Report:

Magic Quadrant for Observability Platforms

Top Vendor Products for Observability Platforms in 2025

• Chronosphere
• Datadog
• Dynatrace
• Elastic
• Grafana Labs
• IBM
• New Relic
• Splunk

Observability Platforms Vendor Product Comparisons 2025

• Amazon Web Services - Challenger
  

  Pros:

  • AI innovation: Amazon announced Amazon CloudWatch investigations to help SREs, IT operations and cloud engineering teams rapidly diagnose, troubleshoot and remediate operational issues. Additionally, AWS launched natural language query generation for Amazon CloudWatch, AWS CloudTrail, Amazon OpenSearch Service and AWS Config, which will enable a wider audience to interrogate and analyze performance issues.
  • Customer experience: AWS provides one of the most comprehensive customer support experiences in the market, with strong custom onboarding support for clients of all sizes, global 24/7 support, vibrant community forums and an industry-leading certification program.
  • Ecosystem: The native integration of AWS observability tools such as CloudWatch provides seamless telemetry collection from AWS services with minimal setup. This enables simple configuration through common UIs, unified management and billing via the AWS console, and consistent security through IAM.

  Cons:

  • Multicloud workloads: Amazon CloudWatch is designed and optimized for integration with AWS services. AWS does provide a limited prebuilt integration for ingestion of metrics from Microsoft Azure, other data sources will need to use the OpenTelemetry (OTel) agent, CloudWatch agent or custom configuration. Users will also need to carefully assess charges, such as egress of metrics from other environments, as well as the cost of ingestion into AWS.
  • Marketing strategy: Unlike many of the vendors in this Magic Quadrant, observability is not the leading product for AWS. As such, AWS marketing efforts specifically for observability lag the Leaders in this research.
  • Costs: Client feedback, on inquiry as well as on Peer Insights, frequently mention high costs associated with Amazon CloudWatch and other tools. While cost challenges are a common issue in observability, the integrated nature of AWS — where services can easily push metrics and logs into CloudWatch — increases the likelihood of unexpected spend. Clients should ensure they are using the appropriate admin tools, such as AWS Cost Explorer and AWS Budgets, and explore the setting for log ingestion frequency and tracing to reduce ingestion costs.


• Apica - Visionary
  

  Pros:

  • Ease of adoption: Apica Ascent does not distribute or require a custom agent to onboard data. Instead, customers can use any or all of a variety of collection technology, including the OpenTelemetry collector, Fluent Bit, Logstash and even the Datadog agent. Flow, the Ascent telemetry pipeline solution, offers additional options to shape and ingest incoming telemetry.
  • Agent fleet management: In addition to data source flexibility, Apica's agent management subsystem, called Fleet, supports automating the installation and configuration of these telemetry collectors. Based on the industry standard OpAMP protocol, Fleet is class-leading and merits evaluation even if Ascent's observability capabilities are not in use.
  • Bring your own storage: Apica Ascent allows customers of its SaaS product to provide their own object storage for their data. This gives Ascent users complete control over the location of their data, which can simplify compliance and supports data sovereignty requirements.

  Cons:

  • Product completeness: Although capable of supporting advanced use cases such as AI observability, Apica Ascent is still evolving and lacks features such as native support for service-level objective (SLO) management and real-user monitoring (RUM).
  • Limited mind share: Apica has been around as a company for 20 years, but it is not well-known as an observability platform provider and is not yet a common choice among Gartner clients.
  • Relative size: Apica's size and revenue is substantially smaller than most of the other participants in this research. Given the competitiveness of this market, sustained viability may be a challenge.


• BMC Helix - Niche Player
  

  Pros:

  • Innovation: BMC Helix has made a number of investments and enhancements to its product suite by embedding AI throughout its products. This includes the BMC HelixGPT, a generative AI solution for assisting with investigation of incidents.
  • Global presence: BMC Helix's customers are geographically distributed, supported by a broad set of local offices, partners in each major region and worldwide hosting choices.
  • Service operations: Although designed to coexist with third-party tools, BMC Helix's approach integrates its own ITSM, discovery and CMDB tools with the observability solutions, opening the potential for significant tool and vendor consolidation.

