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7 Best Kubernetes Observability Tools in 2026 (Tested & Compared)
Discover the top Kubernetes observability tools in 2026. Compare their up-to-date features (including AI) and find the best fit for your needs.
Most Kubernetes observability tool guides I've come across are outdated. The landscape in 2026 looks different. There is a bigger push for no vendor lock-in, eBPF-based auto-instrumentation, OpenTelemetry support, and AI that actually helps with root cause analysis.
In this post, we are going to explore some new, some mature tools with their updated list of features, pricing, and ease of setup.
Want quick comparisons? Jump to the comparison table.
NOTE: We evaluated each tool by testing it in a demo Kubernetes environment and also checking their reviews on G2 to also get a sense of community feedback and user satisfaction.
Tool Categories
We split these tools into two main categories:
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Specialized Kubernetes observability platforms: Purpose-built for Kubernetes, these tools understand K8s constructs natively.
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General full observability platforms: Full-stack observability platforms that support Kubernetes alongside other environments (cloud, on-prem, serverless).
Why Kubernetes needs specialized observability tools ?
Pods disappear, services scale up and down, and a single request can hit dozens of microservices. Traditional tools weren't built for this. K8s also has its own telemetry signals (Kubernetes events like OOM kills, scheduling failures, rollout) that most tools completely ignore or just ship as raw events. Every signal needs to be marked with k8s resource labels such as (workload name, namespace, pod name, container name, etc.) to be useful.
This guide focuses on full-stack observability tools. For single pillar providers (logging-only, tracing-only, etc.) and DIY stack guidance, see our Kubernetes Observability Guide.
1. Metoro
Specialized Kubernetes observability platform
Pricing: ~$20/node/mo - includes 100GiB of ingest per month per node. Usage-based pricing available with 0.20/GiB.
Setup time: under 5 minutes.
Metoro is a Kubernetes-native observability platform that combines full-stack telemetry (metrics, logs, traces, profiling) with an AI SRE assistant. One Helm install, no code changes, eBPF handles auto-instrumentation across your entire stack. Works with service meshes (Istio, Envoy, Linkerd) and persistently records K8s events that normally expire after an hour. Fully OpenTelemetry compatible.
Tool complexity: Low
Differentiator(s):
- 5-minute setup with zero-instrumentation via eBPF: Captures requests and queries across all pods without code changes, including third-party services.
- AI SRE assistant: Root-cause analysis, automatic alert triage, AI-powered deployment verifications, and can generate fix PRs from runtime telemetry.
- Handles high-cardinality at scale: Predictable per-node pricing, no memory issues from metric explosion.
- Kubernetes-native: Ability to chart/visualize values from k8s yaml files.
Don't use if:
- Not running Kubernetes (purpose-built for K8s only).
- Using GKE Autopilot or environments that restrict DaemonSets/eBPF.
- Require completely open-source solution with no proprietary components.
Deployment options: Cloud (SaaS) / BYOC (your VPC, managed by Metoro) / On-prem (air-gapped supported).
2. Coroot
Specialized Kubernetes observability platform
Pricing: $1/CPU core/month. OSS available.
Setup time: Minutes
Coroot is an open-source observability platform with eBPF-based auto-instrumentation. It combines metrics, logs, traces, and continuous profiling with SLO-based alerting and cloud cost monitoring. AI-powered root cause analysis identifies issues and suggests fixes, while deployment tracking compares performance across K8s rollouts.
Tool complexity: Low
Differentiator(s):
- Open-source core: Apache 2.0 licensed, self-hostable.
- AI root cause analysis: Turns hours of debugging into minutes.
- Cost monitoring: Breaks down which apps drive cloud costs.
Don't use if:
- Running high-scale environments with many containers. Coroot's architecture relies on Prometheus (coroot-prometheus) which struggles with high cardinality metrics common in large Kubernetes clusters. Each unique label combination creates a time series consuming ~3-4KB of memory – at 10 million series, you need 30-40GB RAM just for series overhead.
