Virtana launched a division of the company dedicated to delivering modern observability and AI solutions for federal, state, and local government agencies.
Purpose-built for the public sector, the AI-powered Virtana Platform provides a unified view across applications, services, and infrastructure, correlating user impact, service dependencies, performance bottlenecks, and cost drivers in real time—whether in secure, on-premises facilities or multi-cloud environments.
The Virtana Platform provides:
- AI at Lower Risk & Cost – Optimized GPU utilization and proactive bottleneck prevention keep AI and data-intensive initiatives efficient and accountable, whether supporting public safety, education, or university research.
- Faster MTTR & Higher Service Resilience – Correlated telemetry and dependency maps reduce time to isolate issues and restore services, improving reliability for everything from citizen-facing portals to research systems and transportation networks.
- End-to-End Cost Optimization and Risk Mitigation – Holistic visibility into performance, capacity, and spend enables agencies to reallocate resources, prevent overruns, and reduce operational risk across programs and projects.
- Operational Transparency Across Environments – A consistent view across multi-cloud, on-premises, edge, and dark data centers removes silos and blind spots, helping agencies, schools, and aerospace organizations maintain compliance and operational continuity.
- Flexible, Future-Ready Platform – Modular observability across services, containers, infrastructure, and AI workloads allows organizations to adopt new capabilities incrementally, aligning modernization with evolving budgets, regulatory requirements, and strategic goals.
The public sector initiative will drive sector-specific solutions and services that support key public sector use cases, including:
Federal
- Civilian Agencies - Cross-program transparency and cost control; accelerate ATO renewals with evidence-rich telemetry and change tracking.
- Defense & Intelligence - Readiness for large-scale AI/ML and mission systems: GPU fleet health, edge observability, dependency mapping, and rapid fault isolation in disconnected ops.
- Space & Research - HPC/AI pipeline visibility from ingest to inference; capacity planning to meet launch windows and research deadlines.
State & Local
- Education – Optimize campus workloads, track cost and performance by department or grant, and protect sensitive data with scoped telemetry.
- Emergency Management - Surge-ready capacity planning, situational observability during incidents, and post-event forensics with retained logs/metrics.
- Smart Communities & Transportation - Real-time service health for IoT/SCADA, curb-to-core dependency mapping, and incident triage with automatic impact analysis.
Beyond these core capabilities, the Virtana Platform also delivers advanced Event Intelligence powered by high-fidelity data correlation across every layer of the stack. Unlike other tools that surface generic alerts or partial insights, Virtana provides the precision and context agencies need to understand true root cause and impact in real time. This differentiation is why federal, state, and local agencies rely on Virtana for decisions that demand accuracy, accountability, and resilience.
"The public sector is facing perhaps the greatest challenges in the endeavor to modernize systems and processes," said Paul Appleby, CEO of Virtana. "Virtana is already helping public sector agencies achieve their rigorous modernization goals and this has become a key pillar of our business. Unlike other providers that offer only surface-level alerts, Virtana delivers high-fidelity data and advanced Event Intelligence that provide the clarity agencies need to act with confidence. We are expanding our capabilities that enable the public sector to deliver secure AI solutions at scale, manage costs with capacity and governance insights, and ensure operational resilience."
The Latest
I've spent a lot of time in the channel, and one thing I keep coming back to is this: a partner program is only as good as what it looks like in the field. Many programs look great on paper, but when a partner is in front of a customer navigating a complex hybrid environment or trying to make the case for AI-powered observability, the gap between what a vendor promises and what it actually delivers becomes very clear, very fast ...
Enterprises today operate in a real-time environment where uninterrupted access to trusted data has become a baseline expectation for users, applications and automated systems. Traditional DataOps models, built on manual effort and human triage, cannot keep pace with this always active demand. AI agents are emerging as the operational backbone, ensuring consistent data availability, reinforcing trustworthiness and enabling a level of scale that manual processes cannot achieve ...
For decades, trust in the digital workplace rested on familiar signals. We trusted faces on video calls, voices on the phone, and emails that appeared to come from people we knew. These cues felt human and intuitive. They anchored how decisions were made, approvals were granted, and access was authorized. AI-powered deepfakes have quietly broken that model ...
Cloud migration was supposed to be a one-way door. For most enterprises, it turns out it isn't. Cloud data repatriation is a real and growing trend. A new survey ... finds that 89% of organizations plan to expand their on-premises infrastructure footprint over the next two years — and 75% have already moved at least some workloads back from public cloud in the past 24 months. The findings point to a broad rethinking of where data belongs ...
Over the past few years, large language models (LLMs) have revolutionized the software industry. Given their ability to excel at multi-step reasoning, LLMs have helped enterprises streamline workflows and adapt to the unknown. However, employing such models comes with sky-high costs, latency issues, and limited flexibility. In the realm of IT operations, it is generally wiser to employ smaller, domain-specific models instead ...
For years, DevOps teams operated under a simple assumption: collect enough telemetry, and you can find and fix any problem. That assumption is breaking down. Modern enterprises now operate across microservices, hybrid cloud environments, APIs, Kubernetes, and highly automated delivery pipelines. Releases happen continuously, dependencies shift constantly, and failures spread faster than teams can diagnose them ...
New Relic surveyed IT and engineering leaders from the media and entertainment (M&E) sector to understand what's working — and where challenges persist with their observability practices. The findings reveal how M&E organizations are navigating rising platform complexity, audience expectations, and AI-driven change. Below are five takeaways that stand out ...
Let me start with something I've seen play out more times than I can count. A team hits a wall with the cloud. Costs creep up, then spike. Performance starts to feel inconsistent. Someone in finance asks a simple question like "why did this double?" and nobody has a clean answer ... Maybe this isn't the right place for everything. That realization feels like a breakthrough, like you've identified the problem. In reality, you've just identified the starting line ...
In MEAN TIME TO INSIGHT Episode 24, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses network observability tool sprawl ...
In cloud-native systems, scaling is often as simple as moving a slider. For on-premise databases, the stakes are different. Over-provisioning hardware is expensive. Under-provisioning leads to performance bottlenecks that are difficult to fix once the equipment is in the rack ...