  Cons:

  • Strategic direction uncertainty: Clients should be aware of operational changes during BMC Helix's ongoing transition into an independent company. This will include potential shifts in account relationships, customer support models and a period of adjustment as both entities solidify their operations.
  • Market execution: BMC Helix marketing has focused on building awareness among existing customers and stakeholders rather than the observability market at large. This may explain why Gartner clients infrequently mention BMC Helix and it does not appear often on competitive shortlists.
  • Product completeness: BMC Helix's solution lacks some features present among leaders in the market, including limited cost-control tools and no support for eBPF. While workarounds can be implemented which can provide some of the missing functionality, it may require additional spending or affect time to value.


• Chronosphere - Leader
  

  Pros:

  • Cost optimization: The Chronosphere control plane enables customers to closely manage the ingestion, storage and retention of incoming telemetry using granular policy controls. Chronosphere's ingestion controls have inspired similar capabilities among other observability platform vendors.
  • Mostly agentless: Chronosphere employs the core maintainers of the popular, open-source telemetry processor and forwarder called Fluent Bit. But the Chronosphere observability platform requires no agents and relies largely on open protocols — such as OpenTelemetry and Prometheus — to ingest telemetry into its platform. Customers can choose the mechanism that is most convenient for a given workload.
  • Highly available: Customers of Chronosphere are provisioned into their own tenant with private storage. This reduces the chance of resource contention between customers and offers an additional layer of security. This has resulted in Chronosphere consistently meeting its 99.9% availability service-level agreement (SLA).

  Cons:

  • Digital experience monitoring: Chronosphere currently offers digital experience monitoring (DEM) via partnerships with Checkly for synthetic monitoring and Sentry for real-user monitoring (RUM). DEM telemetry can be ingested, analyzed and visualized using Chronosphere, just as with any other metrics, events, logs and traces (MELT) telemetry. But generating it and getting it to Chronosphere is a customer responsibility that can be facilitated through the aforementioned partners.
  • Permissions structure: Chronosphere includes a coarse role and permission structure that does not support object-based separation of duties. Organizations with these sorts of requirements would require multiple Chronosphere tenants or the use of a GitOps-based mechanism.
  • Nominal AI: Unlike many vendors in this research, Chronosphere has not emphasized AI capabilities in its observability platform. To date, this has not materially reduced the quality of insights and actions available from the platform, but organizations concerned about the presence of specific AI-based capabilities may choose to evaluate other solutions.


• Coralogix - Visionary
  

  Pros:

  • Storage optimization: The Coralogix TCO Optimizer allows customers to balance cost and performance by supporting policy-driven tiering and retention of log and trace telemetry based on access and usage requirements. This includes use of a customer's own S3 bucket.
  • AI enablement: The Coralogix AI Center comprises tools that monitor the health, performance and security of LLM-based applications. Measurements available include token usage, errors, response quality issues and cost. The current version supports both OpenAI and Amazon Bedrock. Coralogix also includes an onboard GenAI assistant called Cora.
  • Customer support: Coralogix prioritizes customer support and offers 24/7, in-product support with a median response time of 17 seconds and a median resolution time of 1 hour. New customers are assisted with onboarding by a customer success team that may include solution architects, implementation engineers, customer success architects and technical project managers.

  Cons:

  • Learning curve: The Coralogix platform is a highly capable observability tool, but the high degree of capability brings complexity with it. Some customers have reported a steep learning curve and an overwhelming number of options as they familiarize themselves with the product.
  • Fleet management: Coralogix does not offer centralized control over the deployment, configuration, and life cycle of agents or collectors. This may lead to increased operational overhead and hamper scalability for larger implementations.
  • Integrated development environment (IDE) integration: At present, Coralogix lacks a mechanism that allows developers to interact with its observability platform from an IDE for diagnosing production problems. Organizations whose workflow requires that capability may wish to evaluate other solutions.


• Datadog - Leader
  

  Pros:

  • SLO management: Datadog provides extensive capabilities for managing service-level objectives across data types, including historic data replay, error budget alerting and best-practice recommendations.
  • Extended Berkeley Packet Filter (eBPF) use: The platform leverages eBPF Linux kernel technology extensively for a range of use cases, such as application performance monitoring (APM) and cloud workload protection, providing deep visibility into system and application behavior without the need for instrumentation and with minimal overhead.
  • Product analytics: The platform leverages RUM, funnel analysis, session replay and heat maps to address questions that extend beyond traditional application health and performance. These capabilities are positioned to support decision making related to product adoption, user behavior and the prioritization of new-feature development.

