Sumo Logic announced major innovations in the platform. A disruptive licensing model, native integrations and universal access unleash the inherent power of Sumo Logic’s multi-tenant platform and machine learning capabilities to enable customers to realize their full data insight potential to build, run, secure and manage modern applications, regardless of the underlying infrastructure and technology stack.
Sumo Logic today announced three major innovations that make continuous intelligence available to all users, in a platform that addresses all data types, cloud platforms, applications and infrastructure, delivered as a low TCO, scalable and secure service.
- Sumo Cloud Flex: New Licensing Model
Sumo Cloud Flex provides customers with maximum flexibility to align data consumption, retention and analytics with different use cases and variable seasonality of data; universal access by removing user-based pricing; and full transparency via a real-time usage dashboard.
Sumo Cloud Flex is designed for large terabyte-scale data sets, and is available today in private beta for enterprises with data ingest as low as 500GB.
- Unified Machine Data Analytics: New Native Integrations
Sumo Logic unveiled new integrations to cloud infrastructure services and cloud application development services to support data ingest from a variety of cloud platforms, apps and infrastructure. These include Amazon Web Services, Google Cloud Platform, Heroku, Microsoft Azure and Pivotal Cloud Foundry, in addition to leading on-premises infrastructures, to deliver an analytics layer that provides maximum flexibility and holistic management across the entire modern application and infrastructure stack.
These integrations are available today, making Sumo Logic a cloud-native and modern application management platform providing a unified system to natively ingest, analyze and correlate structured (metrics) and semi-structured (logs) across a diversity of cloud systems, containerization, third party integrations, networks, devices and environments – while remaining data and cloud agnostic.
- Universal Access: New Experience Capabilities
Sumo Logic is providing new experience capabilities, including a contextual and intuitive user interface to improve user productivity and public dashboards, and improved content sharing for faster collaboration with role-based access (RBAC) controls. These user experience enhancements enable Sumo Logic customers to simplify the process of uncovering, sharing and acting on machine data insights, thereby making machine data analytics relevant to technical and non-technical users across the organization. These new experience capabilities are available today.
“We are pushing the boundaries to continuously innovate in order to remove the complexity and cost associated with getting the most value out of data – whether it’s build-it-yourself open source toolkits and legacy on-premises commercial software packages, the data tax associated with legacy licensing models, or the technology limitations that have prevented universal access for all types of data sources and users,” said Ramin Sayar, President and CEO for Sumo Logic. “Real-time machine data analytics is the only way digital businesses can experience the continuous intelligence needed to drive their continuous innovation processes. We’re excited to deliver a powerful and unified machine data analytics platform that democratizes machine data and unlocks the full data intelligence potential of modern applications and infrastructures.”
The Latest
Most organizations approach OpenTelemetry as a collection of individual tools they need to assemble from scratch. This view misses the bigger picture. OpenTelemetry is a complete telemetry framework with composable components that address specific problems at different stages of organizational maturity. You start with what you need today and adopt additional pieces as your observability practices evolve ...
One of the earliest lessons I learned from architecting throughput-heavy services is that simplicity wins repeatedly: fewer moving parts, loosely coupled execution (fewer synchronous calls), and precise timing metering. You want data and decisions to travel the shortest possible path. The goal is to build a system where every strategy and each line of code (contention is the key metric) complements the decision trees ...
As discussions around AI "autonomous coworkers" accelerate, many industry projections assume that agents will soon operate alongside human staff in making decisions, taking actions, and managing tasks with minimal oversight. But a growing number of critics (including some of the developers building these systems) argue that the industry still has a long way to go to be able to treat AI agents like fully trusted teammates ...
Enterprise AI has entered a transformational phase where, according to Digitate's recently released survey, Agentic AI and the Future of Enterprise IT, companies are moving beyond traditional automation toward Agentic AI systems designed to reason, adapt, and collaborate alongside human teams ...
The numbers back this urgency up. A recent Zapier survey shows that 92% of enterprises now treat AI as a top priority. Leaders want it, and teams are clamoring for it. But if you look closer at the operations of these companies, you see a different picture. The rollout is slow. The results are often delayed. There's a disconnect between what leaders want and what their technical infrastructure can handle ...
Kyndryl's 2025 Readiness Report revealed that 61% of global business and technology leaders report increasing pressure from boards and regulators to prove AI's ROI. As the technology evolves and expectations continue to rise, leaders are compelled to generate and prove impact before scaling further. This will lead to a decisive turning point in 2026 ...
Cloudflare's disruption illustrates how quickly a single provider's issue cascades into widespread exposure. Many organizations don't fully realize how tightly their systems are coupled to third party services, or how quickly availability and security concerns align when those services falter ... You can't avoid these dependencies, but you can understand them ...
If you work with AI, you know this story. A model performs during testing, looks great in early reviews, works perfectly in production and then slowly loses relevance after operating for a while. Everything on the surface looks perfect — pipelines are running, predictions or recommendations are error-free, data quality checks show green; yet outcomes don't meet the ground reality. This pattern often repeats across enterprise AI programs. Take for example, a mid-sized retail banking and wealth-management firm with heavy investments in AI-powered risk analytics, fraud detection and personalized credit-decisioning systems. The model worked well for a while, but transactions increased, so did false positives by 18% ...
Basic uptime is no longer the gold standard. By 2026, network monitoring must do more than report status, it must explain performance in a hybrid-first world. Networks are no longer just static support systems; they are agile, distributed architectures that sit at the very heart of the customer experience and the business outcomes ... The following five trends represent the new standard for network health, providing a blueprint for teams to move from reactive troubleshooting to a proactive, integrated future ...
APMdigest's Predictions Series concludes with 2026 AI Predictions — industry experts offer predictions on how AI and related technologies will evolve and impact business in 2026. Part 5, the final installment, covers AI's impacts on IT teams ...