AWS
Top 8 Cloud Observability Tools in 2026
What Are Cloud Observability Tools? #
Cloud observability tools monitor and analyze system health by collecting and correlating data like logs, metrics, and traces. Popular examples include CloudQuery, Datadog, Dynatrace, and Splunk Observability Cloud, which offer integrated platforms for visibility into applications, infrastructure, and user experience across cloud environments.
Cloud environments are dynamic, with resources scaling up or down, ephemeral workloads, and distributed architectures. Observability tools capture and correlate data from these complex systems, offering real-time insights that help engineers understand system behavior and quickly pinpoint issues.
These tools integrate logs, traces, events, and metadata from multiple layers of the cloud stack. The primary goal is to allow teams to detect anomalies, troubleshoot problems, maintain service reliability, and optimize cloud resource usage. Unlike legacy monitoring, which often relied on static thresholds and limited context, observability solutions enable proactive detection and root cause analysis of failures, performance bottlenecks, and security risks.
In this article:
Key Features of Modern Cloud Observability Tools #
Distributed Tracing and Service Maps #
Distributed tracing is a core feature in cloud observability, enabling detailed insights into transactions as they move through microservices and complex application stacks. By correlating requests across service boundaries and visualizing their journey, tracing tools help engineers understand latency, bottlenecks, and failure points. Service maps complement tracing by providing a dynamic, visual layout of all interacting components, revealing dependencies and flow patterns across cloud resources.
The implementation of distributed tracing typically leverages open standards like OpenTelemetry, supporting consistent data collection across diverse platforms. Service maps, often generated automatically from tracing data, offer an up-to-date overview of architecture, highlighting unhealthy nodes or unusual traffic patterns.
AIOps and Anomaly Detection #
AIOps capabilities in modern observability tools use machine learning to automate event correlation, anomaly detection, and noise reduction. These algorithms analyze trends and baseline patterns in metrics, logs, and traces, surfacing only the truly unusual or business-critical alerts. By filtering out routine fluctuations, AIOps reduces alert fatigue and helps teams focus on proactive problem-solving.
Beyond basic thresholding, AIOps can forecast capacity issues, recommend remediations, or trigger automated responses to certain types of incidents. Anomaly detection models evolve continuously as they process more data, adapting to normal shifts in usage or application architecture. This continuous learning enhances their reliability and supports more sophisticated use cases such as predictive maintenance or impact analysis.
Real-Time Dashboards and Visualization #
Real-time dashboards are vital for cloud observability, providing instant, actionable views of system health, infrastructure telemetry, and application performance. These dashboards aggregate data from many sources (metrics, logs, traces) and present them via customizable graphs, tables, and alert visualizations. Teams can monitor resource utilization, latency, error rates, or business KPIs at a glance, adjusting thresholds or filters on demand.
Visualization features have advanced to support complex queries, drill-down investigations, and correlation between infrastructure and application layers. Modern dashboards are interactive, enabling users to zoom in on anomalous time windows, overlay different data streams, and share findings with stakeholders.
Cloud-Native Integrations and Multi-Cloud Support #
Cloud-native integrations allow observability tools to connect directly with cloud provider APIs, managed services, and orchestration platforms such as AWS CloudWatch, Azure Monitor, Kubernetes, and serverless architectures. These integrations retrieve telemetry data automatically, reducing the need for manual instrumentation or out-of-band collectors.
Multi-cloud support ensures that organizations running workloads across different cloud providers maintain consistent observability. Tools with multi-cloud capabilities unify disparate telemetry sources into a cohesive view, regardless of whether applications run on AWS, Azure, Google Cloud, or hybrid/on-premises infrastructure.
Security Observability and Compliance Tracking #
Security observability is now a built-in feature of leading observability tools, merging infrastructure telemetry with security-focused events such as access logs, intrusion attempts, or misconfiguration alerts. By correlating security data with application and infrastructure events, teams can detect risks in real time and investigate incidents with full audit trails.
Compliance tracking modules monitor adherence to regulations and security frameworks, such as GDPR, HIPAA, or SOC 2, by automatically analyzing configuration changes, data flows, and access patterns. Observability tools can alert stakeholders when policy violations occur or when sensitive data traverses unexpected paths.
Notable Cloud Observability Tools #
1. CloudQuery #
CloudQuery makes it straightforward to sync information from all of your cloud platforms (including AWS, Google Cloud and Azure) to the database of your choice. It then makes it easy for you to run queries on that data and build dashboards that allow for ongoing observability. With a choice between an on-prem, CLI-based solution and the cloud native CloudQuery Platform, it is straightforward to get up and running, pull insights on your cloud operations and monitor your cloud on an ongoing basis.