- Want minimal operational overhead. You need to manage both Prometheus and ClickHouse backends, each with their own scaling, tuning, and maintenance requirements.
- Need visibility into ingress traffic from outside the cluster. Coroot's eBPF tracing captures connections where the client is instrumented, so incoming requests from external sources (outside your cluster) won't show server-side trace data or request breakdowns (2XX/4XX/5XX).
- Using service meshes with BoringSSL (Istio/Envoy). eBPF-based TLS tracing has limitations with statically linked SSL libraries – Envoy statically links BoringSSL with stripped binaries, making trace capture unreliable.
Deployment options: Self-hosted (open-source) or Coroot Cloud (SaaS).
3. Dash0
General full observability platform
Pricing: Usage-based (~$0.20/M metrics, ~$0.60/M logs & traces).
Setup time: Minutes to a few hours (depending on existing oTEL setup)
Dash0 is a platform built on open standards (OpenTelemetry, PromQL, Perses). It correlates Kubernetes metrics, logs, and traces with APM, infrastructure monitoring, log management in one product. Their AI SRE agents help with instrumenting your services with oTEL, explain PromQL queries and help during an incident.
Tool complexity: Medium. (Requires familiarity with OTel/PromQL)
Differentiator(s):
- Open standards & portability: 100% OpenTelemetry compatible and PromQL support.
Don't use if:
- You need fast setup and don't want to spend time with manual instrumentation of every service in your cluster.
- You need an on-prem or self-hosted deployment.
- Your team requires a large ecosystem of third-party integrations (newer player with fewer built-in integrations).
Deployment options: Cloud SaaS only (multi-tenant or AWS Marketplace). No self-hosted edition.
4. Grafana Cloud
General full observability platform
Pricing: Metrics: $6.50/ 1k series, Logs, Traces and Profiling: $0.50/GB ingested.
Setup time: Days to weeks. Highly relies on instrumentation and configuration
Grafana Cloud is basically an observability stack of open-source tools: Mimir (metrics), Loki (logs), Tempo (traces), and Pyroscope (profiling) – all visualized through Grafana dashboards. Includes synthetic monitoring (k6) and frontend RUM (Faro). Prebuilt K8s dashboards cover node resources, pod health, and cluster-to-container visibility.
Tool complexity: High. Flexible UI but requires PromQL/LogQL knowledge. Powerful but steep learning curve to unlock full capabilities.
Differentiator(s):
- Open-source alignment: Managed "LGTM" stack with no proprietary agents.
- Extensibility: Plugin ecosystem connects third-party data (databases, cloud services, GitLab, Jira, etc.) for single-pane correlation.
Don't use if:
- You prefer no-configuration solutions (still requires setting up data collection and building dashboards).
- You have a very high volume and cardinality of metrics data. (Known scaling problems with Prometheus)
- You need 24×7 support included (free/pro tiers have limited support).
Deployment options: Cloud SaaS / BYOC / Self-managed OSS components available.
5. Datadog
General full observability platform
Pricing: ~$15/host/mo (infra) + ~$31/host/mo (APM) + ~$0.10/GB logs. Many add-ons (RUM, security, network) for extra.
Setup time: Under an hour. Helm chart deploys DaemonSet agent that auto-discovers pods and collects metrics/logs. Requires custom instrumentation for traces and APM.
Datadog is a comprehensive cloud monitoring and APM platform offering end-to-end visibility: Kubernetes cluster metrics to application traces to front-end performance. APM includes flame graphs and Continuous Profiler for always-on CPU/memory profiling. "Logging without Limits" lets you index what matters and archive the rest. Watchdog and Bits AI automatically surface issues and can explain/remediate incidents.
Tool complexity: Complex. Polished UI but the breadth of features can be overwhelming. Tuning for cost (which logs to index, custom tags) requires planning.
Differentiator(s):
- Comprehensive platform: Infra, APM, logs, RUM, synthetics, database monitoring, network, security, CI/CD visibility – all in one.
