Artificial intelligence has become the foundation of modern business transformation, but for enterprises, the real challenge lies not in creating advanced models but in running them effectively. As organizations deploy large language models (LLMs) into production, they face steep costs, GPU shortages, and complex infrastructure demands. Impala AI, a Tel Aviv and New York-based company, has raised $11 million in seed funding to solve these challenges by reimagining how enterprises handle AI inference at scale.
The funding round, led by Viola Ventures and NFX, will allow Impala AI to expand its team and accelerate the rollout of its proprietary inference platform. Designed for enterprise environments, the system enables organizations to run LLMs efficiently, securely, and affordably, all within their own virtual private clouds (VPCs).
Why AI Inference Has Become the Enterprise Bottleneck
Most AI discussions focus on training, but the real cost comes from inference, the process of deploying and operating models in production. Each time a user interacts with an AI application, the model runs an inference cycle that consumes compute resources and incurs costs.
According to Canalys, global spending on AI inference is expected to exceed $106 billion by 2025 and reach $255 billion by 2030 (Canalys, 2024). This represents a major shift from training to operations, as inference becomes the dominant cost in enterprise AI.
A recent Dell Technologies report revealed that inefficient GPU allocation and underutilized hardware can inflate inference expenses by up to 40 percent. As enterprises scale their AI workloads, managing these inefficiencies becomes critical to profitability and sustainability.
This is the gap Impala AI is filling, helping organizations run AI workloads faster, cheaper, and more securely, without losing control of their infrastructure.
Redefining AI Infrastructure for Cost and Control
Impala AI’s platform offers a fully managed, multi-cloud, and multi-region inference solution that runs directly inside a customer’s VPC. This design gives enterprises full ownership of their data while maintaining the scalability of a cloud-native environment.
At its core is a custom inference engine capable of reducing the cost per token by up to 13 times compared to traditional platforms. This performance improvement is achieved by optimizing GPU scheduling, automating workload distribution, and reducing idle compute time.
As CEO Noam Salinger, a former executive at Granulate, explained, the company’s mission is to make inference seamless. Teams can focus on building AI products while Impala handles the complexity of scaling, provisioning, and optimizing resources behind the scenes.
The Growing Demand for Efficient AI Operations
Enterprises are increasingly realizing that operational efficiency is the true measure of AI maturity. Research from Intuition Labs, “LLM Inference Hardware: An Enterprise Guide to Key Players”, underscores how inference infrastructure has become a defining factor in enterprise AI competitiveness. Companies that can run AI workloads more efficiently gain a measurable edge in speed, cost, and performance.
Similarly, academic studies such as “From Words to Watts: Benchmarking the Energy Costs of Large Language Model Inference” have highlighted that inference, not training, represents the majority of AI’s energy consumption and long-term carbon footprint. This adds another layer of urgency for enterprises seeking sustainable ways to scale AI.
By optimizing inference, Impala AI is not only cutting operational costs but also contributing to more energy-efficient AI infrastructure, a growing priority for global corporations under sustainability mandates.
Governance and Security at the Core
As enterprises integrate AI into critical workflows, data security and compliance have become central concerns. A 2025 arXiv paper titled “Multi-Stage Prompt Inference Attacks on Enterprise LLM Systems” warns that poorly managed inference environments can expose sensitive data and create vulnerabilities in production systems.
Impala AI’s architecture mitigates these risks by enabling enterprises to run inference workloads within their own secure environments. The platform includes built-in auditing, access controls, and compliance features, ensuring full transparency and governance. This approach appeals particularly to industries such as finance, healthcare, and government, where strict data policies make most public AI services unviable.
A Foundation for the Inference Economy
As the market shifts from model innovation to operational deployment, inference infrastructure is emerging as the backbone of enterprise AI. Impala AI’s funding marks a significant milestone in this evolution, positioning the company to lead in what some experts now call the “inference economy.”
The combination of cost optimization, enterprise-grade control, and scalability positions Impala AI at the forefront of this next phase. Its platform bridges the gap between AI experimentation and real-world execution, enabling enterprises to scale confidently and sustainably.
In the coming years, the winners in AI will not only be those with the most powerful models but also those who can deploy them intelligently. With $11 million in fresh funding, Impala AI is building the infrastructure that will make that possible.