Why deploy observability for AI on your cloud, anyways?

Traditional observability has an architecture shortfall for agentic applications, and it’s going to affect companies when they’re least expecting it. groundcover just debuted AI Observability on the first extended Berkeley Packet Filter-powered sensor (eBPF) in a bring-your-own-cloud (BYOC) design, and our customers are preparing to meet a new wave of applications. This new addition to the platform marks an exciting time of innovation in the Observability space.

What did groundcover release with AI Observability?

groundcover is the leading observability platform that deploys directly to customers’ clouds, but is managed for the same experience as traditional SaaS. The release today gives all groundcover customers access to full visibility of their LLM and agentic workloads in the same private and SaaS-like, fully managed platform experience that they already love. There are no additional SKU’s groundcover customers have to pay for with this release.

With AI observability, groundcover customers see prompts, responses, tokens, and latency. On top of that, groundcover is able to unify costs of LLM usage across the different foundational model providers - who all calculate token costs slightly differently. 

With AI Observability on groundcover, customers can set their service level requirements, track the communication between agentic AI & LLM services, and evaluate the full context of information to capture hidden dangers like hallucinations and LLM drift.

Paired with Agent Mode on groundcover, dev and SRE teams are able to dramatically accelerate their remediation efforts and simplify investigations. AI unlocks incredible capabilities with usage patterns like:

• Intelligent, environment-aware prompt recommendations surface the most relevant investigation starting points and SRE teams use them as guide posts.
• Trigger-based agentic background workflows continuously monitor environment signals and autonomously initiate investigations, alerts, and remediation actions. Dev/eng teams deploy repeatable and scalable systems based on those signals rather than manually combing through issue logs like breadcrumbs.
• Cursor integrates with groundcover for AI-assisted code remediation, enabling engineers to move directly from a production issue detected in groundcover to an automated pull request in their repository.

What are the pitfalls of AI Observability?

Traditional SaaS observability falls short for agentic systems with privacy, economics, and context.

Increasingly, companies from enterprise to startup are deploying agentic systems as the keystone for everything from their customer-facing applications to their back office automation, and they are unknowingly creating a data privacy gap they’re not prepared for. Cyberhaven, a leading AI & Data Security company published a report in 2025 that nearly 35% of all data input into AI tools is considered sensitive. groundcover’s approach to AI observability is fundamentally different.

It’s never been harder for today’s dev and devOps teams to justify their observability budgets, and they’re having to make accommodations because of the economics of SaaS platform design. Teams are deploying observability to fewer critical environments or instrumenting less of their agentic infrastructure. Sampling becomes a requirement rather than a smart option.

It’s essential to sample telemetry data on your terms because that data is increasingly critical for the full context of Agentic systems ingrained in SRE teams. The economics of SaaS prevent teams from persisting it all, and because agentic workflows don’t fail in the way that every other deterministic application fails, SaaS observability falls short. 

When we say “inside your cloud”, why does it matter?

groundcover, the bring-your-own-cloud-driven (BYOC) observability platform, uses an innovative architecture to fill the gaps in legacy SaaS observability, and AI Observability paired with Agent Mode is the most significant part of the ecosystem to see its benefits. 

The BYOC-first design of the groundcover platform means that any trace, metric, log, prompt, or response data never leaves a customer’s cloud. All of the privacy concerns teams face when it comes to sharing their data with third-party vendors evaporates. 

Since customers don’t ship their data to someone else’s cloud, they also don’t pay for data ingestion at the destination. groundcover has changed the commercials of observability with that design. Since the platform doesn’t charge by data ingestion, and instead by nodes, customers can afford to persist up to 10x the amount of data they would otherwise have to downsample to their SaaS observability platforms. There’s no sampling unless it’s the right choice for your business. That also means there are no surprise observability bills with groundcover. 

By now, the tech world understands that agentic workflows are only as good as the data (often distilled into ‘context’) the agents have access to. Sampling was never supposed to be a requirement, as much as it was supposed to be an option. That’s a tradeoff groundcover customers aren’t forced to make. Agents enjoy full context, and with agent mode the data has never been easier to search.   

Concluding Thoughts + Where to learn more

groundcovers AI Observability expands critical capabilities, eradicates surprise overages, and supports growing teams by giving them the confidence to deploy Agentic applications in production environments. The privacy gaps, budget pressures, and context loss that come with traditional SaaS have proven to be more than abstract risks. If your team is shipping AI products, the infrastructure should live where your data does, and be easily accessible in the SaaS-like groundcover BYOC observability platform. 

This week groundcover is at Google Next (Booth 5301 at the Mandalay Bay Convention Center in Las Vegas), but we’re also hosting a webinar on May 19th for devs, their leads, and their teams to see AI Observability in action and ask questions live. Sign up here.