- 600+ integrations for AWS, databases, queues, and virtually any service.
- Fast query performance (at a cost with query-based pricing dimension)
Don't use if:
- Budget is a primary concern.
- Datadog is the most expensive solution out there.
- People usually complain about surprise bills and hidden costs.
- Teams often sample logs/traces to keep the costs down but miss out on end to end observability as a result.
- Query-based pricing dimension makes it difficult to predict costs.
- Strict on-prem/air-gapped requirements (primarily SaaS, no general self-hosted option).
- You don't want vendor lock-in. Datadog is a proprietary platform that is not compatible with open-source tools (it has its own querying language etc.).
- You need simplicity. The breadth of features can be overwhelming for minimal use cases.
Deployment options: Cloud SaaS (multi-tenant, multi-region). FedRAMP environment for US Gov. No self-managed version. Agent deployed via Helm/Operator in your cluster.
6. Honeycomb
General full observability platform
Pricing: Event-based. ~$130/mo for 100M events.
Setup time: Hours to days. Custom instrumentation required to send rich events. Separate K8s agent available for cluster data.
Honeycomb is an observability platform focused on high-cardinality, event-driven insights – built for "debugging production with data". Excels at distributed tracing and rich querying of trace/span data. Now also supports logs and metrics with a unified event store.
Tool complexity: High. Query-based UI (requires understanding your data schema).
Differentiator(s):
- Event-based pricing & high-cardinality strength: Send detailed events with many dimensions without sampling. Built for "wide" events (user IDs, feature flags, etc.) without performance penalty.
- Powerful exploratory analytics: BubbleUp heatmaps and trace spanning for rapid outlier isolation.
- Distributed tracing at scale: Ingest billions of spans, slice on any attribute with sub-second queries. Follow requests across microservices with aggregate views.
Don't use if:
- Your team can't instrument code or send custom telemetry (Honeycomb shines with rich, custom events).
- You need fully self-hosted/on-prem (SaaS primary, Private Cloud for enterprise only).
- Very cost-constrained with huge volumes of trivial logs (not designed as cheap log storage).
Deployment options: Cloud SaaS. Private Cloud (managed by Honeycomb) for compliance. No self-managed OSS version.
7. Dynatrace
General full observability platform
Pricing: Complex pricing. Please see here.
Setup time: Under an hour. OneAgent deployed as a DaemonSet auto-instruments applications at bytecode level.
Dynatrace is a platform offering full-stack monitoring from infrastructure to applications. Known for its "OneAgent" auto-instrumentation and the Davis AI engine for automatic root-cause analysis.
Tool complexity: Medium. The UI is powerful but dense. Requires understanding of the data model used by Dynatrace.
Differentiator(s):
- OneAgent auto-instrumentation: Deploy once, get full-stack visibility without code changes or SDK integration. Auto-instruments applications at bytecode level for popular languages (Java, .NET, Node.js, Go).
- Davis AI root-cause analysis: Automatically identifies the root cause of issues without manual investigation. Correlates anomalies across metrics, logs, traces, and events.
- Smartscape topology: Auto-discovered, real-time dependency map of your entire stack – from hosts to processes to services to applications.
Don't use if:
- Cost-sensitive or budget is a primary concern. High log/metric volumes (DDU costs) add up quickly. Complex pricing makes it difficult to predict costs.
- Running unusual or unsupported tech stacks (auto-instrumentation coverage varies depending on the language and framework).
- User feedback for Davis AI is mixed – many say it provides high-level summarization rather than pinpointing exact root causes.
Deployment options: SaaS (Dynatrace-managed) or Managed (Dynatrace software on your infrastructure/private cloud).
