The Gartner® Magic Quadrant™ for Observability Platforms places leading vendors into four different quadrants based on their vision and execution. For CIOs, IT leaders, and platform engineering teams, understanding the Magic Quadrant and the criteria behind it is key to making informed technology decisions.
Inclusion Criteria: What Gets a Vendor Recognized?
The Gartner view of the market focuses on transformational technologies and approaches that address the future needs of end users. Gartner defines observability platforms as products used to understand the health, performance, and behavior of applications, services, and infrastructure. Some mandatory features, as suggested by Gartner, include the ability to:
- Ingest, store, and analyze operational telemetry feeds, including (but not limited to) metrics, events, log, and trace data.
- Identify and analyze changes in application, service, and infrastructure behavior to determine the causes of outages, performance degradation, and quantify their impact on end-user experience.
- Enrich telemetry by providing contextualization, such as topological dependency or service mapping.
- Support the modeling or mapping of relationships between monitored services and their role in business transactions.
- Collect telemetry from public cloud providers (for example, Amazon Web Services, Microsoft Azure, and Oracle Cloud Infrastructure).
- Support interactive exploration and analysis of multiple telemetry types (including traces, metrics, and logs) to generate insights about user and application behavior.
Understanding Critical Capabilities
Gartner also publishes a Critical Capabilities report as an essential companion to the Gartner Magic Quadrant. This methodology provides deeper insight into providers’ product and service offerings by extending the Magic Quadrant analysis. Organizations can use this research to further investigate product and service ratings based on key capabilities set to important, differentiating use cases, including:
- Ingest, optimize, store telemetry: An observability platform’s ability to ingest multiple sources of telemetry, including but not limited to metrics, events, logs, and traces.
- Interoperability: The extent to which the platform supports integrations with other systems and tools involved in the development, deployment, and operations of IT systems.
- Exploration of telemetry: Provides a skilled platform user with ad hoc access to raw telemetry in a manner that supports iterative, hypothesis-driven, and exploratory analysis and facilitates the discovery and classification of user and workload behavior.
- Observability cost control: Capabilities designed to facilitate the understanding, management, and optimization of the cost of observability.
- LLM observability: The ability to monitor, understand, and analyze the behavior and performance of large language models and their workloads in real time or during their development.
- Generate actionable insights: Advanced analytics, AI, and machine learning are able to deliver guided analysis, surface issues, and provide actionable insights about workload behavior that may be predictive or causal.
- User experience and business analysis: The identification and analysis of key business performance indicators, user experience, customer journeys, and user behavior.
- Automated response: The ability of the platform to react to configured, ingested, or detected events by triggering actions, including remediation using internal or external automation frameworks.
Matching Platform Strengths to Business Needs
Not all observability platforms are created equal. Gartner evaluates how well vendors meet critical use cases that align with real-world business scenarios. It has identified several key personas who benefit significantly from observability platforms. According to Gartner, example use-case scenarios or business problems addressed by observability platforms include:
- IT operations: IT operations teams responsible for live production environments are tasked with ensuring that applications and services are available, responsive, and performant at all times — and especially during periods of high demand. Observability platforms allow these teams to be alerted when issues are detected, and make it possible to interrogate the data to identify the underlying cause.
- Platform engineering: Platform engineers’ use of observability platforms resembles that of IT operations as well as software development. Observability platforms help these teams ensure that production environments consistently meet service-level objectives, in addition to supporting data-driven continuous improvement and platform evolution.
- Software development: Development teams use observability platforms as an integrated part of the CI/CD pipeline, providing rapid feedback on code deployments. This enables faster delivery of new features, as well as improved product resilience.
- Business analysis: Business analysts may use observability platforms to understand and analyze key business metrics. These metrics are often specific to the organization and client (for example, a retailer measuring the cost of abandoned shopping carts and average customer spend).
SolarWinds and the Observability Landscape
SolarWinds has been recognized as one of 20 companies in this year’s Observability Platform Magic Quadrant. The company is positioned as a Niche Player in the 2024 Gartner Magic Quadrant for Observability. To read more about SolarWinds Observability and its place in today’s IT management landscape, download the full report here: 2025 Gartner® Magic Quadrant Observability | SolarWinds
Gartner, Magic Quadrant for Observability Platforms, Authored by: Gregg Siegfried, Matt Crossley, Padraig Byrne, Andre Bridges, Martin Caren, 7 July 2025.
https://www.gartner.com/en/research/methodologies/magic-quadrants-research
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