The Cost of Observability Solutions Remains a Major Concern for Businesses
Predicting the expenses associated with observability tools is a complex task. These solutions are often difficult to forecast, sometimes presented in confusing formats, and become harder to manage as their usage scales up. As the demand for comprehensive system visibility grows, so does the anxiety surrounding their costs.
This issue has been emphasized once again in Gartner’s latest Magic Quadrant dedicated to the observability market. The consulting firm had already highlighted the rising prominence of this phenomenon last year, but since then, the concerns among buyers have only intensified. Currently, industry demand for transparent and predictable pricing is at its peak, especially as pricing models shift from traditional per-host schemes to consumption-based plans, increasingly blended with hybrid approaches.
Functional Criteria and Market Leaders
From a functional standpoint, the essential requirements for these solutions have remained relatively unchanged over recent years. Vendors are expected to offer:
– Data ingestion, storage, and analysis of telemetry data—including metrics, events, logs, and traces
– Application, service, or infrastructure behavior identification and analysis
– Enrichment of telemetry with dependency mapping and relationship analysis between components
Additionally, vendors must cover at least four of the following areas:
– Digital Experience Monitoring (DEM) for web applications, mobile apps, and APIs
– Integration capabilities with ITSM, CMDB, automation, and DevOps tools
– Use of machine learning and advanced analytics
– Automated discovery and mapping of infrastructure, network, and application components
– Cost management for workloads and observability solutions
– Business process supervision
– Observability of AI workloads
– Automation of code and configuration changes
– Application security features, including vulnerability detection and exploitation blocking
The top players remain consistent from last year: Chronosphere, Datadog, Dynatrace, Elastic, Grafana Labs, New Relic, and Splunk. IBM also joins this cohort. Conversely, Logz.io and ServiceNow have exited the Magic Quadrant, while five newcomers—Apica, Coralogix, ITRS, ScienceLogic, and SolarWinds—have been added.
Evaluating Execution Capability
Gartner assesses the “Execution” axis, which measures a company’s ability to effectively meet market demands. The current rankings show Dynatrace, Datadog, New Relic, and Amazon Web Services (AWS) at the top, with Dynatrace and Datadog maintaining the same position as last year. Grafana Labs has improved its position notably with a two-place gain, while Elastic climbs by three spots, reflecting improved operational performance.
Conversely, organizations such as Chronosphere and LogicMonitor have seen declines, and new entrants like Coralogix, ScienceLogic, ITRS, SolarWinds, and Apica are establishing their presence.
On the “Vision” front, which evaluates strategic direction, Grafana Labs again leads, improving by six positions this year. Datadog maintains its strong stance, with other key players like Elastic and IBM also climbing in the rankings.
Special Focus: Chronosphere’s Approach and Limitations
Last year, Gartner praised Chronosphere for its monolithic architecture and its ingestion controls that support cost optimization. Its comprehensive support for OpenTelemetry, especially after acquiring Calyptia, added further value. The platform’s dedicated architecture remains advantageous, notably offering high availability with an SLA guarantee of 99.9%, achieved through agentless operation and open protocols.
However, Gartner notes that Chronosphere’s hosting remains US-centric. The platform lacks specialized User Experience Monitoring (UEM or DEM) capabilities and depends on third-party providers like Checkly for synthetic monitoring and Sentry for real user monitoring. This reliance introduces dependencies and potential concerns regarding integrated user experience insights.
Furthermore, Chronosphere’s focus on AI is limited compared to its competitors, and its instrumentation demands unforeseen workloads to be compatible with OpenTelemetry or Prometheus, which could pose integration challenges. The platform also faces criticism for its insufficient differentiation in AI functionalities and for not fully leveraging AI’s potential.
Datadog: Potential Risks of Vendor Lock-in
Datadog continues to excel with rapid feature delivery, especially in visualization and its roadmap targeting DevSecOps and automation integration. Its capabilities in managing Service Level Objectives (SLOs), leveraging eBPF technology, and offering product-oriented analytics are highly regarded.
