The observability market, long dominated by a handful of established giants generating billions in annual revenue, is facing its most significant architectural challenge in years. Groundcover, a four-year-old startup that intentionally spells its name in lower case, has just raised a $100 million Series C round led by One Peak, bringing its total funding to $160 million. The company claims it now has over 250 paying customers and has tripled its annual recurring revenue over the past year. While these are internally reported figures, they signal a growing conviction that the way enterprises monitor their increasingly AI-driven infrastructure needs a fundamental overhaul—one that the incumbents may not be able to deliver.
How AI Is Reshaping the Telemetry Landscape
For the better part of a decade, observability has been treated as a post-production discipline. Engineers deploy applications, monitor logs, metrics, and traces, investigate the occasional incident, and work to improve reliability over time. That workflow, however, is being upended by the rapid adoption of AI-assisted development. Coding assistants generate more code, infrastructure evolves more rapidly, and organizations are deploying increasingly complex distributed systems that blend microservices, Kubernetes clusters, APIs, and large language models. Enterprises are also beginning to operate AI agents that execute multi-step workflows, call external tools, and interact directly with production systems.
Each of these activities generates a vast new stream of telemetry. AI applications introduce layers of observability beyond traditional infrastructure monitoring—prompt execution, model latency, token consumption, retrieval pipelines, tool invocations, and agent behavior. As organizations experiment with autonomous systems, that telemetry becomes invaluable because it provides the only context to understand what an AI system actually did and why. The result is an explosion of operational data that enterprises increasingly want to retain rather than discard.
This creates a direct tension with the pricing models of established observability platforms, nearly all of which charge according to the amount of data ingested. Historically, engineers have responded by sampling traces, shortening retention periods, or simply limiting which data is collected. These approaches reduce costs but also reduce visibility—precisely when AI-driven systems demand more complete operational context.
“We’ve seen telemetry exploding,” groundcover co-founder and CEO Shahar Azulay said during a recent media briefing. “Users are frustrated by not getting all the value from Datadog and similar platforms. They’re limiting the data, siloing it, sampling it.”
Why the Architecture Itself Must Change, Not Just the Features
Most observability vendors have responded to the AI wave by adding AI assistants, AI-powered root cause analysis, and dedicated AI observability features. Datadog, Dynatrace, New Relic, and Grafana have all announced products aimed at helping enterprises monitor AI applications or automate operational tasks. Groundcover acknowledges these developments but argues they do not address what it sees as the more fundamental issue: where telemetry lives and how customers pay for it.
Instead of operating a conventional SaaS platform that stores customer telemetry in vendor-managed infrastructure, groundcover uses a bring-your-own-cloud (BYOC) architecture. Customers keep the data plane—including telemetry storage and processing—inside their own AWS, Microsoft Azure, or Google Cloud environments, while groundcover provides the managed control plane and user experience. A fully self-hosted deployment option is also available.
What is a bring-your-own-cloud (BYOC) architecture, and how does it work in observability?
A bring-your-own-cloud (BYOC) architecture in observability means the customer’s telemetry data—logs, metrics, traces—never leaves their own cloud infrastructure. The vendor provides the software and user interface, but the data plane, including storage and processing, operates inside the customer’s AWS, Azure, or GCP account. This allows organizations to retain full control over their data, which can be critical for compliance and security, and fundamentally changes how the vendor can price its services.
This architectural decision influences nearly every aspect of the company’s strategy. Because customers already pay for their own cloud infrastructure, groundcover can avoid charging based on telemetry ingestion. Instead, pricing is based primarily on monitored hosts, regardless of telemetry volume. The company believes this changes customer behavior. Rather than deciding which logs or traces are too expensive to keep, organizations can theoretically retain complete telemetry for operational analysis, compliance, and AI-assisted troubleshooting.
