in a single OpenTelemetry-native platform with self-hosting and predictable ingestion-based pricing.

As telemetry grows across cloud and Kubernetes environments, teams using Grafana or Datadog often face fragmented pipelines, escalating per-host costs, and short retention windows.

CubeAPM solves these challenges with smart sampling (95 % compression), unlimited retention, and BYOC/self-host deployment—helping teams cut observability spend by up to 80 % while maintaining full data control and compliance.

In this comparison, we’ll evaluate Grafana, Datadog, and CubeAPM across their core features, scalability, and pricing to find which platform delivers the best full-stack visibility in 2025.

Grafana vs Datadog vs CubeAPM Comparison

Grafana vs Datadog vs CubeAPM: Feature-by-Feature Breakdown

Multi-Agent Support

CubeAPM: Designed for interoperability, CubeAPM supports multiple agents out of the box — OpenTelemetry, Prometheus, Datadog, New Relic, and Elastic. This flexibility lets teams migrate existing pipelines without changing instrumentation or collectors. It’s particularly useful for hybrid setups already running mixed agent types across environments.

Grafana: Grafana integrates with Prometheus, InfluxDB, StatsD, and OTEL exporters. However, each integration requires manual configuration and tuning of exporters or back-ends like Loki or Tempo. It’s great for DevOps teams that prefer hands-on control but can add management overhead.

Datadog: Datadog relies on its proprietary agent ecosystem with limited OTEL support. While setup is easy, the lack of open standards limits portability and creates vendor lock-in when scaling or switching infrastructure.

OpenTelemetry Support

CubeAPM: Built natively on OpenTelemetry (OTEL), CubeAPM supports complete ingestion of metrics, traces, and logs without any vendor-specific agents. Teams can plug in existing OTEL collectors and begin receiving correlated MELT data instantly. Its agent-agnostic design also ensures smooth migration from tools like Datadog or New Relic, allowing you to standardize observability pipelines across Kubernetes, serverless, and hybrid environments.

Grafana: Provides partial OpenTelemetry compatibility through integrations like Tempo for distributed tracing and Loki for logs. While flexible, it often requires manual configuration of exporters and custom dashboards to visualize OTEL data effectively, which can slow adoption for teams without deep instrumentation experience.

Datadog: Supports OpenTelemetry ingestion via its OTLP endpoint, but the data is translated into Datadog’s proprietary schema once inside its backend. This limits full interoperability with other OTEL-native tools and makes switching away from Datadog more complex in the long run.

MELT Coverage (Metrics, Events, Logs, Traces)

CubeAPM: Provides full MELT observability in one backend — Metrics, Events, Logs, and Traces are correlated automatically. This unified view cuts MTTR and eliminates the need to maintain multiple data stores.

Grafana: Achieves MELT visibility only when combined with separate tools — Loki for logs, Tempo for traces, and Mimir for metrics. Though flexible, running these services independently can increase operational complexity and storage costs.

Datadog: Offers full MELT coverage natively within its SaaS stack, but every telemetry type is metered separately. As workloads grow, the segmented pricing for logs, metrics, and traces leads to unpredictable spend.

Deployment Options

CubeAPM: Offers self-hosting and BYOC (Bring Your Own Cloud) options. Teams can deploy CubeAPM inside their own VPC or on-prem environment, maintaining full data sovereignty while CubeAPM manages scaling and updates remotely.

Grafana: Supports both Grafana Cloud and open-source self-hosting. While cloud deployment is quick, it moves data outside your control. Self-hosting gives flexibility but demands infrastructure expertise to manage updates and scaling.

Datadog: Entirely SaaS-based, with no self-hosting option. This simplicity suits fast-moving teams but doesn’t meet compliance requirements for regulated sectors needing in-region data storage.

Pricing and Cost for Different Team Sizes

Based on verified vendor pricing, CubeAPM offers the most transparent and predictable model at $0.15 per GB ingested — flat, unified, and consistent across all telemetry types. Below is a cost comparison for small, mid-sized, and large teams.