Key features include:
- Full-stack visibility: Deep coverage of tables across the major three cloud providers and beyond makes it easy for you to fully monitor your cloud infrastructure and quickly identify anomolies before they prevent problems.
- API-support: CloudQuery offers a wide range of integrations and API support which allows you to use tools that don't offer built-in support.
- Dashboards: CloudQuery's web-based CloudQuery platform includes pre-built dashboarss that make it easy to answer the most common questions about your cloud unfrastructure
- SQL-based querying: Get useful insights into your cloud infrastructure by querying it using standard SQL.
2. Datadog Cloud Observability Platform #
Datadog is a unified observability platform that monitors the health and performance of cloud-based systems across the technology stack. It aggregates and correlates data from infrastructure, applications, services, and security sources into a single, customizable interface.
Key features include:
- Full-stack visibility: Monitor infrastructure, services, applications, and security signals in one interface
- Correlated data analysis: Pivot seamlessly between logs, metrics, traces, and events for faster troubleshooting
- Over 1,000 integrations: Built-in connectors for cloud platforms, services, and third-party tools
- Custom dashboards: Create and tailor visualizations with synchronized tagging for unified insights
- AI-powered detection: Use machine learning (Watchdog) to surface anomalies and performance issues automatically
3. New Relic Intelligent Observability Platform #
New Relic offers an AI-based observability platform to deliver visibility across the technology stack while connecting system health to business outcomes. With over 50 integrated capabilities and support for more than 780 pre-built integrations, the platform unifies telemetry from multiple sources and can scale continuously.
Key features include:
- Intelligent data processing: Automatically understands and correlates diverse telemetry beyond traditional metrics, events, logs, and traces (MELT)
- Business-aligned insights: Maps system performance to business impact, helping prioritize what truly matters
- AI-driven automation: Predicts incidents and orchestrates responses autonomously to minimize downtime
- Unified observability stack: Over 50 built-in features in a single platform, eliminating data and tool silos
- Extensive ecosystem: Over 780 quickstart integrations with support for open-source tools to prevent vendor lock-in
4. Dynatrace Observability #
Dynatrace delivers observability for cloud environments, combining AI, automation, and full-stack context to eliminate blind spots and accelerate problem resolution. Unlike traditional monitoring tools, Dynatrace focuses on causation, not just correlation, turning streams of telemetry into actionable answers.
Key features include:
- Causation-based AI (Davis®): Delivers root-cause analysis automatically, reducing reliance on manual troubleshooting and eliminating noise
- Context-rich observability: Enriches metrics, logs, and traces with topology, user experience, and security data to provide a complete operational picture
- Automatic discovery & instrumentation: OneAgent auto-detects and instruments apps, containers, and infrastructure with no code changes required
- Topology mapping: Dynamically maps entity relationships to understand how components interact and impact each other
- Scalable data collection: Automatically captures high-fidelity telemetry across thousands of services in multi-cloud and microservice environments
5. Elastic Observability #
Elastic Observability is a flexible, open-source observability solution to ingest, analyze, and store telemetry data at scale while using AI to speed up root-cause analysis and reduce operational overhead. Built on the Elasticsearch platform, it combines log analytics, application performance monitoring, infrastructure visibility, and real user monitoring into a unified workflow.
Key features include:
- Agentic AI workflows: AI Assistant and machine learning automatically detect anomalies, highlight patterns, and surface root causes without manual intervention
- Unified telemetry ingestion: OpenTelemetry-compliant and supports ingesting logs, metrics, traces, and events from any source
- Log analytics at scale: Analyze petabytes of log data with high-performance search, custom visualizations, and AI-powered log stream organization
- Application performance monitoring: Stream native OpenTelemetry data without proprietary agents, supporting multiple languages and high-throughput workloads
- Infrastructure monitoring: Out-of-the-box support for over 400 integrations across cloud, Kubernetes, bare metal, and serverless environments
6. Splunk Observability Cloud #
Splunk Observability Cloud is a full-stack, OpenTelemetry-native platform that unifies metrics, logs, and traces to eliminate blind spots and speed up root-cause analysis across environments. Intended for cloud-native, hybrid, and AI-driven systems, it correlates telemetry automatically, enabling faster troubleshooting without the need to switch between tools.