  Cons:

  • Licensing model: Determining requirements and negotiating contracts can be challenging due to the extensive and growing suite of product lines offered by Datadog. Limited flexibility across these product lines adds to the complexity of customer budget forecasting.
  • Cost: The cost of Datadog remains a concern among Gartner clients — specifically, log ingestion and retention, and custom metric ingestion at scale. Datadog has responded to these challenges with features such as Flex Logs storage and its "without limits" model.
  • Vendor lock-in: Datadog's ease of use and short time to value can be quite attractive, but its tightly integrated ecosystem can make the cost and complexity of switching away or integrating with non-Datadog tools a challenge for customers.


• Dynatrace - Leader
  

  Pros:

  • Portfolio breadth: Dynatrace offers a wide array of solutions for observability and security, making it particularly attractive for larger enterprises. This includes observability for modern architectures such as Kubernetes, containers, cloud functions and large language models (LLMs), as well as monitoring for legacy enterprise solutions, such as mainframe and SAP monitoring.
  • AI-powered automation and root cause analysis: A core strength of Dynatrace is its AI engine, Davis, which provides automated root cause analysis and predictive modeling. It can automatically discover and map complex application environments, identify performance anomalies and pinpoint the precise cause of problems in real time, significantly reducing manual effort and mean time to repair (MTTR). This automation extends to baselining, detecting anomalies and providing actionable insights.
  • Scalability and enterprise focus: Dynatrace is designed to handle large, complex and dynamic enterprise environments, including microservices, containers and multicloud architectures. Dynatrace can scale to monitor tens of thousands of hosts and millions of dependencies, making it suitable for large organizations with extensive IT landscapes. Its robust architecture and automated approach help manage the complexity and scale inherent in modern enterprises.

  Cons:

  • License model: Dynatrace Platform Subscription (DPS) contracts include many line items not present in previous licensing models. While providing additional capabilities, they also increase the difficulty for procurement to understand and predict observability costs. Clients should ensure that teams are fully aware of the cost-control mechanisms for reporting, alerting and prediction in the Account Management interface.
  • Platform complexity: Due to the sheer number of features and the depth of data available in Dynatrace, new users may require onboarding assistance. To maximize return on investment, organizations must prioritize training and active user adoption. Clients should look to use either vendor or trusted third-party services to assist with initial deployments.
  • Suitability for SMBs: Dynatrace is a fully featured observability platform primarily targeted toward larger enterprises. While alternative channels such as public cloud marketplaces make it more accessible, small and midsize businesses may find that cost justification limits Dynatrace use to monitoring the most business-critical systems.


• Elastic - Leader
  

  Pros:

  • AI augmentation: Elastic's AI assistant helps users identify issues and find solutions quickly by querying large volumes of data in a natural language format, which enables wider access to insights without needing to learn SQL-like language.
  • Product: Elastic's platform provides market-leading capabilities for observability, including strong support for SLOs, fleet management and strong analytics capabilities, which will increase the productivity of IT operations and SRE teams.
  • Vision: Elastic's vision as an open-source platform for not only observability telemetry but also security and enterprise search is differentiated from other vendors in the market.

  Cons:

  • Market awareness and reach: While its search and security products are relatively well-known, awareness and adoption of Elastic Observability is still comparatively low. Elastic is building sales and marketing traction for its observability offering.
  • Required expertise: While Elastic Cloud removes the significant burden of infrastructure management associated with self-managed deployments, unlocking the platform's full potential still requires a considerable level of in-house technical expertise.
  • Pricing estimate: Elastic Cloud's pricing model (based on RAM, storage, data transfer and feature tiers) makes estimating and forecasting usage difficult as data volumes grow. This challenge is somewhat mitigated by the platform's pricing calculators and data tiering capabilities.


• Grafana Labs - Leader
  

  Pros:

  • Cost management: Grafana Cloud's Adaptive Telemetry features — currently supported for logs and metrics — enable customers to control costs by reducing the ingestion of unused or unimportant telemetry. Customers manage system recommendations and can choose to apply them, pause them or configure exceptions. Adaptive Telemetry for traces is on the roadmap.
  • Geographic and CSP footprint: Grafana Cloud is hosted in 25 AWS, Microsoft Azure and Google Cloud Platform (GCP) cloud regions globally at the time this research was published. This extensive footprint enables customers to choose a location based on their latency requirements and data sovereignty needs.
  • Customer experience: Grafana Labs offers high-touch, guided onboarding that includes activation and migration planning, architecture support and training. Beyond onboarding, a variety of support options and communities are available for customers to interact with each other as well as with the company.

