Virtana announced the grant of US Patent No. 12,348,389 B2 for full-stack optimization of AI-centric cloud applications.
The patented method builds a live application model, links performance targets to the required resources (including GPU-accelerated infrastructure), detects emerging issues, and autotunes the stack to maintain SLOs while computing real-time cost per operation, enabling right-sizing decisions that reduce spend and improve ROI for both AI and traditional workloads. This follows the recent announcement of another patent awarded for creating an orchestration system that dynamically manages AI analytics workloads.
The new patent describes a practical control loop for continuously monitoring cloud applications, building a full-stack application model, mapping performance needs to underlying resources, detecting issues as they emerge, and automatically adjusting the stack to meet service-level objectives (SLOs) while computing per-operation cost in real time.
Why this patent matters for customers:
- SLO adherence with fewer firefights: Proactive detection and targeted adjustments reduce variance and long-tail latency.
- Right-sizing without guesswork: Matches standard and AI-workload needs to resources in real time, eliminating over-provisioning and waste.
- Cost-to-serve transparency: Real-time per-operation cost analysis enables accurate showback/chargeback and better architectural choices across traditional and modern AI environments.
- Faster triage and safer changes: A model-driven approach isolates the tuning layer, reducing the risks posed by broad, manual interventions.
- AI, hybrid, and multi-cloud fit: Works across on-premises and cloud environments for consistent performance and cost governance.
This patent extends Virtana's optimization loop to AI workloads by linking model/service performance to the exact infrastructure required (compute, storage, network, GPUs) and then tuning the right layer at the right time. The same closed-loop records cost as resources are consumed, giving platform teams clear cost-to-serve for AI operations alongside traditional services, so teams can:
- Align LLM latency targets (p95/p99) and vector DB throughput to GPU/CPU, memory, and storage choices.
- Tune RAG and inference pipelines without broad, risky changes—adjusting the narrow layer that drives the bottleneck.
- Identify per-operation AI cost (e.g., per prompt, per embedding, per inference) for comparing models, routes, and deployment options on both performance and spend.
"With a full-stack model and SLO-driven control loop, our platform can adjust the right layer at the right time and capture the cost impact as it happens," said Paul Appleby, CEO and President of Virtana. "That combination drives both reliability and financial accountability at scale. For AI, that means predictable model behavior, lower MTTR, and better ROI on GPU fleets and supporting infrastructure."
The invention formalizes a full-stack optimization method that:
- Builds a live application model spanning services, dependencies, and infrastructure.
- Maps performance requirements to resources (compute, storage, network, GPUs, etc.) using that model.
- Detects performance problems early via continuous telemetry and policy thresholds.
- Dynamically adjusts specific layers (application, platform, or infrastructure) to maintain SLOs.
- Calculates real-time aggregate cost for a specified operation as resources are consumed.
"Cloud applications are dynamic systems, and AI makes them even more dynamic. This patent codifies a disciplined way for organizations to be performant and cost-aware by connecting what the app or AI workload needs to what the infrastructure delivers in real time," said Amitkumar Rathi, SVP of Product and Engineering at Virtana. "Customers get steadier SLOs and clearer economics without constant manual tuning."
The patented capability underpins optimization workflows in the Virtana Platform and is available as part of ongoing product updates. This patent complements another patent Virtana was recently awarded for priority-aware scheduling and backpressure mechanism that dynamically reorders and resubmits analytic tasks based on real-time resource availability [Virtana Patents Orchestration System for Dynamically Managing AI Analytics Workloads].
The Latest
Cloud outages are usually described as technical failures. When a service goes down, a dependency breaks, or a region has issues, the focus immediately shifts to infrastructure. But if you look closely at how these incidents actually unfold, the root cause is rarely the technology itself. It is almost always tied to decisions made earlier, during design, implementation, or day-to-day operations. The system behaves the way it was built. The real question is how it was built ...
77% of leaders say their teams need AI skills urgently. 64% say their organization plans to train current employees rather than hire new ones. So far, so reasonable. The part that surprised me is who's been put in charge: 34% of those leaders say IT and engineering own the AI skills mandate. Learning and Development or HR own it at 7% of organizations. That's roughly five-to-one in favor of the people who understand the tools, over the people whose actual job is teaching adults how to learn new ones ...
In the ever-evolving digital landscape, enterprises are increasingly focused on enhancing their observability stacks to gain deeper insights into their IT environments. Observability has become a cornerstone of modern IT operations, enabling organizations to monitor, diagnose, and optimize their systems with unprecedented precision. However, a critical piece of the puzzle often goes unnoticed in this transformation: IBM i ...
We just surveyed 300 frontend and mobile engineers across 16 countries, and the finding that keeps sticking with me isn't the one about AI. It's this: 74% of engineering teams rate themselves in the "middle" of the observability maturity scale. Not reactive, not strategic. Stuck in the middle. They have dashboards, they have tracing, they have alerts. And yet when something goes wrong, they still can't tell you why ...
In MEAN TIME TO INSIGHT Episode 25, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses AI's impact on the Wide Area Network (WAN) ...
Application performance monitoring (APM) dashboards are only as useful as what they are configured to measure. The default setup covers obvious failure modes such as downtime, error spikes, and latency breaches, but it does not cover everything. Some failures produce no alerts or anomalies. The dashboard stays green while users experience a broken product. Here are six signs that is happening ...
The race to deploy AI is largely over. Most enterprises have entered it. The question now is not whether artificial intelligence is running inside the organization. The question is whether anyone is genuinely responsible for what it does. That is not a technical question. It is a leadership one. And most organizations are not yet structured to answer it honestly ...
A new analysis of 250 real-world queries across common retail tasks, such as product pricing, availability, ratings, shipping and specifications, reveals systemic inefficiency at the heart of web-based AI agents. On average, 97.9% of the data retrieved by agents from live web pages is irrelevant to the query being answered. Specifically, the average page ingested ran nearly 9,000 characters, while the average answer was just 32 characters, resulting in a noise-to-signal ratio of 278:1. Price queries were the most extreme outlier, with noise rates approaching 99.5%. That's not a rounding error. That's a structural problem ...
The enterprises that will define the next decade are not the ones that deployed the most technology. They are the ones who understood what their technology was actually doing. That distinction is not a philosophical point. It is the central operational challenge facing every organization that has spent the last five years modernizing at speed ...
AI is becoming the operating system of the enterprise. It acts as an invisible coordination layer that understands intent, connects systems, and executes work across complex SaaS environments. Previously, employees had to click through multiple systems — CRM, ERP, support tools, collaboration platforms — to complete a single task. Now, instead of navigating each application manually, they can simply state what they need to accomplish ...