Key features include:
- Unified telemetry correlation: Metrics, logs, and traces are automatically linked in one place for full-stack visibility
- GenAI Assistant: Provides expert troubleshooting guidance and productivity boosts through conversational AI workflows
- NoSample™ Tracing: Captures all trace data for more accurate analysis and detection of edge-case failures
- OpenTelemetry-native: Standardized instrumentation allows full control of telemetry data and avoids vendor lock-in
- AI-powered analytics: Features like Service Maps and Trace Analytics offer guided root-cause analysis and performance insights
7. Prometheus #
Prometheus is an open-source monitoring and alerting solution to collect and store metrics as time series data. It best suits environments where systems need to be monitored without reliance on external storage or distributed infrastructure. It scrapes metrics from instrumented targets over HTTP and stores them locally, useful for machine-level monitoring and dynamic microservices architectures.
Key features include:
- Time series data model: Metrics are stored as timestamped series with key-value labels for filtering and aggregation
- PromQL query language: Flexible and expressive query language designed specifically for working with time series data
- Autonomous operation: Each Prometheus server runs independently without relying on distributed storage or network services
- Pull-based metric collection: Actively scrapes targets over HTTP, which simplifies configuration and avoids overloading endpoints
- Push support via gateway: Allows support for short-lived or batch jobs by pushing metrics through an intermediary Push gateway
8. Grafana Cloud #
Grafana Cloud is a full-stack observability platform that unifies metrics, logs, traces, and profiles into a single, flexible view. Designed around open standards like OpenTelemetry, Grafana Cloud gives teams control over their observability data without vendor lock-in. It offers AI onboarding, adaptive telemetry, and integration with existing tools.
Key features include:
- Unified telemetry platform: Monitor metrics, logs, traces, and profiles together in one place with built-in visualization tools
- AI-assisted onboarding: Get started quickly with guided setup and intelligent workflows that reduce time to insight
- Adaptive telemetry: Automatically prioritize and retain the most valuable data, cutting waste and saving on observability costs
- Open and extensible: Based on open standards like OpenTelemetry, with support for custom plugins and third-party integrations
- No vendor lock-in: Bring your own tools, cloud, and data sources. Grafana Cloud works with the existing stack
Evaluating and Selecting the Right Cloud Observability Tool #
Selecting the right cloud observability tool depends on aligning the tool’s capabilities with your organization's architecture, operational model, and observability maturity. While many platforms offer similar core features, the right choice often hinges on deeper considerations around scalability, integration, cost, and user workflows.
Here are key considerations when evaluating cloud observability tools:
- Telemetry volume and retention requirements: Assess how much data your systems generate and how long you need to retain it. Some tools charge based on data volume or retention time, which can significantly impact total cost of ownership at scale.
- Granularity and fidelity of data: Look for tools that support high-resolution metrics and full-fidelity traces without sampling. This ensures accurate troubleshooting and helps capture rare or intermittent issues.
- Integration with existing stack: Consider native integrations with your current cloud providers, container orchestration systems (e.g., Kubernetes), CI/CD pipelines, and service meshes. Seamless integration reduces setup time and improves data continuity.
- Open standards support (e.g., OpenTelemetry): Tools built around open standards promote portability, reduce vendor lock-in, and simplify instrumentation across services. Ensure the platform can ingest and export telemetry in open formats.
- Data correlation and root cause analysis: Evaluate how well the tool links metrics, logs, and traces into cohesive narratives. Advanced platforms use AI to surface causal relationships, not just temporal correlations.
- Multi-tenant and multi-cloud visibility: For enterprises with multiple teams or cloud providers, ensure the tool supports secure isolation between tenants and unified visibility across environments.
- Security and compliance capabilities: If your systems operate under regulatory constraints, prioritize tools with built-in compliance reporting, access auditing, and security telemetry correlation.
- Customization and extensibility: Check for flexible dashboards, custom alert logic, and APIs that allow integration with external systems or internal tooling.
- Pricing transparency and scalability: Compare pricing models (per-host, per-event, or usage-based) and understand how costs scale with environment growth. Look for cost-control features like adaptive sampling or data prioritization.
- User experience and collaboration features: Evaluate UI responsiveness, search capabilities, and collaboration tools (e.g., shared dashboards, annotation support). Efficient interfaces enhance incident response and cross-team coordination.
Making the right choice involves piloting shortlisted tools in your actual environment, measuring performance impact, and confirming that the observability solution aligns with your operational goals and engineering practices.