According to the State of Platform Engineering Vol 4, 32.8% of practitioners identify observability as a main focus area, reflecting its critical role in managing distributed systems. The challenge isn't finding tools - it's finding tools that support your dual mandate: maintaining operational visibility while enabling developer self-service. This blog post evaluates 10 observability tools through the lens of platform engineering requirements: OpenTelemetry support, cost optimization, and integration with internal developer platforms.
Why tool selection matters for platform teams in 2026
Traditional monitoring tells you something broke. Observability tells you why. That distinction matters when you're managing Kubernetes clusters, microservices, and distributed systems where a single user request might touch dozens of services across multiple clouds.
Platform engineers face a dual mandate. You need operational visibility into shared infrastructure - CI/CD pipelines, Kubernetes control planes, shared services - while simultaneously enabling developers to observe their own applications without creating tickets or waiting for ops teams. This requires treating observability as a platform capability, not an afterthought.
The strategic shift is clear. Organizations report a 2.6x average ROI from observability spending through improved developer productivity and operational efficiency. 63% plan to increase investment over the next two years. Your tool selection determines whether that investment becomes a force multiplier or another cost center.
Platform engineering evaluation criteria
OpenTelemetry native support and vendor neutrality
OpenTelemetry adoption is non-negotiable for future-proofing. The vendor-neutral standard provides unified APIs and semantic conventions that make telemetry portable across tools. When you enforce semantic conventions like service.name and http.response.status_code, you ensure logs, metrics, and traces remain queryable and reusable regardless of backend.
Look for platforms that support OpenTelemetry natively, not as an afterthought. The best tools embrace OTel's semantic conventions, provide auto-instrumentation capabilities, and integrate cleanly with the OpenTelemetry Collector - your telemetry router and policy engine. This centralized control lets you sample high-volume traces, redact sensitive fields, or drop debug logs without touching application code.
Developer self-service and cost optimization
Cost-fatigue is reaching a fever pitch. Recent market analysis shows that cost discussions now dominate observability tool inquiries. Platform teams need transparent pricing models and data lifecycle management capabilities - sampling, filtering, and retention policies - that prevent runaway bills.
Auto-instrumentation features create paved paths to visibility. Developers should get observability out-of-the-box with smart defaults, not after weeks of manual instrumentation. Integration with CI/CD pipelines and GitOps workflows - treating dashboards and alerts as code - ensures consistency and enables self-service without sacrificing governance.
The goal is simple: remove toil. When developers can deploy instrumented services automatically and access pre-built dashboards without filing tickets, you've succeeded.
10 observability tools to evaluate
Established enterprise platforms
Datadog
Datadog offers comprehensive coverage across APM, infrastructure monitoring, and log management with strong correlation capabilities. The platform excels at topology discovery and provides extensive integration ecosystem with cloud providers and third-party services.
Key strengths:
- Unified platform reducing tool sprawl
- Strong APM with distributed tracing
- Extensive integration ecosystem
Platform engineering fit: Best for teams prioritizing comprehensive coverage and willing to invest in a single vendor. OpenTelemetry support exists but the platform encourages proprietary agents. Cost can scale quickly with data volume.
New Relic
New Relic positions itself as developer-focused observability with programmable platform features. The platform provides query-driven analysis and emphasizes developer workflows over traditional ops tooling.
Key strengths:
- Developer-centric UI and workflows
- Flexible query language (NRQL)
- Programmable platform capabilities
Platform engineering fit: Strong choice for teams emphasizing developer experience. OpenTelemetry support is solid. Pricing model based on data ingestion provides predictability but requires careful data management.
Dynatrace
Dynatrace leads in AI-powered automation and topology discovery. The platform automatically maps dependencies and uses AI to reduce alert noise and identify root causes without manual configuration.
Key strengths:
- Automatic topology discovery
- AI-driven root cause analysis
- Strong enterprise support
Platform engineering fit: Ideal for large enterprises managing complex environments. The automatic instrumentation reduces platform team toil. OpenTelemetry support is available but the platform's strength lies in proprietary agents.
Cloud-native and Kubernetes-focused solutions
Grafana Cloud
Grafana Cloud provides a composable architecture built on open-source foundations: Prometheus for metrics, Loki for logs, Tempo for traces. The platform embraces open standards and integrates naturally with existing Prometheus deployments.