“We don’t price by data volume,” Azulay said. “We price by the size of the infrastructure.” The distinction matters because AI workloads tend to increase telemetry far faster than they increase the underlying infrastructure itself. That does not make host-based pricing universally cheaper; organizations with light workloads spread across many hosts may find different economics than dense Kubernetes environments generating enormous telemetry volumes. The broader argument is less about cost alone than about predictability. Enterprise infrastructure teams often struggle with observability bills that fluctuate alongside application growth. Groundcover’s model attempts to align pricing more closely with infrastructure planning.
eBPF at the Center of Technical Differentiation
The second pillar of groundcover’s strategy is eBPF, a Linux kernel technology that has rapidly become a crucial building block for modern cloud observability. Instead of requiring developers to manually instrument applications, eBPF allows software running inside the operating system kernel to observe network traffic, system calls, and application behavior with minimal code changes. For organizations operating Kubernetes clusters and cloud-native applications, this reduces instrumentation complexity, significantly shortening deployment times while increasing telemetry coverage.
Azulay argues this becomes especially important as AI systems generate increasingly complex interactions across services. “Our sensor allows us to observe systems very deeply—from infrastructure to application to AI workloads—without developers needing to instrument code,” he said. While eBPF itself is now used by many observability vendors, groundcover’s differentiation lies in combining automatic eBPF collection with customer-controlled storage, OpenTelemetry compatibility, and unified pricing inside a single platform.
Groundcover acknowledges that none of these technologies individually represents a competitive moat. The claimed differentiation is in the combination of eBPF-first collection, managed BYOC architecture, host-based economics, and full-stack observability delivered together.
AI Agents as Both Customers and Users of Observability
Perhaps the most forward-looking aspect of groundcover’s strategy extends beyond traditional monitoring. The company increasingly describes observability as infrastructure for autonomous software development. Historically, observability platforms served human operators investigating production incidents. Groundcover believes that future observability platforms will increasingly serve AI agents as well.
Its Agent Mode product already allows engineers to investigate incidents using natural language across logs, metrics, traces, and Kubernetes events. More importantly, Azulay envisions observability becoming the feedback mechanism that informs coding agents about what actually happened in production. Rather than simply detecting failures after deployment, observability becomes continuous operational context that autonomous systems can use to evaluate changes, identify regressions, and eventually recommend or implement fixes.
“We’re seeing observability moving from being a post-production tool… to people taking context from production and feeding it back to their coding agents so they can write code better,” Azulay said. Today, humans remain in the loop. Agent Mode investigates incidents and surfaces recommendations, but production changes still require human approval. Azulay expects autonomy to increase gradually as organizations become more comfortable allowing AI systems to participate in operational workflows.
The Competitive Reality Groundcover Faces
Groundcover is entering an intensely competitive market populated by vendors with decades of enterprise experience. Datadog alone generated more than $3 billion in annual revenue in 2025. Dynatrace, Cisco’s Splunk business, Grafana Labs, and New Relic all maintain extensive partner ecosystems, mature integrations, and enterprise support organizations that newer entrants cannot easily replicate. Groundcover is not attempting to outscale these incumbents overnight.
Instead, it argues that AI creates an architectural inflection point similar to previous transitions from on-premises infrastructure to cloud-native computing. According to Azulay, many customers initially adopt groundcover to reduce observability costs but increasingly remain because they want unrestricted access to richer telemetry and AI-native workflows. Deployments typically replace incumbent platforms rather than operate alongside them, although the company has not publicly disclosed customer migration data or independent studies validating that claim.
There are important caveats. Revenue growth, customer counts, and enterprise adoption figures originate from groundcover itself. Published customer case studies reporting significant cost savings are vendor-authored and should not be treated as independent validation. The observability market has become crowded—Gartner currently tracks more than one hundred observability products—and nearly every major vendor now markets AI-powered operational capabilities. Success will likely depend less on whether AI matters, which increasingly appears inevitable, and more on whether enterprises conclude that existing architectures remain sufficient for the challenges ahead.
Viewed narrowly, groundcover’s Series C is another large infrastructure funding round. Viewed more broadly, it reflects a growing debate about what observability becomes in an era where software increasingly writes, tests, and operates itself. If AI continues generating exponentially larger volumes of operational data, traditional assumptions about telemetry collection, pricing, and storage may come under increasing pressure. Vendors that built businesses around charging for data ingestion may need to evolve their economics. New entrants, meanwhile, have an opportunity to design around those changing assumptions from the outset. The company’s latest funding round suggests at least some investors believe the next battle in observability will not be fought over dashboards or alerts. It will be fought over who builds the operational data layer that increasingly intelligent software relies upon to understand—and eventually manage—the systems it runs.
