How a Fortune 10 Company Gave Thousands of Engineers Out-of-the-Box Observability for Their AI/ML Workloads

The Stakes

A Fortune 10 company set out to build an internal platform-as-a-service for AI workloads - the paved road on which thousands of engineers across the company would build, deploy, and iterate on LLM-powered applications. The platform team behind it was small, single-digit headcount, and responsible for making that road safe, fast, and observable by default.

Observability was not a follow-up phase. For a workload class as opaque and fast-moving as LLM applications, visibility is required for the development loop. But the same company that needed that visibility also carried some of the strictest data-governance and access-control requirements of any enterprise on earth. Nothing could leave their premises, and no engineer could see a byte they weren't entitled to see.

The team was therefore solving for two things the observability market usually treats as opposites: total, effortless depth for every engineer building on the platform, and total control over where that data lives and who can reach it.

Highlights

• Out-of-the-box observability for every workload deployed on the platform - no instrumentation, no code changes, no per-team onboarding
• Deep LLM payload visibility by default: full prompts, completions, tool calls, and agent trajectories alongside classic ML workloads benchmarking
• Tens of thousands of workloads consolidated into a single self-hosted dataplane
• Per-engineer, granular RBAC fully in sync with the company's custom in-house identity provider
• The only platform in the evaluation - against Datadog and Splunk - that could meet the RBAC and sovereignty bar
• AI agents use the platform daily, inheriting the same RBAC boundaries as human users out of the box
• A 2–5 person team delivering a full PaaS to thousands of engineers, with zero in-house maintenance liability for the observability stack
• Fully on-prem: no third-party SaaS anywhere in the data path

The Challenge

The platform team's mandate was to abstract away infrastructure so that engineers across dozens of business units could focus on their models and applications. Deploy to the platform, and networking, scaling, security, and compliance are handled. Observability had to work the same way - present the moment a workload started running, with nothing asked of the engineer who deployed it.

That ruled out the instrumentation-first model. At the scale of thousands of engineers and tens of thousands of workloads, any approach that requires application teams to add SDKs, wrap clients, or annotate code becomes a coverage problem that never closes. Some teams instrument well, most instrument partially, and the platform team ends up chasing gaps rather than building the platform.

LLM workloads made the gap sharper. Traditional signals - request rates, latency, error codes, resource utilization - say almost nothing about whether an AI application is behaving correctly. The interesting failure modes live in the payloads: a prompt that drifted, a tool call that returned the wrong shape, a chain that looped, a context window quietly truncating. Engineers debugging these systems were reconstructing behavior from application logs they had written themselves, which meant they could only see what they had already thought to look for.

Then came the governance requirements, which is where most of the market fell away. As a Fortune 10 company, the team had to satisfy two constraints simultaneously:

• Sovereignty. Observability data - including full LLM payloads, some of the most sensitive data the company generates - could not transit or reside in a third-party SaaS environment. It had to stay entirely within the infrastructure the company controls.
• Access granularity. Tens of thousands of workloads belonging to hundreds of teams would land in one platform. Every engineer needed access to exactly their own scope and nothing else, and that mapping had to derive automatically from the company's custom, internally built identity provider - not from a parallel permissions model a small team would have to hand-maintain.

Both Datadog and Splunk were on the table. Neither could deliver a single, centralized dataplane that stayed on-prem while enforcing per-employee access at the required granularity against a custom IdP. A DIY open-source stack could theoretically satisfy sovereignty, but it would have handed a small platform team permanent ownership of an observability product - exactly the liability they were building a PaaS to eliminate.

The Implementation

groundcover was adopted as the observability backbone of the platform initiative. Because the sensor is eBPF-based and deployed at the infrastructure layer, coverage came from the platform itself rather than from application teams. Any workload deployed to the platform - by any team, in any language, with or without knowing groundcover existed - became observable the moment it started running.