However, Gartner warns of growing complexity in controlling costs as usage expands, compounded by a proliferation of different product lines that can confuse users. Managing fleet-scale environments also presents difficulties, risking cost overruns and operational inefficiencies. The integration depth within the Datadog ecosystem might also make switching vendors a costly and complex process, raising concerns over vendor lock-in.
Dynatrace: Licensing Complexity and Pricing Concerns
Dynatrace continues to impress with its extensive portfolio, enhanced AI features through the Davis engine, and scalable architecture. Its ability to discover environments automatically and root cause analysis are seen as strong advantages.
Nonetheless, Gartner advises caution regarding Dynatrace’s flexible “drawdown” pricing model, which allows customers to draw from their contracts, but only with a minimum annual commitment. The slow adoption of the 2023 mass log ingestion mechanism and the less-than-ideal suitability for small and medium-sized enterprises (SMBs) remain issues. Additional complexity arises from the subscription-based DPS licensing, which contains many elements that obscure the total costs, making budget forecasting difficult.
Elastic: Cost Estimation Challenges
Elastic earned recognition for integrating AI features, such as customizable models, RAG capabilities, and an open architecture conducive to extensibility. Its platform supports diverse deployment options, including on-premises versions comparable to cloud offerings.
Yet, Gartner highlights the steep learning curve associated with Elastic’s observability solutions, which is often lesser-known compared to its security or search products. Additionally, the resource-based pricing model complicates cost estimation—especially as data volumes grow—due to the difficulty in predicting resource consumption and the significant expertise required to maximize its potential.
Grafana Labs: Community-Driven Dependencies
Grafana Labs stands out with its rapid deployment of new features, broad global coverage through multiple cloud regions, and highly customizable platform architecture. Its telemetry-based cost management, particularly for logs and metrics, is also a notable strength.
However, Gartner emphasizes caution regarding the company’s reliance on community plugins and third-party integrations, which can introduce complexity and unpredictability in configuration and cost management. Also, the configuration process can be challenging, especially for users unfamiliar with Prometheus or complex plugin ecosystems, with documentation inconsistencies posing additional hurdles.
IBM: Market Coverage but Lacking Engagement
Previously classified as a “visionary,” IBM’s observability solutions benefit from extensive geographic coverage, supportive support services, and a partner network. Bundling options with solutions like Apptio and HashiCorp add value—along with transparent, scalable pricing.
Nonetheless, IBM’s offerings are viewed as targeting large enterprises, with less recent innovation in AI functionalities compared to competitors. Its engagement within the user community remains relatively modest, possibly limiting the ongoing co-creation of features and feedback.
New Relic: Watch Out for Cost Surprises
New Relic is recognized for its flexible licensing model, robust monitoring of AI stacks, and its platform built on a proprietary database. Gartner highlights recent significant improvements in observability features for large language models (LLMs), utilization of eBPF, and cost control mechanisms.
However, concerns about configuration complexity persist, especially following the 2023 acquisition by Francisco Partners and TPG. The commitment-based consumption model, which scales with user volume and data ingested, can lead to unexpectedly high costs. Additionally, Pathpoint, focusing on business process observability, has yet to achieve notable market recognition.
Splunk’s Struggle with Limited Brand Awareness
Finally, Splunk previously gained praise for its SLO management, comprehensive OpenTelemetry support, and cohesive platform offerings. This year, Gartner notes that despite its strong support ecosystem and legacy in Cisco’s infrastructure, awareness of Splunk Observability Cloud remains limited among clients.
The integration between Splunk’s enterprise cloud offerings and Cisco’s product ecosystem remains weak. As a result, managing the total cost of ownership proves difficult—particularly when deploying in combination with tools like IT Service Intelligence (ITSI). This disconnect might hinder adoption and value realization.
In summary, while the market for observability solutions continues to evolve with innovative offerings, concerns around cost predictability, vendor lock-in, and implementation complexity remain top challenges for organizations seeking to harness full visibility into their digital environments.
