The AI agent observability space is taking off — but how can enterprises be sure what observability products and solutions they need?
Observability startup groudcover (lower case «g» intentional) announced this week that it raised $100 million in a round led by One Peak, bringing its total funding to $160 million.
The company says it has more than 250 paying customers, tripled annual recurring revenue over the past year and is increasingly replacing established observability platforms inside enterprise environments. Those are company-reported figures, but together they point to growing momentum in one of enterprise software’s most competitive markets.
That market has long been dominated by companies including Datadog, Dynatrace, New Relic, Splunk and Grafana. Between them, they represent billions of dollars in annual revenue and years of product maturity. Breaking into that group has never been easy.
groundcover’s argument is that artificial intelligence has fundamentally changed the assumptions those platforms were built on.
Rather than competing feature for feature, the four-year-old company is trying to convince enterprises that the architecture underpinning observability itself needs to change as AI systems become more autonomous, produce vastly more telemetry and increasingly participate in software operations. Whether that thesis proves correct remains an open question, but it offers a compelling lens through which to examine how observability is evolving alongside enterprise AI.
AI is turning telemetry into an infrastructure problem
Observability has traditionally been viewed as a post-production discipline. Engineers deploy applications, monitor logs, metrics and traces, investigate incidents, and improve reliability over time.
That workflow is changing.
AI-assisted software development has dramatically accelerated deployment cycles. Coding assistants generate more code, infrastructure evolves more rapidly, and organizations are deploying increasingly complex distributed systems that combine microservices, Kubernetes clusters, APIs and large language models. At the same time, enterprises are beginning to operate AI agents that execute multi-step workflows, call external tools and interact with production systems.
Each of those activities generates telemetry.
The result is an explosion of operational data that organizations increasingly want to retain rather than discard. AI applications introduce additional layers of observability beyond traditional infrastructure monitoring, including prompt execution, model latency, token consumption, retrieval pipelines, tool invocations and agent behavior. As enterprises experiment with autonomous systems, that telemetry becomes increasingly valuable because it provides the context needed to understand what an AI system actually did and why.
For many organizations, this creates tension with pricing models that charge according to the amount of data ingested.
Historically, engineers have often responded by sampling traces, shortening retention periods or limiting which data is collected. Those approaches reduce costs, but they 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.»
Whether that frustration is widespread enough to reshape the market remains to be seen, but the underlying trend is difficult to ignore. AI is making observability less about collecting enough data and more about collecting everything organizations may eventually need.
Rather than adding AI, groundcover argues the architecture itself has to change
Many observability vendors have introduced AI assistants, AI-powered root cause analysis and AI observability features over the past two years. Datadog, Dynatrace, New Relic and Grafana have all announced products aimed at helping enterprises monitor AI applications or automate operational tasks.
groundcover acknowledges those 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 what it calls 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 a managed control plane and user experience. A fully self-hosted deployment option is also available.
While some competitors, including Datadog and a few other observability vendors, do offer limited hybrid or customer-controlled data residency options, these are generally not equivalent to a full BYOC model. In most cases, telemetry is still processed and stored within the vendor’s managed infrastructure, with only partial controls (such as regional data residency, private links, or selective log forwarding) available.
That architectural decision influences nearly every aspect of the company’s strategy.
Because customers already pay for their own cloud infrastructure, groundcover argues it 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 and use it 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 infrastructure itself.
That does not necessarily make host-based pricing universally cheaper. Organizations with relatively light workloads spread across many hosts may find different economics than dense Kubernetes environments generating enormous amounts of telemetry. The company’s own briefing notes that per-host pricing is most advantageous for organizations with high telemetry density and may be less compelling for lightly utilized fleets.
Still, the broader argument is less about cost alone than 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 rather than data generation.
eBPF sits at the center of the company’s technical differentiation
The second pillar of groundcover’s strategy is eBPF, a Linux kernel technology that has rapidly become one of the most important building blocks 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.
That enables faster deployment and broader visibility across infrastructure.
For organizations operating Kubernetes clusters and cloud-native applications, reducing instrumentation complexity can significantly shorten 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 during the briefing.
eBPF itself is hardly unique. Many observability vendors now incorporate it into their platforms.
What groundcover argues differentiates its approach is combining automatic eBPF collection with customer-controlled storage, OpenTelemetry compatibility and unified pricing inside a single platform.
The company’s own research briefing acknowledges that none of these technologies individually represents a competitive moat. The claimed differentiation lies in the combination of eBPF-first collection, managed BYOC architecture, host-based economics and full-stack observability delivered together.
AI agents are becoming both customers—and users—of observability
Perhaps the most interesting aspect of groundcover’s strategy extends beyond traditional monitoring.
The company increasingly describes observability as infrastructure for autonomous software development.
Historically, observability platforms have served human operators investigating production incidents.
groundcover believes future observability platforms will increasingly serve AI agents as well.
Its Agent Mode product 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, the company emphasizes that 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.
That vision reflects a broader trend emerging across enterprise software, where AI agents increasingly span development, testing, deployment and operations rather than functioning as isolated assistants.
Why some enterprises are considering alternatives
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 those 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.
He says 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.
The company’s journalist briefing also urges caution around some performance claims.
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 without additional evidence. The briefing also recommends scrutinizing exactly what metadata leaves customer environments in standard BYOC deployments, rather than assuming that no operational data ever reaches vendor infrastructure.
Those caveats are important because 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.
The larger question investors are betting on
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 alongside customer expectations. New entrants, meanwhile, have an opportunity to design around those changing assumptions from the outset.
groundcover believes that opportunity lies in combining customer-controlled infrastructure, automatic telemetry collection and AI-assisted operations into a platform designed for autonomous software rather than simply adding AI features to existing observability products.
Whether that architectural bet proves durable will depend on enterprise adoption over the next several years.
But 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.
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