The same mechanism produced the LLM visibility the team could not get elsewhere. With no SDK and no code change, engineers got full payload-level visibility into their AI workloads: the actual prompts sent, the completions returned, the tool calls invoked and their results, and the full shape of multi-step agent behavior. Engineers were seeing their own production prompts and tool calls without having written a line of instrumentation - which changed what the organization considered debuggable in the first place. Alongside that, groundcover's native OpenTelemetry support meant teams that did emit their own telemetry were folded into the same environment rather than being routed to a separate system.

Architecturally, groundcover's bring-your-own-cloud model was the unlock for the governance requirements. The entire dataplane runs inside the company's own environment - every log line, trace, metric, and LLM payload stays on their infrastructure, with no third-party SaaS in the loop. That let the team centralize tens of thousands of workloads into a single dataplane instead of fragmenting observability across business units to satisfy data-handling rules.

On top of that single dataplane, groundcover's RBAC was wired directly into the company's custom identity provider. Access is derived from the existing identity graph, so an engineer's observability scope follows their organizational scope automatically. For a platform team of a handful of people, this was the difference between a tractable operation and an impossible one: they administer a policy model, not thousands of individual grants.

The Delta & the Proof

Observability that arrives with the workload

The delta: before, visibility into a new AI workload depended on what its team had instrumented. After, every workload deployed to the platform is observable by default - infrastructure, application, network, and LLM behavior - with zero integration work from the engineers deploying it.

The proof: the platform team shipped their next-generation ML platform on schedule with observability practices baked in for thousands of engineers, and did not have to run an onboarding or instrumentation program to get there. Coverage is a property of the platform, not a per-team project.

AI workloads that evolve as fast as the teams building them

The delta: engineers iterating on prompts, tools, and agent architectures now get immediate, payload-level feedback on what their application actually did in production - not an inference drawn from logs they wrote before they knew what would go wrong. Full prompts, tool calls, and execution paths are visible for every change.

The proof: teams across the company can evolve their AI workloads at the pace they want to move, with observability keeping up with the rate of application change rather than lagging behind it. The platform team stopped being the bottleneck between an engineer and an answer about their own workload.

Fortune 10 access control, without a Fortune 10 administrative burden

The delta: instead of choosing between centralization and control, the company got both. One dataplane holds tens of thousands of workloads; granular per-employee permissions, synced from their custom IdP, decide what each of thousands of engineers can see inside it.

The proof: groundcover was the only platform evaluated - against Datadog and Splunk - that satisfied both the sovereignty and the RBAC requirements. A team of 2–5 engineers manages access for thousands of colleagues as a routine operation, and the entire stack runs on-prem with no third-party SaaS in the data path.

Agents as first-class users of the platform

The delta: the platform isn't only a place engineers look at LLM observability - it's a system that AI agents query themselves, daily, as part of how work gets done. Because groundcover's RBAC applies to agent access out of the box, an agent operates inside the same permission boundaries as the human it acts for.

The proof: agentic use of the observability platform was unblocked without a bespoke security review or a parallel access model. Enterprise-grade RBAC extending natively to agents is what made daily agentic observability viable at a Fortune 10 company rather than a proposal waiting on governance.

Why groundcover

The platform team needed three things that the observability market has historically forced customers to trade off against each other: coverage without instrumentation, data that never leaves their premises, and access control granular enough for a Fortune 10 identity model. groundcover's architecture - an eBPF sensor for zero-instrumentation depth, native OpenTelemetry ingestion, and a bring-your-own-cloud dataplane with IdP-synced RBAC - delivered all three at once, which is why it survived an evaluation that Datadog and Splunk did not.

The outcome is a platform where bleeding-edge observability practice and Fortune 10 governance are not in tension. eBPF-level depth, native OTel, full LLM payload visibility, and AI-driven investigation all operate inside the company's own infrastructure, under its own identity system, with no external SaaS dependency. That combination is what let a small team hand thousands of engineers a fully observable paved road for AI workloads - and take on no in-house maintenance liability for the stack underneath it.

It also moved the company early into a shift the rest of the industry is still approaching: observability consumed by agents as routinely as by people, with enterprise access boundaries intact. As the AI platform grows, observability grows with it automatically - the next thousand workloads arrive already visible.