  Cons:

  • Learning curve: Users that lack Prometheus experience may find the syntax of configuration files and available options challenging. Organizations should anticipate a need for training to ensure their teams can maximize the value of the platform's emerging capabilities. Grafana Labs is addressing this by offering an increasing number of out-of-the-box templates for popular use cases.
  • Documentation: The open-source foundation of Grafana Cloud is apparent from the documentation. The components of the platform are documented, which is important. However, the docs appear incomplete in places and do not offer much to the beginner.
  • Integration and compatibility: Grafana Labs and the Grafana community have created a large number of data source plugins and prebuilt dashboards; indeed, many advanced features and integrations rely on community-driven plugins. Operations teams must vet and manage the third-party components they incorporate into their Grafana Cloud environments in accordance with their risk management policies.


• Honeycomb - Visionary
  

  Pros:

  • Telemetry management: HTP enables enterprises to efficiently route, filter and sample telemetry data across multiple sources and destinations, helping them scale observability while maintaining control over telemetry life cycle and associated costs.
  • Fleet management: Honeycomb leverages OpAMP to provide fleet management for OpenTelemetry Collectors. This delivers the ability to centrally manage, configure and monitor large-scale OpenTelemetry deployments while minimizing operational overhead.
  • Innovation: In April 2025, Honeycomb announced the acquisition of Grit, an AI codebase analysis engine. Grit's technology allows software development teams to more readily implement OpenTelemetry in their code, thereby improving observability on existing codebases.

  Cons:

  • Licensing model: Honeycomb's differentiated event-based pricing encourages the ingestion of high-cardinality telemetry without incurring penalties. However, it can initially make usage estimation and forecasting challenging.
  • Limited channel ecosystem: Honeycomb primarily operates through a direct sales model, with minimal reliance on channel partners. As a result, enterprises seeking localized expertise or third-party implementation and integration services may find partner options limited.
  • Adoption of AI/ML capabilities: While Honeycomb has emphasized telemetry exploration through features like BubbleUp anomaly detection, its adoption of generative AI has been relatively limited, with Query Assistant being the primary offering. This has left Honeycomb trailing competitors — a gap it aims to close through the recent acquisition of Grit.


• IBM - Leader
  

  Pros:

  • Market execution: IBM has a significant presence within enterprises globally, with sales and support in all major markets. Additionally, within IBM, Instana is part of the same software group that contains Apptio and HashiCorp, making a compelling enterprise bundle for IT operations, automation and FinOps.
  • Geographic strategy: IBM's sales and support teams and its large partner network provide localized customer support in all regions. IBM has also expanded its data center and cloud provider support to include more regions and deployment options, and its tools support multiple languages.
  • Pricing model: In an era where clients are increasingly focused on cost, Instana's pricing model, based on a per-host metric, is simple to understand, scales well with volume and is differentiated in the marketplace.

  Cons:

  • Offering (product) strategy: IBM introduced fewer new and innovative AI features in 2024 compared to other Leaders evaluated in this research. While Instana is exploring the use of agentic capabilities and expanding its AI observability offerings, significant opportunity remains for IBM to strengthen its position in this area.
  • Market perception: When discussing IBM Instana with clients during inquiry as a possible observability platform, many are inclined to disregard it as they perceive it to be only for large enterprises. Small or midsize enterprises are significantly less likely to consider IBM, unless they are already using IBM solutions, and sales opportunities rely on cross-selling from the existing client base.
  • Customer experience: While online forums exist for users to connect, IBM Instana lacks the same level of engagement that exists in other vendor's user communities.


• ITRS - Niche Player
  

  Pros:

  • Pricing model: ITRS's recently revamped pricing strategy provides bundling options for essential and enterprise tiers. As it migrates from multiple products to a single platform solution, ITRS has simplified options into two tiers, targeting larger and more complex IT environments.
  • Real-time alerts: Based on its heritage in financial services, ITRS Analytics supports extremely fast ingestion and alerting. Its claim is less than one second, which may not apply to all situations, but still may be faster than many competitors.
  • Granular RBAC: ITRS's RBAC capability allows for fine-grained, auditable control at the entity level. Access can be restricted based on attributes or conditions. Authorization controls support fine-grained compartmentalizing of commands. This level of control is not commonly available from products in this market.

