BYOC: What if Your O11y Tool Just…Worked?

You know that moment during an outage where you're jumping between three different dashboards, grepping logs in one tool, checking traces in another, and wondering why your monitoring bill spiked 40% last month for the privilege of being this confused? Or when you realize a service quietly stopped reporting traces because someone updated an OTel SDK and the instrumentation broke? 

That's the SaaS observability experience that a lot of Developers are all too familiar with. They’re stitching together data from different sources, babysitting SDK versions, hoping your instrumentation didn't silently regress, and paying more every time your systems get busy enough to actually need monitoring.

✨BYOC — Bring Your Own Cloud — is the architectural answer to these frustrations. It means the observability platform runs inside your cloud, not someone else's. Your data stays put. And that's exactly what groundcover was built to do ✨

groundcover, a cloud-native observability platform built from the ground up on eBPF and a BYOC architecture, was built to make observability work the way it should. You get complete visibility with zero-code instrumentation and your data never leaves your cloud.

In this post, we'll break down:

• What eBPF is and how it naturally produces a BYOC architecture
• What other vendors actually mean when they say "BYOC" — and what they leave out
• The observability-security-agent contradiction and why most BYOC breaks down when AI enters the picture
• How BYOC and data completeness give groundcover's Agent Mode the context to do real root cause analysis

eBPF Sensors: laying the groundwork for true BYOC 

eBPF is a Linux kernel technology that lets you observe everything happening on a system without touching application code. Unlike traditional SDK-based instrumentation that runs in user space alongside your apps, eBPF sensors run sandboxed programs inside the kernel itself. 

groundcover deploys an eBPF sensor onto your K8s cluster to capture telemetry straight from your nodes. With one installation you immediately get traces, logs, and metrics across your entire stack including your infrastructure, K8s, applications, RUM, and AI workloads.

✨BYOC is a direct and natural consequence of how groundcover collects data with eBPF ✨

Because the data is captured at the kernel level inside your environment, there's no architectural reason for it to leave. All your telemetry is stored and processed in the data plane which runs in an isolated account in your cloud. Meanwhile groundcover's control plane manages the platform, but is walled off from your production workloads and telemetry data at the network level. You get a fully managed SaaS experience, but your data stays fully under your control for true BYOC. 

That's what makes groundcover's BYOC fundamentally different from what the rest of the industry is selling. To better understand why groundcover has true BYOC and why it’s different, let’s contrast against what BYOC actually means when other vendors use the term.

What Other Vendors Actually Mean When They Say "BYOC"

✨For other vendors, BYOC is just relocated SaaS without data sovereignty ✨

BYOC is becoming a buzzword. Other vendors may limit customers to a single cloud provider, locked behind premium tiers, or only cover a slice of the stack. Maybe the developers can send logs but not traces without an upsell. Other vendors require you to fully manage the observability solution yourself. They treat BYOC as an add-on or a partial solution to the cost of SaaS.

Tom Wilkie, Grafana's CTO was refreshingly honest about this interview with The Register. Their BYOC “isn't designed to solve privacy concerns”. It's a cost optimization play. His advice to buyers? Think about it “'the same way you think about SaaS, just with a different economic model.” That honesty is rare, and it clarifies something important: there's a difference between relocating your SaaS to your own account and building an architecture where data separation is the foundation. Let’s break down the differences between typical “BYOC” and groundcovers true BYOC:

Think about a healthcare platform processing patient data across microservices. You chose BYOC to keep PHI in your environment. But your vendor's BYOC only covers logs. Traces, metrics, APM, and RUM still run through their SaaS. Not all of the data is contained.

To further understand why groundcover is offering true BYOC let’s look at how groundcover’s implementing observability agents and how it’s different. 

The Observability-Security-Agent Contradiction

Imagine a fintech company that processes fraud detection. That telemetry contains transaction details, account patterns, behavioral signals: data that can't leave your environment. Data sovereignty is why teams choose BYOC. 

But then something breaks in production and you need to debug it. Your vendor's AI agent needs to send those traces to their cloud for analysis because the agent runs on the vendor's infrastructure. But this is a breach of your data data residency requirements, so now you can’t use that agent or give it access to the data it needs to be useful.

✨The contradiction is baked in: you chose BYOC to keep your data safe, and the AI tools your vendor is selling you only work if the data leaves✨

With groundcover, the data plane runs inside your VPC. Even groundcover’s Agent Mode runs on Amazon Bedrock inside your own AWS account, so you can use the Agent with trust.

✨And here's where it gets interesting for agentic workflows and eBPF ✨

There's an even deeper layer of trust here. As AI agents take on more autonomy in production, a new vulnerability arises: can you even trust your telemetry? If coding agents can inject traces or manipulate logs at the application level, your observability is only as honest as the agent. Because groundcover captures data at the kernel level with eBPF, below the application layer, it sees what your agent is actually doing, not what the application reports is happening.

With groundcover you can trust that your data is complete, safe, secure, and reliable. Now let’s dive into what you can do with it.

Root Cause Analysis: Groundcover Agent Mode with BYOC 

You know that feeling when you're staring at a dashboard at 2am, you know something is broken, and you just want to ask: what happened? Not "let me check this dashboard, then cross-reference that log, then open a trace in a different tool." Just — what happened?

✨Data completeness is the context Agents need to actually be useful ✨

Because groundcover is BYOC, you didn't exclude your agentic workflows for security reasons. Because pricing is per node, you didn't drop traces to save money. Because eBPF captures at the kernel level, you didn't miss a service that forgot to configure an SDK. Everything is there. 

So when you ask groundcover's Agent Mode, “Why did my cost spike?”, it can give you an answer informed by a complete data context. For example, it shows you that your LLM token costs surged because the underlying K8s node was memory-starved, which throttled your service, which drove up retries. You can't get that from a tool that only has half of the data picture because of security or price limitations.

In fact, try asking Agent Mode any of the following:

• "Set up an alert if any K8s node drops below 15% available memory"
• "Show me every database running in production and who's talking to it"
• "File a Linear ticket for this root cause with a suggested fix"

Final Thoughts

BYOC isn't a deployment option. It's a design decision that shapes everything downstream including what data you keep, whether you can trust it, and what your agents can actually do with it. 

Most vendors treat BYOC as a pricing lever. groundcover treats it as the foundation. eBPF captures the data inside your environment. The architecture keeps it there. And because nothing is excluded for cost or compliance reasons, you end up with something most observability platforms can't offer: the full picture.

You’re not left with having to make tradeoffs between visibility and security. It feels good to use an observability platform that works the way you always assumed it should.

I hope these resources help you to get started with groundcover:

• Getting Started with groundcover–EKS observability with zero-code instrumentation
• If you don’t want to spin up any infra you can get a feel for groundcover by checking out the groundcover Playground.
• Slack