Private Inference API
Accelerated Data

Your data lake should accelerate AI, not slow it down

Nebul Infinia is a high-performance, AI-native data lake built on NVMe architecture. Designed to remove data bottlenecks for AI, Spark, and real-time analytics at any scale.

01

Storage bottlenecks

Data access becomes the limiting factor as GPUs and Spark clusters wait on storage, leaving expensive compute resources underutilized.

02

Metadata scalability

Listing, searching, and accessing data slows down at scale, making metadata operations unpredictable and increasingly inefficient.

03

Degrading performance

As data volumes grow, traditional architectures introduce hotspots, cold tiers, and tuning complexity that collapse overall performance.

04

Uncontrolled costs

Cloud economics break down as egress fees, data movement, and opaque pricing models make AI analytics expensive and hard to control.

The problem isn’t how much data you store. It’s feeding AI workloads at scale.

Rethinking how data lakes serve AI workloads

Data lakes were never designed to feed AI workloads. Traditional data lakes were built for cheap storage, not for feeding AI workloads or modern Spark workloads at scale.

Nebul Infinia changes how data is accessed and delivered by treating the data lake as an active AI data platform, not a passive storage layer. At the core of this approach is an NVMe-first, metadata-driven architecture designed to remove data bottlenecks at any scale.

Performance
without compromise

Nebul Infinia delivers consistent, low-latency access using NVMe-based architecture. GPUs and analytics engines stay fully utilized, even under sustained and high-concurrency workloads.

Built for
the AI era

Designed for continuous, parallel data access required by modern AI workloads. Supports training, inference, RAG, and real-time analytics at scale, across diverse workload patterns.

Native integration with Spark and beyond

Nebul Infinia integrates directly with Spark and modern analytics and AI frameworks, ensuring high-throughput data access across the full AI pipeline without proprietary APIs or lock-in.

Predictable
performance

Consistent low-latency and high-throughput data access under sustained AI and analytics workloads, without tuning, tiering, or unexpected performance degradation.

GPU-fed
data

High parallel throughput keeps GPUs and accelerated analytics engines continuously supplied with data, reducing idle time across training, inference, and Spark workloads.

Seamless
scaling

Scale from terabytes to exabytes without architectural changes, data rebalancing, or performance cliffs as data volumes grow over time.

AI-native
workloads

Designed for modern AI and analytics pipelines including Spark, Accelerated Spark, RAG, and training and inference, rather than retrofitted from archive storage.

Unified
access

Support multiple data access patterns and workloads through a single data platform, eliminating silos across analytics, AI, and operational systems.

Sovereign
and open

Deploy in Nebul’s sovereign NeoCloud or on-premises using open interfaces and transparent economics, without cloud lock-in or hidden data movement costs.

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Turn your data lake
into an AI platform

Talk to an expert