As a market leader in enterprise agentic automation and business orchestration, UiPath is helping to pioneer an industry shift toward agentic AI. With it, the company is deploying autonomous agents to actively reason, make decisions, and execute complex business processes across its disparate systems.
This transition from simple task automation to cognitive decision-making agents requires a massive surge in computational power and powerful infrastructure that’s reliable enough for the needs of the world's largest enterprises.
Being able to orchestrate hundreds of GPUs in perfect harmony can be what makes the difference between just running a research experiment and building a global AI platform. Such orchestration requires balancing massive training jobs with real-time inference, all without letting costs spiral or latency spike.
To do so, UiPath re-architected its infrastructure to support high-scale intelligent document processing** (IDP) using UiPath IXP and moved from isolated clusters to a shared Google Cloud GPU fleet, balancing A3 VM instances (NVIDIA H100 GPUs)** for training with G4 VM instances (NVIDIA RTX Pro 6000) for inference. This architecture lets UiPath solve its “spiky workload” problem and count on predictable costs and open-source patterns that the company’s engineering teams can use to replicate this architecture themselves.
"Realizing the full potential of enterprise agentic AI requires an infrastructure that matches our ambition. Google Cloud provides the scale and flexibility we need to train specialized models and deploy them globally. This partnership allows us to deliver high-precision intelligent document processing and autonomous agents that don't just chat, but actively drive business outcomes for our customers." – Raghu Malpani, Chief Technology Officer, UiPath
UiPath has run its full-stack automation platform on Google Cloud for years, but as its agentic AI initiatives expanded, it faced a series of new infrastructure challenges.
Core capabilities like IDP, computer vision, and LLM-powered reasoning require heavy-duty math, so UiPath’s engineering team utilizes LLAMA model grounding that allows its robots to "see" interfaces with human-like clarity. And with specialized document models built on the Qwen architecture, the team can extract valuable data from messy, real-world paperwork.
These models live on the UiPath cloud infrastructure, where cutting every possible millisecond of latency is essential. Moving from a "cool demo" to a reliable production tool without exploding costs meant the team had to rethink its underlying silicon.
In the past, when a team at UiPath needed to train a new model or run inference, it provisioned GPU nodes on demand and scaled up or down depending on whether the workloads were spiking or slowing.
This was a functional strategy when cloud capacity was cheap, abundant, and perfectly elastic. But as its AI ambitions grew, UiPath found this approach could no longer keep up with its operational complexity. It now faced three new challenges:
Spiky workloads**:** To ensure it had sufficient power for peak demand, UiPath often had to buy extra capacity that sat idle during quieter periods, wasting expensive headroom. The company needed intelligent, on-demand scaling that didn't require paying for silicon that wasn't crunching numbers.
Supply bottlenecks**:** For large-scale fine-tuning, the price-to-performance ratio on gold standard high-end A3 VM instances with 8-cluster H100s is unbeatable. But global demand for those chips has outstripped supply, making it nearly impossible to scale training efforts at the speed UiPath desired just by adding nodes.
Operational overhead**:** UiPath was also struggling with geographical inefficiency because stable inference demand still meant maintaining dedicated clusters in multiple regions to ensure low latency for international customers. Further, managing GPU infrastructure for both training and inference added inefficient layers of operational overhead.
With all of that in mind, UiPath decided to treat its GPUs as a shared strategic resource instead of a product-centric elastic infrastructure.
As a result, its engineering team designed a platform-level shared GPU fleet managed by its machine learning services (MLS) platform, which prioritizes work across teams and time windows while balancing demand across workflows. During the day, the fleet serves real-time inference and latency-sensitive workloads, and at night or during off-peak hours, it automatically switches to batch training and long-running jobs.
By coordinating workloads at the fleet level, MLS lets UiPath maximize utilization while reducing contention, all without relying on per-instance elasticity. It also enables the company to schedule capacity in advance, which improves predictability for both research and production use cases.
To support its growing scale, UiPath leveraged Google Cloud AI Hypercomputer, which offers a system-level approach integrating performance-optimized hardware, open software, and flexible consumption models into a unified environment. AI Hypercomputer also minimizes the friction between hardware and software layers, which allows engineering teams to focus on model performance rather than infrastructure management.
Once it settled on a shared fleet model, UiPath needed a cloud partner that could offer reliable GPU availability, competitive pricing, and burst capacity. That’s why it chose to expand its existing Google Cloud footprint with a highly specialized AI stack running on Google Kubernetes Engine .
Now, UiPath can take advantage of predictable capacity by leveraging** **Google Cloud’s Dynamic Workload Scheduler (DWS) to solve its supply bottleneck. The company knew Google Cloud could secure its GPU capacity consistently with notice windows measured in days. DWS allows the engineering team to schedule training runs in advance and secure capacity for short bursts, and it can now plan for capacity rather than having to react to scarcity. Today, UiPath runs all its training and most of its IDP model inference workloads on Google Cloud.
While UiPath uses A3 VM instances for heavy-duty training and fine-tuning, not all of its tasks require that level of power. That’s why it now deploys Google Cloud G4 VM instances as a net-new optimization for inference workloads. These instances offer a cost-effective balance of performance and price, which allows UiPath to run lighter inference tasks without occupying the high-performance clusters reserved for training. "The shift to a shared fleet on Google Cloud transformed our operational model. We moved from reactive provisioning to a predictable, high-performance engine that powers our most advanced IDP and agentic AI workloads. With tools like Dynamic Workload Scheduler and a mix of A3 and G4 instances, we have the flexibility to optimize for both cost and speed. This ensures our engineers spend their time innovating rather than waiting for compute." - Arthur Wilcke, director of AI infrastructure, UiPath
With consistent access to Google Cloud GPUs, UiPath can now bring advanced models into production. It can also schedule large training jobs without blocking production inference, letting it balance research experimentation with production reliability.
This allows UiPath to deliver advanced IDP capabilities that extract data from highly unstructured and variable documents with high accuracy. For example:
Omega Healthcare uses UiPath to automate over 100 million transactions with 99.5% accuracy, a 40% reduction in processing time, and 15,000 less hours of repetitive tasks per month.
Thermo Fisher Scientific uses UiPath to extract data from PDFs like invoices and purchase orders and is now able to process 53% of its invoices without human involvement, while cutting processing time by 70%.
UiPath’s most significant wins so far have been increased availability and reliability. As its workloads continue to transition and it decommissions its legacy GPU resources, the company expects to see additional cost improvements.
For engineering teams looking to build similar platforms, some key takeaways include: Decouple capacity**:** Instead of tying hardware to specific products, pool resources to smooth out usage spikes.
Schedule, don't react**:** Using tools like DWS to book compute in advance guarantees availability and stabilizes costs.
Right-size the silicon**:** Use A3 VM instances for training, but choose efficient options like G4 VM instances for inference.
After its recent infrastructure evolution, UiPath is still refining its MLS platform to support the next evolution of AI innovation. To replicate this success in your own organization, use the resources below:
Build it**:** explore engineering patterns on [GitHub](https://github.com/GoogleCloudPlatform).
Optimize it**:** Get started with [Google Cloud G4 VM instances](https://cloud.google.com/compute/docs/gpus#g4-gpus).
Learn more about [UiPath - IXP](https://www.uipath.com/assets/downloads/ixp-ebook)