Cost for Small Teams (~30 hosts):

• CubeAPM: $2,080/month
• Grafana: ~$3,870/month
• Datadog: ~$8,185/month

Cost for Mid-Sized Teams (~125 hosts):

• CubeAPM: $7,200/month
• Grafana: ~$11,875/month
• Datadog: ~$27,475/month

Cost for Large Teams (~250 hosts):

• CubeAPM: $15,200/month
• Grafana: ~$26,750/month
• Datadog: ~$59,050/month

Teams switching to CubeAPM typically achieve 60–80% lower total observability costs compared to Grafana Cloud’s modular model or Datadog’s per-host billing.

CubeAPM: Cost for Small, Medium, and Large Teams

Based on real customer data, CubeAPM simplifies pricing through a single ingestion-based plan at $0.15/GB, covering logs, traces, metrics, and events. There are no per-user licenses, data-transfer charges, or retention add-ons.

CubeAPM Pricing:

• $0.15/GB ingested

Estimated Monthly Costs:

• Small teams (~30 hosts): $2,080
• Mid-sized teams (~125 hosts): $7,200
• Large teams (~250 hosts): $15,200

This structure gives DevOps and SRE teams full budget predictability, especially for large Kubernetes and hybrid environments where data ingestion can spike.

Grafana Cloud: Cost for Small, Medium, and Large Teams

Grafana follows a modular billing model where each service — Logs, Metrics, and Traces — is priced separately. While this flexibility benefits smaller setups, costs scale sharply once multiple telemetry pipelines are ingested simultaneously.

Grafana Cloud Pricing:

• Logs: $0.50/GB ingested
• Traces: $0.50/GB ingested
• Pro Base: $19/user/month

Estimated Monthly Costs:

• Small teams (~30 hosts): ~$3,870
• Mid-sized teams (~125 hosts): ~$11,875
• Large teams (~250 hosts): ~$26,750

Grafana Cloud is ideal for teams primarily focused on visualization and dashboards, but it becomes harder to budget for as data volume grows.

Datadog: Cost for Small, Medium, and Large Teams

Datadog’s pricing is host-based, which means costs rise with infrastructure size and the number of features enabled. Each module — APM, Infrastructure, and Logs — is billed separately, often accompanied by data-transfer fees.

Datadog Pricing:

• APM: $31/host/month
• Infrastructure: $15/host/month
• Data Transfer: $0.10/GB

Estimated Monthly Costs:

• Small teams (~30 hosts): $8,185
• Mid-sized teams (~125 hosts): $27,475
• Large teams (~250 hosts): $59,050

While Datadog offers excellent out-of-the-box monitoring, its per-host and per-feature billing makes cost forecasting challenging for teams with dynamic scaling or large microservice fleets.

In total cost and simplicity, CubeAPM remains the most predictable option — delivering unified observability at one flat rate without the layered billing models of Grafana or Datadog.

Sampling Strategy

CubeAPM: Implements Smart Sampling with ~95 % compression. It keeps latency and error-related traces intact while discarding noise, reducing telemetry volume by up to 70 % and keeping analysis accurate at scale.

Grafana: Uses tail or probabilistic sampling managed in Tempo. It’s effective for basic performance insight but lacks contextual awareness, so critical anomalies may be dropped during reduction.

Datadog: Datadog primarily applies head-based sampling within its agents, meaning traces are filtered before leaving the host. The process is efficient but static, as decisions are made before the full trace context is known. Teams can add retention filters to keep specific spans, yet these still rely on predefined rules.

Data Retention

CubeAPM: Retains data indefinitely at no extra cost, with all telemetry stored inside the customer’s cloud. This makes it ideal for teams needing long-term analytics, SLA reviews, or compliance audits.

Grafana: Default retention ranges from 15 to 30 days; extending it incurs extra cloud-storage fees or added infrastructure overhead. It’s practical for short-term monitoring but not for historical trend analysis.

Datadog: Offers up to 15-month retention on enterprise tiers. Lower plans store data for shorter windows, and extended retention requires higher-priced contracts.

