Skip to main content

groundcover Acquires Wand

groundcover has acquired Wand, a Kubernetes resource optimization platform. 

The acquisition, groundcover's first, moves the company from giving engineers and AI agents the context to understand production toward giving software the ability to safely act on it. Wand's founding team and employees will join groundcover. Terms of the deal were not disclosed.

The acquisition brings together two capabilities groundcover believes will converge as infrastructure becomes more autonomous: the context required to understand production, and the ability to act on it safely.

groundcover captures high-fidelity telemetry across applications and infrastructure using eBPF and OpenTelemetry, retaining that production history inside the customer's own cloud through its BYOC architecture. That data already gives engineers and AI agents the context to investigate issues, understand changes and determine what happened in production. Wand adds a different capability: software that uses production behavior to continuously make and execute decisions about live Kubernetes infrastructure.

"Observability has become much more than a tool engineers use to understand what went wrong," said Shahar Azulay, CEO and co-founder of groundcover. "In an AI-native software development cycle, production data becomes context that agents use to build, troubleshoot, fix and operate software. But understanding is only part of the problem. For infrastructure to become autonomous, systems also need to act safely on what they know. Wand has spent years building exactly that capability for Kubernetes."

Wand's in-cluster technology continuously analyzes workload behavior and makes vertical, horizontal and cluster-level resource decisions together, evaluating the cluster as a whole rather than one workload at a time. Rather than generating right-sizing recommendations for engineers to implement later, Wand adjusts CPU and memory allocation on live infrastructure as demand changes. It installs as a Helm chart, so teams do not take on another set of thresholds and tuning rules to maintain. Wand optimizes for performance and availability first, with cost efficiency following from running the cluster well and removing unnecessary headroom.

"Right-sizing assumes we know the future," said Shon Lev-Ran, CEO and co-founder of Wand. "Spoiler: we don't. Infrastructure changes continuously. Joining groundcover lets us combine Wand's ability to act on live infrastructure with a much deeper understanding of what is happening across production."

groundcover's architecture captures telemetry across applications and infrastructure without requiring teams to instrument every service first, and retains high-fidelity production data without forcing teams to decide in advance what to sample or discard. That history gives engineers and agents more basis for understanding what normal looks like, what changed, and how one part of production affected another.

groundcover is also extending that context beyond telemetry, connecting production data with the engineering systems where information about code, deployments, incidents and decisions lives. An agent can start with something happening in production and understand not only what changed, but why it changed and what happened afterward.

Wand's engine also runs inside the customer's environment, close to the workloads and telemetry it uses to make decisions. The two companies arrived at similar architectural principles independently: as software takes on more responsibility for live production, both the data informing those decisions and the systems acting on them should stay under the customer's control.

groundcover will apply Wand's technology in two areas.

First, groundcover will use Wand to make its own BYOC data plane more efficient and resilient. Because groundcover runs inside customer cloud environments, the resources it consumes appear directly on customer cloud bills. Making storage and processing more efficient lowers that cost, which in turn lets customers economically retain more telemetry at full fidelity — giving engineers and agents more context to work with.

Second, groundcover will bring Wand's Kubernetes resource optimization to groundcover customers, extending the platform from observing infrastructure toward operating it.

"We're bringing together the context required to understand production and the technology required to act on it," said Azulay. "You need to see what's happening before you can understand it, and understand it before you can safely act. That's the foundation we're building for autonomous infrastructure." 

The Latest

For decades, traditional observability tools have forced IT professionals to sift through endless dashboards, turning a flood of alerts into actionable insight and driving widespread burnout. While enterprises adopted AI to eliminate this friction, most still deploy it reactively rather than proactively ... As systems become more complex and IT moves to the center of business strategy, a reactive posture is unsustainable ...

Ask an operations team what they monitor and you will hear a familiar list: latency, errors, throughput, uptime. Those signals still matter for AI, but they no longer answer the most important question. As Krishna Sai, CTO of SolarWinds, puts it: "Traditional observability tells you a system is running. AI observability tells you whether it's still reasoning soundly. Those are completely different questions" ...

AI observability serves several roles across an organization. AI engineers use it to build, evaluate, and improve AI systems. SREs use it to operate those systems reliably and efficiently in production, including managing their cost. Leaders need it to connect AI behavior and operational performance to business outcomes. The common need is a clear understanding of whether AI systems are delivering the intended result ...

Part 4 outlines the challenges enterprise face when adopting AI observability. The main challenge is that AI complexity compounds at every stage of maturity. LLM-based applications introduce new behavior, dependencies, and quality considerations, while Agentic AI adds further failure modes as systems make decisions and take actions ...

Part 3 reports on the current state of AI observability. Most experts agree: enterprises are moving faster with AI than they are with the operational discipline needed to run it safely ...

When discussing AI Observability, one of the first questions that arises: Is this just an extension of traditional observability or is this a whole new type of tool? Part 2 compares traditional observability with AI observability ...

In this extensive 14-part series that will post over the next three weeks, experts across observability and AI will provide insight and opinions on the need for AI Observability, the challenges, the benefits, the current state, and predictions for the future ...

Amid rising complexity, organizations are moving quickly toward agent-driven operations: AgenticOps, according to The Impact of Agentic AI on Network Operations, a new report from Cisco ...

AI, automation, and enterprise software give organizations an extraordinary amount of leverage. Work that once required hours can happen in minutes. Information buried across systems can be analyzed almost instantly. Complex workflows can be coordinated across teams with far less manual intervention. That leverage creates a temptation to start with the technology ...

Everyone wants to talk about models. Compute clusters. Benchmark scores that climb a fraction of a point and make headlines anyway. It's an easy conversation to have because it's measurable. But it's also increasingly beside the point ...

groundcover Acquires Wand

groundcover has acquired Wand, a Kubernetes resource optimization platform. 

