The observability platform market is expanding rapidly—but so is the noise. Gartner projects the market will grow to $14.2 billion by 2028, fueled by surging complexity across hybrid and cloud-native environments. With more than 40 vendors vying for attention, Gartner suggests in its latest Magic Quadrant that it’s an overcrowded field marked by constant upheaval, rising costs, and increasingly skeptical buyers.
Cost-fatigue is reaching a breaking point. Questions about total cost of ownership are now standard, Gartner notes, as customers try to make sense of growing feature sets. The race to differentiate has led to deeper capabilities, but also higher complexity and price tags. In this year’s Magic Quadrant for Observability Platforms, Gartner evaluates 20 standout vendors in a highly competitive space—leaving others, such as Observe, out despite viable offerings. What sets the leaders apart? Innovation in analytics, cost optimization, and AI observability—a fast-emerging capability that helps enterprises monitor and manage generative AI outputs.
“The observability platforms market for mid-2025 is continuing the lively evolution that began during the worldwide pandemic. This year, complying with the Magic Quadrant ceiling of 20 vendors required difficult inclusion decisions, as there was no choice but to leave viable participants out,” the report reads. “We expect to see this level of energy continue, at least in the short term. To date, this has resulted in an ever-improving depth of capabilities and increased options for buyers. There may be a point at which the struggle for differentiation moves beyond more advanced use cases into a fashion show.”
Gartner defines observability platforms as tools that collect and analyze telemetry—logs, metrics, events, and traces—to give IT teams visibility into system performance, reliability, and security. But in today’s climate, visibility alone isn’t enough. The platforms that succeed are those that deliver real value without overwhelming teams or budgets. (See also: 10 network observability certifications to boost IT operations skills)
Critical observability capabilities
Modern observability offerings must go beyond basic monitoring and distinguish themselves through unified platforms that integrate metrics, logs, and traces into a single, correlated view. The shift toward full-stack observability is critical in today’s complex, hybrid, and multi-cloud environments, according to Gartner. This aggregation of data will enable enterprises to more quickly and accurately determine the root cause of issues and perform analysis, significantly reducing mean time to resolution (MTTR).
Another capability becoming more mandatory in observability platforms is the use of AI and machine learning to deliver insights to enterprises. For instance, deterministic AI provides enterprises with capabilities to analyze service dependencies and better perform root-cause analysis. Machine learning is being applied to enable anomaly detection, predictive alerting, noise reduction, and automated incident correlation. AI is being used to power self-healing workflows, alert deduplication, and performance forecasting, which Gartner says will align observability with AIOps practices.
Support for OpenTelemetry and open standards is another differentiator for Gartner. Vendors that embrace these frameworks are better positioned to offer extensibility, avoid vendor lock-in, and enable broader ecosystem integration. This openness is paired with a growing focus on cost optimization—an increasingly important concern as telemetry data volumes increase. Leaders offer granular data retention controls, tiered storage, and usage-based pricing models to help customers
Gartner also highlights the importance of the developer experience and DevOps integration. Observability leaders provide “integration with other operations, service management, and software development technologies, such as IT service management (ITSM), configuration management databases (CMDB), event and incident response management, orchestration and automation, and DevOps tools.”
On the automation front, observability platforms should support initiating changes to application and infrastructure code to optimize cost, capacity or performance—or to take corrective action to mitigate failures, Gartner says. Leaders must also include application security functionality to identify known vulnerabilities and block attempts to exploit them.
Gartner identifies observability leaders
This year’s report highlights eight vendors in the leaders category, all of which have demonstrated strong product capabilities, solid technology execution, and innovative strategic vision. Read on to learn what Gartner thinks makes these eight vendors (listed in alphabetical order) stand out as leaders in observability:
- Chronosphere: Strengths include cost optimization capabilities with its control plane that closely manages the ingestion, storage, and retention of incoming telemetry using granular policy controls. The platform requires no agents and relies largely on open protocols such as OpenTelemetry and Prometheus. Gartner cautions that Chronosphere has not emphasized AI capabilities in its observability platform and currently offers digital experience monitoring via partnerships.
- Datadog: Strengths include extensive capabilities for managing service-level objectives across data types and providing deep visibility into system and application behavior without the need for instrumentation. Gartner notes the vendor’s licensing model that might make it challenging for customers to negotiate contracts, and the cost of the product remains a concern among Gartner customers. Gartner also notes that its tightly integrated ecosystems can make the cost and complexity of integrating with non-Datadog tools a challenge.
- Dynatrace: Strengths include AI-powered automation and root-cause analysis as part of Dynatrace’s AI engine Davis, which can automatically discover and map complex application environments, identify performance anomalies, and pinpoint the precise cause of problems reducing manual effort and MTTR. Gartner warns that new customers might require onboarding assistance due to the sheer number of features and depth of data available. Small to midsize customers might also be challenged to justify the cost of Dynatrace.
- Elastic: Strengths include Elastic’s AI assistant that helps users identify issues and find solutions quickly by querying large volumes of data in a natural language format, and its open-source platform differentiates Elastic from other vendors. Gartner cautions that Elastic’s observability platform isn’t well-known and requires a considerable level of in-house technical expertise. Also, the vendor’s pricing model makes estimating and forecasting usage difficult as data volumes grow, Gartner warns.
- Grafana Labs: Strengths include Grafana’s cost management capabilities that enable customers to control costs by reducing the ingestion of unused or unimportant telemetry, and its extensive footprint lets customers choose a location based on latency requirements and data sovereignty needs. Gartner cautions that customers will need training to ensure they can maximize the value of the platform’s capabilities, and that operations teams must vet and manage third-party components and community-driven plugins.
- IBM Instana: Strengths include IBM’s significant presence within enterprises globally, and Instana is part of the same software group that contains Apptio and HashiCorp, which would create an enterprise bundle for IT operations and automation. IBM also expanded its data center and cloud provider support to include more regions and deployment options. Gartner cautions that IBM introduced fewer new and innovative AI features in 2024 compared to other leaders evaluated, and small or midsize customers are less likely to consider IBM, thinking it’s suited for larger enterprises.
- New Relic: Strengths include a “forward-looking vision for agentic orchestration,” a standardized API for agent integration, and a growing library of specialized agents, which enable intelligent, cross-platform automation. New Relic also enhanced its product portfolio with large language model (LLM) observability, cost controls, and improvements to its generative AI interface. Gartner cautions that New Relic’s consumption pricing can result in larger-than-expected costs for clients.
- Splunk (a Cisco company): Strengths include Cisco’s broad global presence and the combination of Splunk and Cisco provides a deep expertise and a strong client base in many industry verticals. Gartner also notes the extensive investments Cisco and Splunk have made in AI across its entire portfolios, specifically rolling out its Cisco AI Assistant to operate with its observability solutions. Gartner cautions that because Splunk’s observability portfolio has grown through multiple acquisitions, it also has limited integration across its products, creating complexity.
In the remaining quadrants, Gartner names four challengers, four visionaries and four niche players. The report explains each vendor’s background and details strengths and cautions for all of the included companies. The full report is available on Gartner’s site, as well as from vendors (such as here and here).
Source:: Network World