Comparison of Kubernetes Observability Tools
| Tool | Category | Pricing | Setup Time | Complexity | AI Features | eBPF Auto-instrumentation | OTel Support | Self-hosted Option |
|---|---|---|---|---|---|---|---|---|
| Metoro | K8s-native | ~$20/node/mo | Under 5 min | Low | ✅ RCA, fixes, deployment verification | ✅ | ✅ | ✅ |
| Coroot | K8s-native | $1/CPU core/mo (OSS available) | Minutes | Low | ✅ RCA | ✅ | ✅ | ✅ |
| Dash0 | General | ~$0.20/M metrics, ~$0.60/M logs | Minutes to hours | Medium | ✅ RCA, PromQL help | ❌ | ✅ | ❌ |
| Grafana Cloud | General | $6.50/1k series, $0.50/GB logs | Days to weeks | High | ❌ | ❌ | ✅ | ✅ |
| Datadog | General | ~$15/host + ~$31/host APM | Under 1 hour | High | ✅ RCA, fix suggestion | limited to network metrics | ✅ | ❌ |
| Honeycomb | General | ~$130/mo per 100M events | Hours to days | Medium-High | ✅ Query assistant | ❌ | ✅ | ❌ |
| Dynatrace | General | Complex (see pricing page) | Under 1 hour | Medium | ✅ Davis AI RCA | ❌ | ✅ | ✅ (Managed) |
Note: Pricing and features may change over time. This table reflects information as of January 2026.
Conclusion
K8s-native platforms (Metoro, Coroot) get you running fast with minimal config. General platforms (Datadog, Grafana, Dynatrace, Honeycomb, Dash0) offer broader coverage but come with more setup time (and higher costs).
How to pick one? Try them out. All tools offer free trials – pick 2-3 that fit your budget and give them a spin.
Metoro is a great option to start with as it's up and running in under 5 minutes – test it yourself
FAQ
What is Kubernetes observability?
Kubernetes observability is the ability to understand the internal state of your Kubernetes clusters and applications by collecting and analyzing telemetry data. Unlike simple monitoring (which tracks predefined metrics), observability lets you ask arbitrary questions about your system's behavior. A complete Kubernetes observability platform typically collects four types of signals: metrics (numeric measurements like CPU usage), logs (event records from containers), traces (request flows across microservices), and Kubernetes events (cluster-level occurrences like pod scheduling or OOM kills).
What are the three pillars of Kubernetes observability?
The traditional three pillars of observability are metrics, logs, and traces. However, for Kubernetes environments, many practitioners now consider Kubernetes events as a fourth pillar since they capture cluster-specific information (deployments, scaling events, resource changes) that the other three don't cover well. Some also add continuous profiling as a fifth pillar for code-level performance insights.
What is the difference between Kubernetes monitoring and observability?
Monitoring is reactive – you define what to watch (CPU > 80%, error rate > 5%) and get alerted when thresholds are breached. Observability is exploratory – you can investigate unknown issues by querying across metrics, logs, and traces without knowing what to look for in advance. Monitoring tells you that something is wrong; observability helps you understand why. Most modern Kubernetes observability tools include monitoring capabilities, but not all monitoring tools provide full observability.
What is the best tool for Kubernetes monitoring?
There's no single 'best' tool – it depends on your requirements. For metrics-only monitoring, Prometheus + Grafana is the industry standard (free, flexible, but requires expertise). For all-in-one observability with minimal setup, platforms like Metoro (eBPF-based, 5-minute setup) or Datadog (comprehensive but expensive) are popular. For cost-sensitive teams, open-source stacks like Prometheus + Loki + Jaeger work well but require more operational overhead. Enterprise teams often choose Dynatrace or Datadog for their breadth of features and support.
How do I monitor a Kubernetes cluster?
To monitor a Kubernetes cluster, you need to collect data from multiple sources: 1. Node metrics: CPU, memory, disk, network at the host level (via node-exporter or eBPF agents) 2. Container metrics: Resource usage per container (via cAdvisor or kubelet metrics) 3. Kubernetes metrics: Pod states, deployment status, replica counts (via kube-state-metrics) 4. Application metrics: Request rates, error rates, latencies (via instrumentation or auto-instrumentation) 5. Logs: Container stdout/stderr (via Fluentd, Fluent Bit, or platform agents) 6. Traces: Request flows across services (via OpenTelemetry or APM agents) You can either assemble these components yourself (Prometheus + Grafana + Loki + Jaeger) or use an all-in-one Kubernetes observability platform that handles collection automatically.