The acquisition, groundcover's first, moves the company from giving engineers and AI agents the context to understand production toward giving software the ability to safely act on it. Wand's founding team and employees will join groundcover. Terms of the deal were not disclosed.

The acquisition brings together two capabilities groundcover believes will converge as infrastructure becomes more autonomous: the context required to understand production, and the ability to act on it safely.

groundcover captures high-fidelity telemetry across applications and infrastructure using eBPF and OpenTelemetry, retaining that production history inside the customer's own cloud through its BYOC architecture. That data already gives engineers and AI agents the context to investigate issues, understand changes and determine what happened in production. Wand adds a different capability: software that uses production behavior to continuously make and execute decisions about live Kubernetes infrastructure.

"Observability has become much more than a tool engineers use to understand what went wrong," said Shahar Azulay, CEO and co-founder of groundcover. "In an AI-native software development cycle, production data becomes context that agents use to build, troubleshoot, fix and operate software. But understanding is only part of the problem. For infrastructure to become autonomous, systems also need to act safely on what they know. Wand has spent years building exactly that capability for Kubernetes."

Wand's in-cluster technology continuously analyzes workload behavior and makes vertical, horizontal and cluster-level resource decisions together, evaluating the cluster as a whole rather than one workload at a time. Rather than generating right-sizing recommendations for engineers to implement later, Wand adjusts CPU and memory allocation on live infrastructure as demand changes. It installs as a Helm chart, so teams do not take on another set of thresholds and tuning rules to maintain. Wand optimizes for performance and availability first, with cost efficiency following from running the cluster well and removing unnecessary headroom.

"Right-sizing assumes we know the future," said Shon Lev-Ran, CEO and co-founder of Wand. "Spoiler: we don't. Infrastructure changes continuously. Joining groundcover lets us combine Wand's ability to act on live infrastructure with a much deeper understanding of what is happening across production."

groundcover's architecture captures telemetry across applications and infrastructure without requiring teams to instrument every service first, and retains high-fidelity production data without forcing teams to decide in advance what to sample or discard. That history gives engineers and agents more basis for understanding what normal looks like, what changed, and how one part of production affected another.

groundcover is also extending that context beyond telemetry, connecting production data with the engineering systems where information about code, deployments, incidents and decisions lives. An agent can start with something happening in production and understand not only what changed, but why it changed and what happened afterward.

Wand's engine also runs inside the customer's environment, close to the workloads and telemetry it uses to make decisions. The two companies arrived at similar architectural principles independently: as software takes on more responsibility for live production, both the data informing those decisions and the systems acting on them should stay under the customer's control.

groundcover will apply Wand's technology in two areas.

First, groundcover will use Wand to make its own BYOC data plane more efficient and resilient. Because groundcover runs inside customer cloud environments, the resources it consumes appear directly on customer cloud bills. Making storage and processing more efficient lowers that cost, which in turn lets customers economically retain more telemetry at full fidelity — giving engineers and agents more context to work with.

Second, groundcover will bring Wand's Kubernetes resource optimization to groundcover customers, extending the platform from observing infrastructure toward operating it.

"We're bringing together the context required to understand production and the technology required to act on it," said Azulay. "You need to see what's happening before you can understand it, and understand it before you can safely act. That's the foundation we're building for autonomous infrastructure." 

The Latest

For decades, traditional observability tools have forced IT professionals to sift through endless dashboards, turning a flood of alerts into actionable insight and driving widespread burnout. While enterprises adopted AI to eliminate this friction, most still deploy it reactively rather than proactively ... As systems become more complex and IT moves to the center of business strategy, a reactive posture is unsustainable ...

Ask an operations team what they monitor and you will hear a familiar list: latency, errors, throughput, uptime. Those signals still matter for AI, but they no longer answer the most important question. As Krishna Sai, CTO of SolarWinds, puts it: "Traditional observability tells you a system is running. AI observability tells you whether it's still reasoning soundly. Those are completely different questions" ...

AI observability serves several roles across an organization. AI engineers use it to build, evaluate, and improve AI systems. SREs use it to operate those systems reliably and efficiently in production, including managing their cost. Leaders need it to connect AI behavior and operational performance to business outcomes. The common need is a clear understanding of whether AI systems are delivering the intended result ...

Part 4 outlines the challenges enterprise face when adopting AI observability. The main challenge is that AI complexity compounds at every stage of maturity. LLM-based applications introduce new behavior, dependencies, and quality considerations, while Agentic AI adds further failure modes as systems make decisions and take actions ...

Part 3 reports on the current state of AI observability. Most experts agree: enterprises are moving faster with AI than they are with the operational discipline needed to run it safely ...

When discussing AI Observability, one of the first questions that arises: Is this just an extension of traditional observability or is this a whole new type of tool? Part 2 compares traditional observability with AI observability ...

In this extensive 14-part series that will post over the next three weeks, experts across observability and AI will provide insight and opinions on the need for AI Observability, the challenges, the benefits, the current state, and predictions for the future ...

Amid rising complexity, organizations are moving quickly toward agent-driven operations: AgenticOps, according to The Impact of Agentic AI on Network Operations, a new report from Cisco ...

AI, automation, and enterprise software give organizations an extraordinary amount of leverage. Work that once required hours can happen in minutes. Information buried across systems can be analyzed almost instantly. Complex workflows can be coordinated across teams with far less manual intervention. That leverage creates a temptation to start with the technology ...

Everyone wants to talk about models. Compute clusters. Benchmark scores that climb a fraction of a point and make headlines anyway. It's an easy conversation to have because it's measurable. But it's also increasingly beside the point ...