  Cons:

  • Pricing transparency: Although ITRS has revamped its pricing and begun offering tiered bundling options, a publicly available price list is not available. Prospective customers must contact the vendor directly, making it difficult to estimate costs.
  • GenAI missing: ITRS does not currently include any kind of generative AI or large language model support. Although capabilities are on the roadmap, this places the company behind competitors that are already moving from chat-based assistants to agentic AI.
  • Agent management: ITRS does not have a centralized deployment system for its monitoring agents. Instead, agent installation depends on external tools like Puppet or Chef. While configuration changes can be managed centrally after deployment, the need to handle multiple specialized agents, such as NetProbe, ITRS Infrastructure Agent, and RUM collectors, adds complexity to setup and maintenance.


• LogicMonitor - Challenger
  

  Pros:

  • Customer retention: LogicMonitor's net retention for FY24 was recorded at 108%. The company saw strong expansion with existing customers in the enterprise and MSP segments. The contributing factors to this high retention rate are platform stickiness, rapid time to value and increasing multiproduct adoption.
  • MSP and partner program: LogicMonitor brings in 80% of its revenue from partner programs. Its programs support SIs, VARs, MSPs and technology alliance partners, and it continues to build out its partner capabilities through strategic investment in resources, enablement and joint GTM programs to drive scalable growth.
  • Robust hybrid coverage: LogicMonitor manages hybrid IT environments (on-premises, cloud, edge) using an agentless collection method. This approach effectively supports legacy and modern systems, helping organizations transition from or maintain hybrid environments.

  Cons:

  • Immature SLI/SLO support: LogicMonitor currently trails other observability platforms in the maturity of its SLI/SLO capabilities. The platform lacks a streamlined approach for defining, establishing, and tracking SLIs SLOs, and error budgets — features that are typically sought by SRE practices.
  • DevOps integration: LogicMonitor's DevOps toolchain integration capabilities are limited and lack full development life cycle support, and additional tooling will be needed for CI/CD pipeline-specific capabilities. Its main focus is around observability and incident response, but it does have some integrations such as Ops Notes to track changes, providing some context to issues that have occurred.
  • Real-user monitoring (RUM): LogicMonitor currently does not offer RUM, which may limit visibility into front-end performance and user experience. Organizations needing detailed insights into client-side behavior or user journeys may need to integrate a separate RUM solution or consider platforms with built-in RUM capabilities.


• Microsoft - Challenger
  

  Pros:

  • Log analysis: Azure Monitor Log Analytics is a capable, mature log-management solution that continues to improve. Two updates introduced over the last year include support for a low-cost, third storage tier called Auxiliary Logs and Simple Mode that offers a low/no-code option to obtain insights from log data without learning the Kusto Query Language (KQL).
  • AI-enhanced: Azure Monitor Application Insights includes AI-enabled code optimizations that identify potential performance bottlenecks based on live telemetry and recommend the code changes necessary to resolve them. This is in addition to the existing Microsoft Copilot in Azure.
  • Integrated security monitoring: Microsoft's security monitoring products, Sentinel and Defender, are built on and well-integrated with Azure Monitor, allowing the entire suite to support a unified analytics, reporting and incident response capability across operations and security functions.

  Cons:

  • SLO management: Azure Monitor does not yet include a first-class SLO creation and monitoring capability, and lags competitors in this respect. In its absence, SLO management in Azure Monitor is possible but extremely cumbersome.
  • Inconsistent OpenTelemetry support: Azure Monitor can ingest OpenTelemetry data via an exporter, but support for ingestion of the OpenTelemetry Protocol (OTLP) directly via a collector interface is absent. Client-side SDKs are available in the Azure Monitor OpenTelemetry Distro, and although Application Insights supports the analysis of OTel data, getting the data from applications into Azure Monitor can be quite complicated.
  • Feature preview: Microsoft's practice is to release Azure features in a "preview" state. Some features are in public preview for a year or more. Although these capabilities are available to all customers, support is limited and Microsoft does not recommend preview items for production use. Many Azure Monitor features have been kept in extended public preview.


