Report:

Magic Quadrant for Observability Platforms

Top Vendor Products for Observability Platforms in 2024

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

Observability Platforms Vendor Product Comparisons 2024

• Chronosphere - Leader
  

  Pros:

  • Ingestion controls and governance: Chronosphere includes a control plane that enables customers to analyze telemetry usage and establish policies and rules for shaping and sampling data at ingestion time. These controls support observability cost optimization and data governance in a way that few other vendors currently can.
  • Open-source-based: Chronosphere supports Prometheus-style collection for metrics and OpenTelemetry for metrics, logs and traces. This facilitates migration for organizations that no longer wish to manage their own Prometheus or Jaeger environments. The Calyptia acquisition brings another open-source tool, Fluent Bit, as well as telemetry pipelines into the Chronosphere portfolio.
  • Single-tenant architecture: 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.

  Cons:

  • Geographic footprint: Although the service is available globally, Chronosphere's delivery platforms are currently hosted in U.S.-based public cloud regions only. This may pose a data sovereignty concern for some and a latency concern for others. A hosting presence in EMEA is planned for 2024.
  • Digital experience monitoring: Chronosphere currently offers no specific digital experience monitoring (DEM) capabilities, such as synthetic monitoring services, or support for inserting real user monitoring (RUM) instrumentation into browser-based or mobile applications. 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.
  • Not self-instrumenting: Chronosphere does not include an agent, so monitored workloads must be instrumented in a manner compatible with Prometheus or OpenTelemetry, or by using a Fluent-Bit-supported format. Chronosphere has built support for ingesting telemetry from the agents of several competing products, so it may be possible to make use of one of those if the most common formats are not available.


• Datadog - Leader
  

  Pros:

  • Overall vision and sales execution: Datadog has rapidly built many new capabilities in APM, log management, DEM, security and software delivery. It resonates well with enterprises looking for best-of-breed solutions. The company's product-led growth (PLG) model has been successful in growing the size of its customer base and expanding customer engagement throughout small and large organizations.
  • Visualization: Datadog's user-friendly visualizations allow users to view all the telemetry in a single pane. It also has widgets for easy customization of dashboards.
  • Product roadmap: Datadog has a strong product roadmap to improve observability, DevSecOps, automation and remediation, software delivery, and governance of telemetry. Its strategy is to have a platform that ties everything together.

  Cons:

  • Pricing: While Datadog provides detailed pricing on its website, some Gartner clients have raised concerns about spend spiraling quickly out of control as usage grows. To address this, Datadog offers configurable ingestion controls to help customers remain within budget.
  • Business model: Datadog's portfolio has grown significantly in recent years, and it now has 20 separate product lines on its website. Each module has its own pricing structure, which can make pricing proposals confusing to understand and negotiate. Datadog has begun to roll out bundled product offerings to simplify price.
  • Fleet management: Datadog's lack of agent fleet management and automated deployment can add complexity to large installations. The company claims these capabilities are under development and in beta status today.


• 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 and cloud functions, as well as monitoring for legacy enterprise solutions, such as mainframe and SAP monitoring.
  • AI advancements: Dynatrace has delivered enhancements to its Davis AI engine. The "hypermodal" approach from Dynatrace embraces the use of AI across its platform, for causal and predictive modeling, as well as deploying new AI assistant technologies using generative AI.
  • Platform extensibility: For those looking to generate insights beyond health and performance monitoring, Dynatrace's AppEngine and AutomationEngine broaden the platform's capabilities by enabling customers and third parties to create custom apps and automation tailored to industry- or business-specific use cases.

  Cons:

  • Purchasing complexity: Dynatrace Platform Subscription (DPS) was introduced in 2023 as a new subscription-based pricing model, with an annual minimum commitment and a drawdown rate card. Many clients have readily adopted this new model, which enables access to any Dynatrace product. However, some procurement departments have been more cautious as they carefully consider the implications of the minimum commitment, the subscription model and access to new Dynatrace components.
  • Slow log adoption: In 2023, Dynatrace released a mechanism for ingesting large volumes of logs. While clients are interested in using this technology to centralize observability data in one platform, Gartner has not seen significant uptake of Dynatrace for bulk log ingestion and analysis.
  • Suitability for SMBs: Dynatrace is a fully featured observability platform primarily targeted toward 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:

