Observability vendors are racing to build AI-powered operations hubs. Gartner’s new Magic Quadrant for observability platforms highlights the need for AI visibility, telemetry, and cost-management capabilities.
Observability platforms are evolving beyond traditional monitoring as vendors add AI capabilities and cost-management features aimed at helping enterprise organizations better manage increasingly complex IT environments.
Vendors are investing heavily in AI observability, autonomous investigations, cost optimization, and operational intelligence as they try to evolve their platforms into systems that help IT teams understand problems, identify root causes, and determine the best course of action, according to Gartner, which just published its latest Magic Quadrant for Observability Platforms.
Gartner defines the observability category as technologies that help organizations understand and optimize the health, performance, and behavior of applications, infrastructure, services, AI agents, and user experiences by collecting and analyzing telemetry data, such as logs, metrics, events, and traces.
There are 19 vendors that made the cut for Gartner’s new report. Its Leaders quadrant includes (alphabetically) Chronosphere, Coralogix, Datadog, Dynatrace, Elastic, Grafana Labs, IBM, and New Relic. The Challengers are Alibaba Cloud, Amazon Web Services, LogicMonitor, Microsoft, and Splunk. The two Visionaries are BMC Helix and Honeycomb. Those dubbed Niche Players are Apica, HPE, ScienceLogic, and SolarWinds. (For specific vendor strengths and cautions, check out the full Gartner report. Some vendors offer free versions of the report with registration.)
Looking beyond quadrant placement, Gartner advises organizations to evaluate vendors based on their ability to deliver full-stack observability and their “roadmap credibility” in key areas such as AI observability, OpenTelemetry interoperability, and the ability to observe and govern AI agents.
AI observability emerges as a key differentiator
Organizations are increasingly looking for visibility into AI workloads, including token consumption, model latency, response quality, hallucination rates, and other AI-specific performance metrics, according to the report. Gartner identifies AI observability as an emerging requirement, driven by growing enterprise interest in large language models (LLMs), genAI applications, and agentic AI systems.
The report recognizes a growing number of vendors introducing AI-focused monitoring, autonomous investigations, AI agents, and specialized observability capabilities designed to help organizations monitor and govern AI-powered applications and workflows. At the same time, Gartner clarifies that many claims surrounding autonomous operations remain ahead of reality.
“The transition from generative AI assistants to autonomous agents is more complex than vendor marketing suggests,” the report states.
Cost management becomes a top priority
While AI may dominate vendor messaging, Gartner states that telemetry cost management remains one of the top concerns for enterprise buyers.
As organizations collect larger amounts of logs, traces, metrics, and events, observability spending is increasingly attracting attention from finance and procurement teams. Gartner notes that 5% of its clients now spend more than $10 million annually with a single observability provider.
Gartner describes pipeline management as a strategic layer that is becoming central to observability deployments. Vendors that fail to address these cost concerns risk losing customers to vendor-agnostic alternatives focused on telemetry optimization. Organizations increasingly want platforms that can provide cost attribution, utilization insights, and financial metrics that help justify observability investments, according to Gartner.
Gartner projects the observability market will reach $14.3 billion by 2028, driven increasingly by organizations’ need to manage growing telemetry volumes.
OpenTelemetry is table stakes as consolidation continues
The growing impact of open standards is a major shift for observability, Gartner notes.
The widespread adoption of OpenTelemetry and eBPF-based instrumentation has lowered barriers to switching observability providers and made telemetry collection increasingly commoditized, the research firm explains. Gartner says many enterprise buyers now consider OpenTelemetry support a baseline requirement rather than a differentiator.
As a result, vendors are now trying to differentiate themselves through analytics, automation, AI capabilities, and user experience rather than proprietary data collection approaches. That shift is forcing vendors to demonstrate value beyond monitoring and visibility, as buyers seek platforms capable of accelerating troubleshooting, automating investigations, and improving operational outcomes, according to Gartner.
Gartner says market consolidation continues to favor platform-oriented vendors that combine full-stack observability with integrated AI capabilities. Organizations are increasingly looking for unified platforms that can monitor applications, infrastructure, digital experiences, and AI workloads from a single environment.
The rise of operational intelligence
As enterprises modernize applications and expand AI initiatives, organizations want platforms that can not only identify problems but also explain causes, prioritize actions, and potentially automate remediation. Vendors are expanding observability platforms with AI-driven analytics, automation, and governance capabilities that span applications, infrastructure, cloud services, and AI workloads.
For enterprise buyers, the next phase of observability may be defined less by telemetry collection and more by how effectively vendors can transform data into intelligence, automation, and measurable business outcomes.
