SAN FRANCISCO--(BUSINESS WIRE)--Mar 24, 2026--
groundcover, the BYOC-driven observability platform for modern architectures, today announced the general availability of groundcover AI Mode, a native AI capability designed to help engineering teams investigate production incidents and analyze infrastructure behavior directly inside their own cloud environments. AI Mode runs natively within the customer’s own AWS infrastructure via Amazon Bedrock, ensuring that logs, traces, and production telemetry never leave the customer’s environment. By running the AI within the customer’s environment, teams can adopt AI-assisted troubleshooting without introducing new security, compliance, or data-governance risks. Customers pay Amazon Bedrock token costs directly with no groundcover markup and can set usage limits by user or team.
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The launch coincides with KubeCon Amsterdam and marks the first time a production-grade AI observability agent has been built on a Bring Your Own Cloud (BYOC) architecture with kernel-level eBPF telemetry as its primary data source.
“The question every engineering team is asking is: how do we get the benefits of AI without handing our production data to another third party?” said Shahar Azulay, CEO and Co-founder, groundcover. “We built the answer. The agent runs inside your infrastructure. Full stop.”
The Problem With Existing AI Observability Approaches
Every major observability platform has added AI capabilities in the past six months. In every case, the architecture is the same: your logs, traces, and metrics leave your environment, get processed on third-party infrastructure, and an answer comes back. For teams operating under compliance constraints, that model is not acceptable. Production data contains API keys, service credentials, traffic patterns and system vulnerabilities. Handing an external agent your API key and asking it to fetch your logs creates two actors who handle your most sensitive production data, with zero controls over what either does next.
groundcover’s answer is architectural. AI Mode deploys on Amazon Bedrock inside the customer’s own AWS account, provisioned automatically during self-service onboarding. AI Mode never calls home. All investigation and analysis happen inside the customer’s environment, and organizations maintain full control over their telemetry and AI usage. Token quotas can be set per user or per team, the same predictable model engineering teams already understand from tools like Cursor.
“Companies struggle to move their workloads to use AI because of compliance. We basically brought AI to their environment. This is insane. This is huge,” said Yechezkel Rabinovich, Co-founder, groundcover.
Why eBPF Changes What the Agent Can Answer
Most AI agents built on observability platforms are limited by what developers have manually instrumented. If a service was never set up with OpenTelemetry, the agent can’t see it. groundcover deploys an eBPF sensor at the kernel level, automatically capturing telemetry without requiring any developer instrumentation. Every log, trace, metric and event is enriched with a cross-signal identifier at ingest, allowing the agent to automatically connect data across signal types.
The practical difference: groundcover AI Mode can answer questions that are structurally impossible with instrumentation-dependent approaches.
These types of questions typically require engineers to manually correlate information across multiple dashboards and telemetry sources.
“Once you give an agent access to eBPF data, you can answer questions that are simply impossible with OTEL,” Rabinovich said. “Just try asking ‘how many databases do I have?’ with manual instrumentation.”
AI Woven Into the Investigation, Not Bolted On
Most observability platforms that have added AI have built it as a separate product. The user switches to it, asks a question, gets an answer, then switches back, ultimately losing the thread of their investigation each time they cross the boundary. groundcover built the opposite model.
AI Mode is accessible from any page in the product, context-aware of where the user is and what they’re looking at. Its output creates first-class groundcover assets, including dashboards, monitors, GCQL queries and OTTL pipelines, all of which live inside the same environment the user was already working in. Multiple AI Mode tabs allow parallel investigations. AI Mode works alongside Cursor and Claude Code as specialist tools when a root cause might be in the codebase.
“You have some companies that are looking at their AI agent as a separate product entirely,” said Orr Benjamin, VP Product Management, groundcover. “That’s the polar opposite of what we want to do. We want to blend the experiences so that traditional observability and AI meet, and asking AI Mode feels like an extension of the same experience.”
Already in Production
Early customers are already running the AI agent in production and the engineering team cited the ability to use session replay and AI Mode analysis without triggering a security review because the data never leaves their account.
A recent groundcover customer migrated approximately 200 dashboards from Elastic to groundcover during the same period. After completing the migration, they also declined a free year of Elastic, which was offered as an incentive to stay, and closed the contract entirely.
Availability
The groundcover AI Mode is generally available today. A self-serve free trial is available at groundcover.com, no sales conversation required. To learn more, visit the groundcover AI Mode webpage and review our blog post.
groundcover will showcase the new groundcover AI Mode at KubeCon + CloudNativeCon Europe in Amsterdam. Attendees are invited to schedule a meeting and visit Booth #501 to see a live demonstration of the technology.
About groundcover
groundcover is a cloud native observability platform powered by eBPF. It runs inside the customer’s cloud and provides complete visibility into applications, infrastructure, networks and AI systems without operational overhead. The platform offers unlimited data coverage at a fraction of the cost of legacy observability tools. Learn more at https://www.groundcover.com.
groundcover AI Mode is a native AI capability designed to help engineering teams investigate production incidents and analyze infrastructure behavior directly inside their own cloud environments.