  • Broad AI integration: Elastic has embedded AI across its platform. Elastic Observability offers over 30 ML models, custom model integration, and an AI assistant based on retrieval-augmented generation (RAG) that enhances traditional AI and data analytics.
  • Open and flexible: Elastic Observability benefits from its roots in the open-source Elasticsearch data platform, with the ability to ingest, transform and analyze heterogeneous, high cardinality at scale. Its open architecture facilitates an extensible and flexible platform.
  • Deployment model versatility: Elastic Observability is globally accessible across multiple regions, encompassing all major cloud hyperscalers, and also offers a self-hosted alternative with a largely comparable feature set. This versatility addresses the requirements of clients who prioritize data sovereignty and adherence to regional statutory regulations, inclusive of FedRAMP certification for U.S. governmental bodies.

  Cons:

  • Market awareness: While its Search and Security products are relatively well-known, awareness and adoption of Elastic Observability are lower. Elastic is still building sales and marketing traction for its observability offering.
  • Learning curve consideration: Open-source software is often associated with a steep learning curve, which may impact time to value. Elastic Cloud simplifies adoption, but organizations planning to self-host should ensure that sufficient skills are available.
  • Pricing estimate: In contrast to other vendors in this market, Elastic bases its pricing model on compute resources. Although Elastic offers a pricing calculator, comparison during procurement or review, and during forecasting of costs and budgets, can be challenging.


• Grafana Labs - Leader
  

  Pros:

  • Geographic and hosting diversity: Grafana Cloud is hosted in 19 AWS, Microsoft Azure or Google Cloud Platform (GCP) cloud regions globally at time of writing. This impressive footprint enables customers to choose a location based on their latency challenges and data sovereignty needs.
  • Delivery cadence: Grafana Labs has substantially increased the capabilities of Grafana Cloud over the last 12 months. Notable items include application and front-end observability, SLO management and Adaptive Metrics, which supports aggregation or discarding of time series data, rather than ingestion of it, as a cost management technique.
  • Composability: Grafana's ability to connect to other telemetry collection repositories and integrate data visually without moving it or requiring that it be stored in Grafana Cloud enables a composability that is difficult to duplicate with other solutions. Beyond visualization, alerts can be triggered based on telemetry collected elsewhere or on a set of conditions that crosses tool boundaries.

  Cons:

  • Learning curve: As more tools and capabilities are added to the "logs, Grafana, traces, metrics" (LGTM) stack, the more challenging it becomes to master. Configuration often requires manual editing of YAML or JSON, and although documentation is available, it tends to be reference-oriented and not geared toward the beginner.
  • Cost predictability: New Grafana Cloud users may struggle to predict their potential spend on metrics in particular, as "active series" and data points per minute may not be measurements they can easily obtain. Use of the Grafana Cloud free tier may help mitigate this.
  • Revisiting "co-opetition": A previous iteration of this research identified the potentially uneasy relationship that Grafana Labs has with other vendors of observability solutions. This is still a concern, particularly as Grafana Labs grows. Maintaining partnerships with AWS, Google and Microsoft for "managed Grafana" services may act as a buffer between Grafana Labs and competitors, but is something to be aware of.


• New Relic - Leader
  

  Pros:

  • Flexible licensing model: New Relic's classic licensing model, based on ingestion and number of users, offers an alternative for customers discouraged by host-based licensing. Its new compute-based licensing option provides further choice by aligning cost with consumption.
  • AI monitoring: New Relic's newly implemented AI monitoring allows engineers to benefit from observability capabilities across the AI stack. In addition to ML model performance monitoring, AI monitoring also compares cost and performance across large language models (LLMs).
  • Consolidated data platform: New Relic's telemetry data platform (TDP) is a storage and analytics engine optimized for telemetry management and built on its New Relic database (NRDB) technology. It supports high cardinality data and native support for OpenTelemetry.