Key strengths:
- Open-source ecosystem and portability
- Strong Kubernetes integration
- Composable architecture
Platform engineering fit: Excellent for teams already invested in Prometheus or prioritizing vendor neutrality. OpenTelemetry support is native. Cost optimization through sampling and retention policies is straightforward. The trade-off is more assembly required compared to all-in-one platforms.
Honeycomb
Honeycomb pioneered query-driven observability designed for complex distributed systems. The platform emphasizes exploratory analysis over pre-built dashboards, enabling teams to ask arbitrary questions of their telemetry.
Key strengths:
- Query-driven exploration
- High-cardinality data handling
- Developer-friendly workflows
Platform engineering fit: Best for teams dealing with complex, unpredictable failure modes. OpenTelemetry is a first-class citizen. The learning curve is steeper but pays dividends for sophisticated debugging. Pricing is based on event volume with transparent cost controls.
Lightstep (ServiceNow Cloud Observability)
Founded by distributed tracing experts, Lightstep brings deep OpenTelemetry leadership and expertise. The platform excels at handling high-volume trace data and providing change intelligence - correlating deployments with performance impacts.
Key strengths:
- OpenTelemetry leadership and expertise
- Change intelligence capabilities
- High-volume trace handling
Platform engineering fit: Strong choice for teams prioritizing OpenTelemetry-native architecture and change correlation. The ServiceNow acquisition brings enterprise support but may concern teams wary of large vendor consolidation.
Emerging OpenTelemetry-native solutions
Dash0
Founded by one of the founders of Instana, Dash0 is built OpenTelemetry-first on a ClickHouse foundation. The tool embraces open standards - PromQL for queries, Perses for dashboards - with a developer-centric UI and simple pricing model.
Key strengths:
- OpenTelemetry-native architecture
- ClickHouse-based performance
- Simple, transparent pricing
Platform engineering fit: Compelling for teams prioritizing vendor neutrality and OpenTelemetry standardization. The emerging player status means less enterprise adoption but also fresh architecture without legacy baggage. Strong fit for platform teams building composable observability stacks.
SigNoz
SigNoz offers an open-source alternative with both self-hosted and cloud options. The tool provides APM, distributed tracing, and metrics in a single interface built on OpenTelemetry and ClickHouse.
Key strengths:
- Open-source with self-hosted option
- OpenTelemetry-native
- Cost control through self-hosting
Platform engineering fit: Ideal for teams with strong operational capabilities who want full control over their observability infrastructure. The self-hosted option eliminates vendor lock-in and provides complete data sovereignty. Cloud option available for teams preferring managed services.
Specialized and innovative approaches
Observe
Observe takes an analytics-driven approach built on Snowflake's cloud data platform. The platform treats observability as a data lake problem, enabling SQL-based analysis and long-term retention at lower costs.
Key strengths:
- Snowflake-based data lake architecture
- SQL-based analysis
- Cost-effective long-term retention
Platform engineering fit: Best for teams already invested in Snowflake or prioritizing analytics-driven observability. The architecture enables correlation with business data stored in Snowflake. OpenTelemetry support is solid.
Coroot
Coroot uses eBPF-based monitoring with automatic service discovery. The platform requires minimal instrumentation, automatically discovering services and dependencies through kernel-level observability.
Key strengths:
- eBPF-based automatic instrumentation
- Zero-code service discovery
- Kubernetes-native architecture
Platform engineering fit: Compelling for teams managing Kubernetes environments who want to minimize instrumentation overhead. The eBPF approach provides deep visibility without code changes. Emerging platform with less enterprise adoption but innovative architecture addressing instrumentation toil.
Your next steps in observability platform engineering
Building composable observability stacks requires standardization through OpenTelemetry. The tools above represent different architectural approaches - all-in-one platforms, composable open-source stacks, data lake architectures, and eBPF-based solutions. Your choice depends on your team's operational maturity, existing investments, and priorities around vendor neutrality versus integrated features.
Start by evaluating 2-3 finalists against your specific context: organization size, cloud environment, existing tooling, and team skills. Consider running proof-of-concept deployments focused on your most critical use cases - incident response workflows, deployment validation, or cost optimization.
The correlation superpower - seamlessly connecting logs, metrics, and traces via shared context - should be non-negotiable. During evaluation, test real incident scenarios: can you navigate from a latency spike in metrics to a specific trace to correlated logs to the deployment change responsible? That fluency reduces MTTR and builds system confidence.
Want to deepen your understanding? Check out the Observability for Platform Engineering course and download the observability whitepaper. Join the Platform Engineering community and connect with peers on Slack.