Support Channel & TAT

CubeAPM: Provides direct Slack and WhatsApp access to core engineers with average response times under five minutes — invaluable for teams resolving production incidents.

Grafana: Offers community and email-based support; enterprise plans add ticketed options but often have slower response cycles.

Datadog: Uses ticket-based and email support with tiered SLAs. Response times vary from hours on Pro tiers to days on basic ones.

Known for

CubeAPM: Recognized for its OpenTelemetry-native architecture, self-hosting flexibility, unlimited retention, and predictable pricing — delivering full-stack observability without vendor lock-in.
Grafana: Known for world-class dashboards, visualization flexibility, and an open-source ecosystem ideal for teams already managing their telemetry pipelines.
Datadog: Known for its mature SaaS platform and extensive integrations, giving quick time-to-value for teams that prioritize simplicity over cost control.

Which Tool Is Best for You? Why Brands Choose CubeAPM

CubeAPM stands out for teams that need complete, OpenTelemetry-native observability with full control over data and predictable pricing. It unifies Metrics, Events, Logs, and Traces (MELT) across APM, Infrastructure, RUM, and Synthetic Monitoring — eliminating the need for multiple vendor tools or hidden licensing fees.

Benefits of Choosing CubeAPM

• Transparent pricing: One simple model — $0.15 per GB of ingested data, covering logs, metrics, traces, and events. No separate user or host charges.
• Intelligent sampling: A smart, context-driven sampling engine automatically filters noise and retains high-value traces, delivering up to 95% compression without losing diagnostic accuracy.
• Unified MELT platform: Complete visibility across applications, infrastructure, and user experience — all in a single, correlated interface.
• Bring Your Own Cloud (BYOC): Deploy CubeAPM inside your own environment to maintain data sovereignty and meet compliance mandates.
• Unlimited data retention: Keep observability data for as long as you want — perfect for teams handling audit trails, SLA analysis, and long-term reliability insights.
• Multi-agent Support: Native compatibility with OpenTelemetry, Prometheus, Datadog, and New Relic makes switching effortless and risk-free.
• API-first access: Pull, query, or automate any telemetry data directly using open APIs — no feature gates or paywalls.

Dedicated real-time support: Direct Slack and WhatsApp access to core CubeAPM engineers with response times measured in minutes.

Optimized for scale: Efficient architecture and 95% telemetry compression keep costs predictable even for multi-terabyte workloads.

Grafana vs Datadog vs CubeAPM: Use Cases

Choose Grafana if:
You want powerful dashboards and custom visualizations for Prometheus or Loki data. Grafana fits teams that already operate open-source stacks and primarily need visualization — not a full APM backend.

Choose Datadog if:
You prefer a fully managed SaaS that’s easy to set up and integrates quickly across your infrastructure and applications. Datadog offers a wide feature set, but costs can climb fast as hosts, metrics, and teams grow.

Choose CubeAPM if:
You want end-to-end observability that’s OpenTelemetry-native, cost-predictable, and data-compliant. CubeAPM gives teams full MELT visibility, self-hosting flexibility, and real-time DevOps support — helping engineering teams reduce MTTR and maintain complete data control.

Ideal for:

• Teams scaling microservices or Kubernetes clusters.
• Organizations with strict compliance and localization needs.
• Engineering teams seeking performance insights without unpredictable SaaS costs.

Conclusion

Grafana and Datadog each serve distinct roles in observability. Grafana excels at visualization but depends on multiple components for complete MELT coverage, increasing operational overhead. Datadog provides an integrated SaaS experience that’s easy to deploy, yet its layered billing and limited control can make scaling complex and costly.

CubeAPM unifies metrics, logs, traces, and user experience monitoring within one OpenTelemetry-native platform. It simplifies observability management, supports self-hosting for data compliance, and offers real-time support directly from engineers — empowering teams to detect, resolve, and optimize faster.

For organizations seeking end-to-end visibility, consistent performance, and full control over their telemetry stack, CubeAPM stands out as the most complete and future-ready choice for modern DevOps and SRE teams.