  Cons:

  • Implementation and configuration complexity: Some clients have indicated that initial setup and configuration is time-consuming, especially if you are not familiar with the tool.
  • Limited geographic footprint: New Relic service delivery platforms are located in the U.S. and EMEA (Germany). The SaaS platforms are largely hosted in AWS, but also include an Azure-based option. Organizations outside North America and EMEA, particularly those in APAC, may need to confirm that the company's offering meets any region-specific performance or sovereignty requirements.
  • Acquisition-related impacts: New Relic's acquisition and shuffle in leadership brings in regular questions regarding product roadmap, customer relationship management and overall stability of the company. New Relic claims to have increased its investment in platform innovation since going private.


• Splunk - Leader
  

  Pros:

  • OpenTelemetry support: Strong support for OpenTelemetry continues to be a strength of Splunk's observability platform. The Splunk OpenTelemetry collector is supported commercially and includes distributions for Linux, Windows and Kubernetes.
  • SLO management: Splunk Observability includes a workflow to assist customers in establishing and managing realistic SLOs via the UI, or on an automated basis using Terraform. Support for alerting on SLO burn rate analysis is included as well.
  • Unified solution: Although the boundaries between components are evident, by more tightly integrating them, Splunk's observability platform supports IT operations, engineering and cybersecurity use cases more seamlessly than many competitors.

  Cons:

  • Acquisition uncertainty: Cisco recently closed its acquisition of Splunk and rationalizing the product lines is a work in progress. The sales process is also uncertain, given the different go-to-market strategies employed by the two organizations.
  • Dissimilar geographic footprint: Splunk Cloud is a superset of the Splunk Observability Cloud regional footprint, with the former having four times the footprint of the latter. Both have presence in North America, EMEA and APAC. Organizations with strict data sovereignty requirements may need to examine these locations more closely.
  • Pricing: Splunk Observability Cloud is licensed by entity (host) or by usage (metric time series and traces analyzed per minute), while Splunk Enterprise is licensed by volume of data ingested or by workload (Splunk Virtual Compute). Pricing comprehension and predictability may be a challenge for customers and prospects.


• Amazon Web Services - Challenger
  

  Pros:

  • Application Signals: In late 2023, AWS introduced CloudWatch Application Signals, the first CloudWatch service centered on monitoring applications. Application Signals allows operators to manage health and performance, and perform triage, by establishing service-level objectives (SLOs) and navigating subsystems and dependencies using service maps.
  • Unified observability: Support for industry standards like OpenTelemetry, Prometheus and Selenium facilitates interoperability and reuse. Although agents are available to collect telemetry, they are not required to do so. Workloads outside AWS can be monitored, and telemetry from a variety of sources visualized using the Managed Grafana service.
  • Geographic footprint and distribution: AWS has a substantial global reach and continues to expand, with four new regions publicly announced, as well as 18 new local zones. As the observability services are available everywhere, customers can decide how and where to store telemetry across its life cycle.

  Cons:

  • Competitive dynamics: The AWS observability tools have markedly improved in recent years with the introduction of capabilities like CloudWatch Application Signals and Internet Monitor. But AWS also acts as the host for many of the other vendors represented in this research and partners with them for sales via the AWS Marketplace. This push-pull relationship is at odds with the aforementioned unification but prioritizes customer choice over time.
  • Cost management: As with most AWS services, observability is a pay-as-you-go offering based on a variety of consumption levers that may not line up exactly with your operating model or use cases.
  • Offering complexity: Depending on how they are counted, there may be between eight and 28 monitoring and observability services available from AWS. Although this represents an impressive diversity of products, it also remains a source of confusion for customers. Improved documentation has been helping customers distinguish CloudWatch components by use case and capability.


• Microsoft - Challenger
  

  Pros:

  • AI-enhanced: Microsoft's alliance with OpenAI has resulted in generative AI capabilities in the form of Copilot interfaces within Azure Monitor. ML support is evident in the Kusto Query Language (KQL) within Azure Monitor, as well as in features that enable customers to create ML pipelines for log data stored in the platform.
  • Prometheus support: As part of the new Azure Monitor Metrics subsystem, Microsoft has released a managed service for Prometheus. It allows customers to directly ingest metrics from supported services such as Azure Kubernetes Service (AKS); remote write from self-managed Prometheus instances and analyze using PromQL; visualize using Grafana; and alert using native alert rules. A metrics explorer with PromQL is in preview at time of publication.
  • Integrated with security monitoring: Microsoft's security monitoring products, Sentinel and Defender, are built upon 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. Such a feature is scheduled for release in 2024. In its absence, SLO management in Azure Monitor is cumbersome.
  • Delayed OpenTelemetry (OTel) collector support: Azure Monitor can ingest OpenTelemetry data via an exporter, but support for ingestion of OpenTelemetry Protocol (OTLP) directly via a collector interface is not yet available. An Azure Monitor edge pipeline, which includes collector support, entered public preview in late April 2024.
  • Prometheus idiosyncrasies: Azure Monitor includes support for Prometheus metrics collection and analysis. There are currently some incompatibilities with open-source Prometheus and other Prometheus-compatible systems.


