A New Approach to AI Observability: groundcover
Innovative architecture that keeps AI system telemetry in your own cloud.
1A New Player in the Observability Market
groundcover raised $100 million in a round led by One Peak, bringing its total funding to $160 million. The company reports having over 250 paying customers, tripling annual recurring revenue in the past year, and increasingly replacing established observability platforms in enterprise environments. These figures indicate growing momentum in one of enterprise software's most competitive markets.
2Changing Dynamics with AI
Traditional observability platforms are struggling as AI changes fundamental assumptions. groundcover argues that instead of competing feature by feature, the underlying architecture of observability needs to change as AI systems become more autonomous and generate vastly more telemetry. This thesis offers a crucial perspective for understanding the industry's transformation.
3Telemetry Explosion and Infrastructure Challenge
AI-assisted software development has accelerated deployment cycles and increased the use of complex distributed systems. Organizations deploy systems combining microservices, Kubernetes clusters, APIs, and large language models. Simultaneously, they operate AI agents that execute multi-step workflows and interact with production systems. Each activity generates telemetry, leading to an explosion of operational data.
4Challenging Traditional Pricing Models
Organizations face tension with traditional pricing models that charge based on data volume. Engineers resort to sampling traces, shortening retention periods, or limiting data collection to reduce costs. However, these approaches reduce visibility precisely when AI-driven systems demand more operational context.
5Keep Data in Your Cloud Model
groundcover uses a 'bring-your-own-cloud' (BYOC) architecture instead of traditional SaaS platforms that store customer telemetry in vendor-managed infrastructure. Customers keep the data plane in their own AWS, Microsoft Azure, or Google Cloud environments while groundcover provides a managed control plane and user experience. This architecture influences nearly every aspect of the company's strategy.
6Pricing by Infrastructure Size, Not Telemetry Volume
groundcover prices based on monitored hosts rather than telemetry ingestion. This approach changes customer behavior. Organizations can theoretically retain complete telemetry and use it for operational analysis, compliance, and AI-assisted troubleshooting instead of deciding which logs or traces are too expensive to keep.
7Differentiation in a Competitive Market
Companies like Datadog, Dynatrace, New Relic, and Grafana have introduced AI assistants, AI-powered root cause analysis, and AI observability features. groundcover acknowledges these developments but argues they don't address the more fundamental issue: where telemetry resides and how customers pay for it. The company differentiates by offering a full BYOC model.
8Impact of AI Workloads on Telemetry
AI workloads tend to increase telemetry much faster than infrastructure itself. This poses a challenge for traditional pricing models. groundcover's model allows customers to operate with predictable costs regardless of telemetry volume growth, offering a significant advantage for organizations scaling AI applications.
9Implications for the Future of Enterprise AI
groundcover's approach has significant implications for the future of enterprise AI. As AI systems become more autonomous, fully understanding operational context becomes critical. Retaining and analyzing complete telemetry provides the necessary context to understand what AI systems actually did and why, potentially increasing AI adoption and effective use in enterprise environments.
10Pioneering Industry Transformation
groundcover stands out as a pioneer in transforming the observability industry. Its BYOC architecture and pricing model based on infrastructure size rather than telemetry volume address the growing needs of AI-powered systems. This innovative approach challenges established players and could shape the future of enterprise AI.