• New Relic - Leader
  

  Pros:

  • AI vision: New Relic sets itself apart in the observability space with a forward-looking vision for agentic orchestration. By introducing a standardized API for agent integration and a growing library of specialized agents, the platform encourages intelligent, cross-platform automation. Early partnerships with GitHub and ServiceNow highlight the potential for AI-driven workflows.
  • Product: New Relic has made significant enhancements in its product portfolio in the last year, including additional eBPF functionality, LLM observability, additional cost controls and improvements to its generative AI interface.
  • Customer experience: Clients report, both via Peer Insights and during client inquiry, high levels of satisfaction with New Relic for services and support.

  Cons:

  • Sales execution: While New Relic continues to achieve high client renewal rates, its market growth since its acquisition by Francisco Partners and TPG and subsequent privatization appears to have proceeded at a more moderate pace compared to several leading competitors in the observability sector.
  • Telemetry cost control: Consumption pricing for New Relic, based on the number of users and the volume of telemetry ingested can result in larger than expected costs for clients. New Relic's recent introduction of a cost-optimizer capability is designed to assist customers experiencing telemetry spikes.
  • Business metrics: New Relic's Pathpoint provides a view into business-process metrics and "business observability." However, few clients seem to be aware of these features to help them gain insights into their critical business processes.


• Oracle - Challenger
  

  Pros:

  • Oracle database vulnerability detection: Oracle's observability platform offers built-in database vulnerability detection to help organizations identify security risks, misconfigurations and compliance issues across Oracle databases.
  • Sovereign cloud: For enterprises prioritizing data sovereignty, geopatriation, regional compliance or residency requirements, Oracle's observability platform is available in 25 countries. It meets a wide range of certifications, including FedRAMP and GDPR. Organizations can choose from deployment options such as the EU Sovereign Cloud, U.S. Government Cloud or an OCI Dedicated Region hosted directly within their own data center.
  • Onboarding and support: For organizations with skill or resource limitations, Oracle's Observability platform benefits from a comprehensive support framework. This includes an extensive ecosystem of certified channel partners and system integrators, Oracle Observability Professional Certification, certifications for SREs and DevOps teams, and client engagement programs.

  Cons:

  • Limited generative AI integration: Aside from a basic support assistant, Oracle has been relatively slow to embed generative AI capabilities into its observability platform. Other vendors in the evaluation have more deeply integrated generative AI for a broader set of use cases. Oracle plans to address this shortfall with an "'observability assistant" in 2025.
  • Business-centric observability: For teams focused on the resilience of critical business services, Oracle's O&M platform lacks capabilities for measuring business outcomes such as SLOs and error budget management, which are foundational for SRE practices.
  • OCI-centric strategy: Oracle O&M is tightly woven into its OCI strategy, offering greater advantages for OCI-centric users. However, organizations prioritizing a distinct, stand-alone observability platform should evaluate whether this integrated model aligns with their broader vision for observability.


• ScienceLogic - Visionary
  

  Pros:

  • AI innovation: ScienceLogic's strategic emphasis and key market differentiators are its event intelligence and AI capabilities, particularly the Skylar AI suite, with the goal of enabling intelligent, automated operations.
  • Execution and presence in market: ScienceLogic has demonstrated effective market execution, achieving a solid footprint in an increasingly competitive IT operations management landscape through direct sales, a network of global system integrators (GSIs) and a broad ecosystem of channel partners.
  • Customer experience: The company provides strong support and customer success channels, including community discussions, learning services and dedicated customer success managers (CSMs). It also has a "SL360 program" aimed at helping customers realize long-term business value from their investment in the product.

  Cons:

  • SLO management: ScienceLogic lacks native support for defining, tracking and visualizing service-level objectives (SLOs). The absence of a dedicated SLO capability makes it challenging for SRE teams to monitor reliability goals and manage error budgets.
  • Application development support: With its roots in infrastructure and network monitoring, ScienceLogic lags competitors in some application-centric capabilities. Notably, it lacks code-level insights and has fewer integrations with common developer toolsets for DevOps workflows than other evaluated platforms.
  • Geographic coverage limitations: With primary operations and a strong presence in North America and EMEA, ScienceLogic could face challenges when approaching markets in other regions. The company has less of a direct LATAM focus, which represents a potential missed opportunity.