• Honeycomb - Visionary
  

  Pros:

  • Kubernetes accelerators: In 2023, Honeycomb announced Honeycomb for Kubernetes. This dedicated solution allows SREs to rapidly identify issues within containerized environments.
  • Engaged client base: Honeycomb as a vendor maintains strong connections with its user base, with many opportunities to listen to feedback directly from clients. It provides frequent workshops, an active Slack community and frequent engagement on social media.
  • Geographic presence: In early 2024, Honeycomb announced the deployment of a new SaaS-based instance in Europe. This allows clients to decide whether to send their observability data to the U.S., the EU, or both, allowing them to adhere to local data sovereignty regulations.

  Cons:

  • Enterprise market traction: Although popular with technology creators and cloud-native organizations, Honeycomb has gained limited traction in the broader enterprise. Gartner clients rarely shortlist Honeycomb as a vendor for their observability platform.
  • Technical focus: Honeycomb's messaging targets and resonates strongly with technical specialists, such as SRE and platform teams. This can overshadow its ability to deliver customer and behavioral insights, which may be what the budgetholders in I&O need to hear.
  • Pricing predictability: Honeycomb's pricing is based on event volume ingested into the platform. For clients, estimating the number of events, as well as predicting the growth over a 12-month period, is challenging.


• IBM - Visionary
  

  Pros:

  • Precision telemetry: Instana collects metrics at one-second resolution and claims that notifications will be generated in three seconds. Taken together, these can contribute to improved availability and SLO adherence.
  • Pricing model: IBM Instana continues to offer an easy-to-understand pricing model, based on a per-host metric, and is competitively priced in the market.
  • AI integration opportunities: Instana provides automated observability and applies AI for insight generation and decision making. Instana provides expert knowledge, performs diagnostic and remediation actions, and helps rapidly resolve incidents. Recently, IBM launched watsonx and Concert, both built with generative AI, which provide opportunities for integration.

  Cons:

  • Limited security tools: Instana has limited offerings around security use cases, relying on other parts of the IBM portfolio to provide this functionality. While Gartner defines security as an optional capability for observability platforms, clients should evaluate their own needs regarding an integrated solution.
  • Market awareness/sales strategy: IBM Instana is rarely mentioned in calls with Gartner clients, either as an incumbent solution or as a shortlisted vendor. Existing IBM clients are often unaware of Instana, even when they are looking for an observability solution. While IBM does have an upgrade path to Instana for those clients on legacy IBM IT operations tools, clients may be unaware of it.
  • Log ingestion: Instana's log ingestion capabilities are limited to application logs and containerized environments. Extending to wider environments is part of its 2024 roadmap.


• LogicMonitor - Visionary
  

  Pros:

  • Scalability: LogicMonitor uses agentless collectors installed on hosts that are physically close or on the same network as the resources being monitored. The number of collectors is aligned to the telemetry to be collected and to the capacity of the server on which the collector is installed. The collectors can handle high volumes of data through automatic balancing across a cluster of collectors, thereby sharing load and supporting failover. It offers a 99.9% SLA to its customers in terms of availability of the platform, to ensure monitoring is available at all times.
  • Container support: LogicMonitor has extensive support for containers. It provides an optimized collector for all Kubernetes (K8s) environments that is Container Runtime Interface (CRI)-agnostic. This enables monitoring without requiring any modifications to the container code.
  • Straightforward pricing: LogicMonitor offers simple and competitive pricing that supports enterprises of nearly any size. It is also suitable for organizations offering services to other businesses in a managed service provider (MSP) model.