Is Prometheus enough for Kubernetes monitoring?
Prometheus is excellent for metrics monitoring but has limitations: • Metrics only: No logs, traces, or profiling – you need additional tools • Short retention: Default 15-day retention; long-term storage requires Thanos or Cortex • No auto-instrumentation: Applications must expose /metrics endpoints • Scaling complexity: Single-node by default; clustering requires additional components • Manual dashboards: You build and maintain Grafana dashboards yourself For small teams comfortable with operational overhead, Prometheus is often sufficient for metrics. For full observability (especially distributed tracing), you'll need to add more tools or consider an all-in-one platform.
What is the difference between Datadog and Prometheus for Kubernetes?
Key differences: • Cost: Prometheus is free (infra costs only), Datadog is ~$15-45/host/mo + add-ons • Scope: Prometheus is metrics only, Datadog covers metrics, logs, traces, RUM, security • Setup: Prometheus is self-managed requiring expertise, Datadog is managed SaaS with quick setup • Instrumentation: Prometheus requires manual exporters, Datadog has auto-instrumentation • Retention: Prometheus is limited without Thanos, Datadog retention is configurable • Lock-in: Prometheus has none (open-source), Datadog is a proprietary platform Choose Prometheus if you want full control and have engineering capacity. Choose Datadog if you prefer managed infrastructure and can afford the cost.
How do I choose a Kubernetes observability tool?
Consider these factors: 1. Scope needed: Metrics only? Full observability (metrics + logs + traces)? 2. Setup time: Can you invest weeks in configuration, or need something working today? 3. Team expertise: Comfortable with PromQL and managing infrastructure? 4. Budget: Open-source (free but ops overhead) vs. commercial (cost but less maintenance)? 5. Scale: Single cluster or multi-cluster? How many nodes? 6. Kubernetes-native: Does the tool understand K8s constructs (pods, deployments, events)? 7. AI/automation: Do you want AI-assisted root cause analysis or manual investigation? For teams prioritizing fast setup and Kubernetes-native features, purpose-built platforms like Metoro offer the quickest time-to-value. For teams with existing Prometheus expertise, extending that stack may be more practical.
What is eBPF and why does it matter for Kubernetes observability?
eBPF (extended Berkeley Packet Filter) is a Linux kernel technology that allows running sandboxed programs in the kernel without modifying application code. For Kubernetes observability, eBPF enables: • Zero-code instrumentation: Capture HTTP requests, database queries, and network calls without adding libraries to your apps • Low overhead: Kernel-level collection is more efficient than userspace agents • Full coverage: Works with any language, including third-party containers you can't modify • Deep visibility: Can capture data that traditional APM agents miss Tools like Metoro, Cilium, and Parca use eBPF for automatic telemetry collection. The main limitation is that eBPF requires Linux kernel 4.14+ (ideally 5.x+) and may not work in restricted environments like GKE Autopilot.
Can I use OpenTelemetry for Kubernetes monitoring?
Yes, OpenTelemetry (OTel) is the CNCF standard for observability instrumentation. For Kubernetes: • OTel Collector: Deploy as a DaemonSet to collect and export telemetry • Auto-instrumentation: OTel Operator can inject instrumentation into pods automatically • Vendor-neutral: Send data to any compatible backend (Jaeger, Grafana, Datadog, etc.) However, OpenTelemetry is primarily an instrumentation and collection framework – you still need a backend to store and query the data. Many Kubernetes observability platforms (Metoro, Dash0, Honeycomb) are OTel-compatible, letting you use OTel for collection while they handle storage and visualization.