In the race to scale artificial intelligence across industries, enterprises are discovering that their biggest challenge is not building smarter models but deploying them efficiently and securely. As large language models (LLMs) evolve into mission-critical business tools, inference, the process of running AI models in production, has become the most expensive and complex part of enterprise AI operations.
This is where Impala AI, a Tel Aviv and New York-based startup, is making its mark. Backed by Viola Ventures and NFX, the company has raised $11 million in seed funding to create a platform that redefines how enterprises manage AI workloads. By focusing on inference rather than training, Impala AI is solving the most pressing problem in modern AI: how to run large models at scale, within secure environments, and without the massive cost burden.
The Shift from Training to Inference
For years, AI development revolved around model training, teaching algorithms to understand and generate data. But according to a 2024 Canalys analysis, inference is now responsible for the majority of enterprise AI spending, projected to reach $106 billion by 2025 and $255 billion by 2030 (Canalys, 2024). This is because inference represents a recurring operational cost that scales with every customer interaction, prompt, and data process.
A recent study from Dell Technologies and Enterprise Strategy Group revealed that running generative AI on-premises often leads to cost overruns due to underutilized hardware, poor scaling, and inefficient GPU allocation. These inefficiencies are now front and center for AI leaders who must balance performance with financial sustainability.
Impala AI’s model is purpose-built for this new reality. It allows enterprises to run inference directly inside their own virtual private clouds (VPCs), giving them full control over data, cost, and performance. This approach combines the scalability of cloud systems with the compliance and governance of on-premise deployment.
Building Infrastructure for Real-World Scale
At the heart of Impala AI’s innovation is its proprietary inference engine, designed to deliver up to 13 times lower cost per token compared to traditional inference platforms. This optimization is achieved through advanced scheduling, GPU orchestration, and token-level efficiency.
Unlike generic AI hosting solutions, Impala’s system is multi-cloud and multi-region, meaning enterprises can deploy workloads across providers without vendor lock-in. The company’s platform also eliminates rate limits and capacity bottlenecks, issues that frequently affect businesses relying on shared infrastructure.
As CEO Noam Salinger, a former Granulate executive, explained in the company’s announcement, Impala AI’s goal is to make inference “invisible,” a seamless process where enterprises can run large models without managing clusters or hardware manually.
Governance and Security as Competitive Advantages
In an era of increasing data scrutiny, governance and security are no longer optional. According to a 2025 arXiv study titled “Multi-Stage Prompt Inference Attacks on Enterprise LLM Systems,” unmonitored inference endpoints can lead to data exposure and model manipulation.
Impala AI addresses this by embedding data control and audit capabilities directly into its architecture. Enterprises retain ownership of their data, which never leaves their secured environment, while still benefiting from a serverless experience. This feature is especially relevant for industries such as finance, healthcare, and government, where compliance with privacy standards like GDPR and HIPAA is critical.
The Broader Context: Inference as Infrastructure
Research from Intuition Labs in “LLM Inference Hardware: An Enterprise Guide to Key Players” highlights how inference infrastructure is becoming a strategic differentiator for enterprises adopting AI. Hardware and software stacks optimized for inference are unlocking new levels of efficiency, making it possible for companies to scale AI without overwhelming budgets.
Impala AI is aligning its platform to this emerging market trend by providing a foundation layer, the invisible infrastructure that makes AI run smoothly behind the scenes. Just as cloud computing transformed how enterprises handle data, inference optimization is now redefining how they handle intelligence.
The Next Phase of Enterprise AI
As organizations move from experimentation to full-scale deployment, AI infrastructure will determine who thrives and who stalls. The companies that can manage inference efficiently will not only save money but also deliver faster, smarter, and more reliable AI-driven experiences.
Impala AI’s emergence marks a pivotal moment in this shift. Its combination of cost optimization, enterprise control, and flexible deployment sets a new benchmark for how AI should operate in the real world.
The future of AI is not just about building bigger models; it is about running them better.