  Cons:

  • Limited emphasis on observability key capabilities: LogicMonitor is still very infrastructure-focused. Most Gartner client inquiries about LogicMonitor are on infrastructure and network monitoring. Despite recent investments, LogicMonitor lags behind in some capabilities, like DEM, vulnerability and threat detection, compared to its peers in this research.
  • Visualization and dashboards: Although LogicMonitor provides many out-of-the-box monitoring templates, the UI has lagged those of Leaders in the market. LogicMonitor has begun to release a new user interface, which may address this. Multiple clicks are often required to analyze the telemetry and relationships, and to put data in context.
  • Market reach: LogicMonitor has put significant effort into marketing around observability. While its vision incorporates hybrid coverage and integrated AI, its messaging and marketing have not yet translated to growing market considerations, based on Gartner inquiries.


• Logz.io - Visionary
  

  Pros:

  • Telemetry cost management: Logz.io is focused on providing cost-effective observability. Its Data Optimization Hub provides a powerful telemetry cost management layer. Its features include telemetry filters with recommendations, tiered data storage, LogMetrics, trace sampling and visibility into cost at the team/environment level.
  • Pathway from open source: For organizations already familiar with open-source monitoring tools such as Prometheus, Fluent Bit and Telegraf, Logz.io offers a path to observability with a fast time to value by incorporating its existing portfolio into an enterprise-level platform.
  • Geographic presence: Headquartered in EMEA, Logz.io's customer base and operations are geographically diversified. While the majority of customers are in North America and EMEA, the Open 360 observability platform is hosted across multiple global SaaS instances, supports 36 languages, and includes regional data security and compliance controls.

  Cons:

  • Lacking front-end focus: Logz.io prioritizes creating insights based on telemetry collected via OpenTelemetry and other open-source solutions. Front-end visibility via, for example, real-user monitoring and session replay is lacking.
  • Technical focus: Insights provided by observability platforms continue to extend beyond system health and performance. The Logz.io platform retains a technical focus, and is missing capabilities sought by more business-oriented stakeholders, such as business transaction monitoring and funnel analysis.
  • Market visibility: Although Logz.io is experiencing growth in customers, this is largely limited to their target of SMEs. In contrast to other vendors in this research, Logz.io is not mentioned as a competitor among its peers, and rarely appears in vendor shortlists or inquiry calls with Gartner clients.


• ServiceNow - Visionary
  

  Pros:

  • OpenTelemetry support: ServiceNow Cloud Observability was created from the outset to support ingestion, analysis and delivery of health and performance insights based on OpenTelemetry. Advanced OTel capabilities include collector management based on Open Agent Management Protocol (OpAmp), as well as Apache Arrow compression.
  • Market presence: ServiceNow has strong traction in the enterprise market, particularly with larger companies. ServiceNow has integrations with a wide range of common business applications and an established presence in enterprise markets with its ITSM offering. This provides a large installed base with which to position Cloud Observability as a product-led growth opportunity.
  • Comprehensive portfolio: Cloud Observability capabilities are increasingly being aligned with those of the broader ServiceNow ITOM and ITSM solutions. This makes Cloud Observability an attractive option for ServiceNow customers that require an observability platform.

  Cons:

  • Market awareness: Gartner inquiries with ServiceNow clients indicate very low awareness of the Cloud Observability solution, with many clients evaluating other vendors in this report unaware of the presence of ServiceNow's offering in this area. Compared to dedicated Observability vendors, ServiceNow does relatively little to promote Cloud Observability in the market.
  • Portfolio confusion: Cloud Observability sits alongside but separate from other parts of the ServiceNow portfolio. Features such as SLO management, which one might expect to be part of an observability platform, are included in ServiceNow ITOM, which may require a much larger purchase. In May, ServiceNow announced plans to further integrate ITOM and Cloud Observability, as well as new components, such as Service Reliability Management.
  • Roadmap: In comparison with Leaders in this research, the ServiceNow roadmap currently demonstrates limited ambition for its Cloud Observability solution relative to other parts of its portfolio.


