The center of gravity in Enterprise AI is shifting – from show-and-tell to ship-and-scale. The first generation of AI applications focused on real-time copilots and assistants. Now, as we move along the adoption curve, the bigger opportunity sits behind the scenes: large, bursty workloads like data curation pipelines, enrichment, personalization, and model refreshes. These use cases don’t need millisecond latency – they need scale, predictability, privacy, and control. That’s the infrastructure Impala.ai is building.

The Enterprise AI stack is opening up – enabling both control and flexibility

Enterprises are moving beyond closed, one-size-fits-all APIs. Open-source LLMs are maturing – from DeepSeek and Qwen who stood out in performance benchmark tests to OpenAI OSS open weights models – giving organizations freedom to tune, deploy, and govern models within their own environments. While closed-source models still excel in certain cases, open systems offer transparency, cost control, and flexibility – essential for scaling sustainably.

The AI Neo-stack is redefining infrastructure economics

A new ecosystem is rising around this transformation. Neoclouds like CoreWeave and Together AI are making specialized compute accessible at scale. Neo-inferencing platforms like Modal and Baseten help developers deploy faster across workloads.

Yet, enterprises still lack one critical layer: an inference platform that runs inside their own cloud, spans any model or hardware, and optimizes for performance and cost without operational complexity.

Impala is building that missing layer.
It’s a bring-your-own-cloud inference platform designed for high-throughput, non-real-time workloads – the kind that power core business processes. Impala automatically orchestrates GPU clusters, maximizes throughput, and reduces cost, all while maintaining full data control and observability.

Why Now: The market is hitting it’s AI-scale inflection point

Over the past year, we’ve all witnessed the rise of “Supernova” AI application companies – those surpassing $100M in revenue in less than two years. Despite the unprecedented growth, many of them are suffering from negative gross margins driven by escalating compute costs. As AI adoption climbs the maturity curve – from VC-backed startups to enterprises, these Supernovas offer an early glimpse into the challenges ahead: Efficiency and cost have become strategic barriers to scaling AI-driven business models.

Besides runaway infrastructure costs that limit accessibility, there is also a shortage of specialized talent to manage complex systems. Most platform engineering teams don’t want to rebuild infrastructure from scratch; they want a platform that simply works – one that aligns with data security policies, offers cost transparency, and minimizes exposure to token-based constraints.

The next wave: open, multi-model, and Enterprise-ready

We believe the next phase of Enterprise AI will look a lot like the early data platform revolution – open, multi-model and increasingly run in private clouds. Just as Databricks became the connective tissue of the data era, Impala is positioned to become the infrastructure layer that powers the AI-native enterprise. In many ways, Impala’s approach echoes what Databricks did for data infrastructure: agnostic, open, and deeply integrated across the stack. It eliminates vendor lock-in and makes scaling AI predictable.

Building Scalable AI Infrastructure Demands a Certain DNA

Solving challenges at the scale of AI infrastructure requires teams that can see around corners.
From our first meeting with Noam and Boaz, it was clear that the Impala.ai team had that rare mix of vision and depth. Their customer focus, product clarity, and technical excellence stood out immediately.
Noam combines strong product intuition with a deep understanding of enterprise needs – balancing technical insight with empathy for users. Boaz brings exceptional expertise in operating systems, vulnerability research, and AI performance optimization, turning complex, low-level engineering into meaningful efficiency gains.

This combination of clarity, adaptability, and focus reflects a mindset we often see in exceptional Israeli founders.

Shaped by environments that demand fast learning, iteration, and calm under pressure, they develop an instinct for when to adjust and when to double down. In a market where speed and precision determine who leads, this mindset is a decisive advantage.
It’s what gives us confidence that Impala.ai is not only building a strong product but also shaping the infrastructure layer that will define how AI operates at scale