• SolarWinds - Niche Player
  

  Pros:

  • Product: SolarWinds Observability enhances its capabilities by leveraging eBPF technology, specifically providing performance insights into Kubernetes environments. This, in turn, provides detailed visibility into Kubernetes clusters, enabling faster identification of issues than traditional monitoring methods.
  • Pricing model and transparency: To support customers transitioning between the cloud and data centers, SolarWinds offers deployment flexibility, which allows customers to mix deployment options while maintaining a competitive price point.
  • Squadcast acquisition: SolarWinds' recent acquisition of Squadcast offers several benefits, such as incident response and the ability to reduce MTTR, third-party tool integration support and CI/CD pipelines integration support. It also brings AI-driven incident response capabilities such as deduplication to reduce alert fatigue and improve operational efficiency and resilience.

  Cons:

  • Limited AI/ML capabilities: While additional investment in AI/ML is on its roadmap, the SolarWinds platform today includes foundational capabilities such as dynamic thresholds, anomaly detection and GenAI-powered query assist. However, relative to its competitors, the SolarWinds AI/ML capabilities are limited.
  • Agent management: The SolarWinds "unified agent" combines its infrastructure, network and database monitoring agents using a plugin-based framework. However, APM and DEM both require additional agents to be managed.
  • DevOps toolchain integration: SolarWinds' support for DevOps toolchains is rudimentary. It mainly supports agent packages implemented into CI/CD workflows used to build host images and Kubernetes collectors added using infrastructure as code (IaC), but it lacks support for bidirectional communication and complexity in configuration with DevOps toolchains.


• Splunk - Leader
  

  Pros:

  • Operations: Cisco and Splunk have a broad global presence, with sales and support in all major geographic areas. Additionally, both companies bring a much wider ecosystem of sales and implementation partners, meaning that customers are well-served at all stages of deployment.
  • Overall viability: Cisco has a strong track record in observability and monitoring, mainly through its acquisitions such as AppDynamics and ThousandEyes. The combination of Splunk and Cisco has deep expertise and a strong client base in many industry verticals, including banking, tech and public sector.
  • Innovation: Cisco and Splunk have made extensive investments in AI across its entire portfolio, and is rolling out its Cisco AI Assistant to operate with its observability solutions. This generative AI tool will help users quickly build queries, interpret results and gain insights more quickly from observability telemetry.

  Cons:

  • Portfolio complexity: Splunk's observability portfolio, grown through multiple acquisitions, currently exhibits limited integration across its diverse products. This creates complexity, hindering workflows and decreasing operator efficiency.
  • Market execution: Many Splunk clients are unaware of the existence of Splunk Observability Cloud, despite the prevalence of Splunk's logging and security solutions in the market.
  • Cost controls: While Splunk's metrics pipeline management offers new capabilities for managing large metric volumes, clients consistently face difficulties in controlling overall Splunk environment costs, especially when utilizing Splunk Cloud and ITSI.


• Sumo Logic - Niche Player
  

  Pros:

  • Log analytics at scale: For clients whose primary focus is log analytics, Sumo Logic's platform offers targeted capabilities such as Log Search, LogReduce and LogCompare, which facilitate log ingestion and analytics at scale. This is complemented by its Flex Licensing, which is based on analytics rather than the volume of data ingested.
  • Open-source adoption: Sumo Logic makes extensive use of open-source technologies. This includes telemetry ingestion via OpenTelemetry collectors and OTLP, along with support for Telegraf and Fluent Bit. Additionally, it leverages OpenSLO for SLO management and OpAMP for OpenTelemetry collector fleet management.
  • Observability and security: For teams embracing DevSecOps or organizations aiming to unify security and IT operations, Sumo Logic offers a platform that delivers both use cases within a single solution.

  Cons:

  • LLM observability: A notable gap in Sumo Logic's current offering is dedicated support for observing large language models (LLMs). While it offers integration to AWS Bedrock and Google Cloud Vertex AI for metric and log collection, it does not provide LLM-specific observability. Capabilities for analyzing token usage, cost, bias, drift and hallucination are becoming increasingly common across observability platforms.
  • GenAI-based capabilities: Compared to other observability platforms, Sumo Logic was relatively late in introducing an LLM-powered assistant. Its Mo Copilot, launched in December 2024, remains in the early stages of maturity and currently functions primarily as a query assistant for data exploration and visualization.
  • Code debugging: For DevOps teams that need to trace application errors back to their source, the lack of integration between Sumo Logic and integrated development environments (IDEs) can be a limitation.

Reference

• Gartner, Magic Quadrant for Observability Platforms, 7 July 2025, ID G00821166

View Market Definition
View Vendor Movements