• BMC - Niche Player
  

  Pros:

  • Broad installed base: As one of the traditional "Big 4" IT operations technology providers, BMC has significant presence in many large enterprises and government agencies. Its experience delivering at this level and familiarity among existing clients present a potentially easier transition into observability.
  • Service operations: Although designed to coexist with third-party tools, BMC's approach integrates its own ITSM, discovery and CMDB tools with the observability solutions, opening the potential for significant tool and vendor consolidation.
  • Investigation tools: The solution includes a health timeline and health score, which are useful in understanding recent changes in performance of the application or service under observation, as well as enabling quick visual checks on what changed. It also has a "Situation Explanation" capability, which uses a number of composite AI technologies, including BMC HelixGPT, to help explain the likely cause of the issue.

  Cons:

  • Portfolio complexity: BMC's full solution draws on many different components from its portfolio, although individual capabilities can be licensed separately. Demonstrations of functionality relied in some cases upon custom-generated dashboards, with functions such as SLOs not available out of the box.
  • Pricing model: BMC does not make list pricing available publicly, outside of government and cloud marketplace pricing. This is not in line with the trend in the observability space, where clients prefer early access to pricing in order to build cost models and pricing estimates.
  • Existing BMC environments: Much of the BMC customer base uses earlier BMC technologies, such as BMC PATROL and TrueSight. BMC has made investments to modernize its portfolio, such as the cloud-native Helix platforms, as well as the acquisition of Netreo. As such, clients will need clear guidance on the most effective upgrade path for these solutions.


• Oracle - Niche Player
  

  Pros:

  • Global coverage: Oracle has a worldwide presence, with support for multiple languages. This extends to compliance with regional regulatory and data residency requirements through its EU Sovereign Cloud, FedRAMP approval, and OCI Dedicated Region for deployment in customers' own data centers.
  • Holistic Oracle integration: Although primarily centered around OCI, Oracle also prioritizes O&M support for its broader portfolio, including E-Business Suite (EBS) and PeopleSoft running outside OCI. Enterprises reliant on these applications will benefit from tailored dashboards, automated instrumentation and end-user experience monitoring out of the box.
  • Multicloud support: In contrast to other cloud service providers represented in this research, Oracle has consistently maintained a vision for O&M that extends beyond its own ecosystem to include support for multicloud workloads.

  Cons:

  • Market presence: Although Oracle targets O&M's capabilities as a multicloud-capable offering, it has achieved limited success in raising awareness of the solution outside its existing customer base. In contrast to other vendors in this research, knowledge of OCI's native observability platform is not widespread, and it rarely appears as a vendor in Gartner client inquiries.
  • Slow to implement generative AI: 2023 saw the majority of vendors in the Magic Quadrant implementing generative-AI-based capabilities. While OCI offers various avenues to support AI and ML, Oracle has been slow to implement generative AI use cases for observability. At the time of writing, generative AI for O&M is in development.
  • Business-centric observability: SRE and DevOps teams are focused on ensuring continuity of key business services. While Apdex scoring and reporting are supported, O&M is missing the implementation of SLOs, service-level indicators (SLIs) and error budget management needed to support SRE best practices.


• Sumo Logic - Niche Player
  

  Pros:

  • Pricing model: Sumo Logic's new pricing, Flex Licensing, is based on analytics and not data ingestion. Customers are charged for analytics (data scanning and queries); for example, dashboards, log analytics or monitors. Ingest cost is $0.
  • OpenTelemetry adoption: OpenTelemetry collection is used to collect telemetry for all new and existing customers. Sumo Logic provides native integrations and updated workflows for leveraging OpenTelemetry.
  • Points of presence: The platform is available in nine AWS regions across the globe, including North America, EMEA and APAC, to support data residency and data sovereignty requirements.

  Cons:

  • Log centricity: Although Sumo Logic has tracing and metrics capabilities, it is positioned primarily as a log analytics tool. Organizations seeking a tool to provide deep insights into application behavior may be better served by other products in this research.
  • Market momentum: While Sumo Logic has seen an increase in overall revenue, growth in observability platform revenue has been flat since 2022. This contrasts with a 10% growth in the market at large.
  • Lack of native synthetic monitoring: Sumo Logic does not offer native synthetic monitoring capabilities. Customers requiring such features must enlist an additional tool to do so. The platform does, however, support out-of-the-box integration to Catchpoint, including prebuilt dashboards.

Reference

• Gartner, Magic Quadrant for Observability Platforms, 12-Aug-2024, ID G00798774

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