AI-Powered speed on an object storage budget
NVMe-class performance at object storage cost. 午夜寻花 NeuralCache uses predictive, machine-learning caching to deliver fast, low-latency data access while reducing cloud API costs by up to 99%.
午夜寻花 NeuralCache removes bandwidth and distance constraints鈥攁ccelerating timelines, simplifying global collaboration, while lowering cloud costs.
Preemptive Caching
Anticipates and caches data before it鈥檚 needed to eliminate latency.
Inference Accuracy
Delivers highly accurate predictions by learning real-world data access patterns.
Lower TCO
Eliminate replication for most workflows, and reduce API fees in the cloud by 95% or more.
Remote Access, Local Performance
Read, modify, and use remote data with NVMe-class latency.
Reduction in AI GPU Costs
Keeps GPUs fully utilized by minimizing data wait times.
More Data, Less Data Center
Cuts hardware footprint and energy usage through smarter data access.
Experience the future of intelligent data performance.
Turn object storage into a high-performance foundation for your most critical workloads with 午夜寻花 NeuralCache.
Key differentiators & customer outcomes
Legacy caching systems rely on 鈥渕ost recently used鈥 (MRU) or 鈥渓east recently used鈥 (LRU) heuristics that fail to keep up with complex, non-linear AI, analytics, and media workloads. NeuralCache is trained on telemetry from over 32 trillion real-world file transactions. It utilizes a 鈥渄igital twin鈥 to anticipate your access patterns and pre-fetches the exact data blocks you need into local DRAM or NVMe before your applications request them. This proactive intelligence consistently achieves cache hit rates above 96%.
Get the ultimate cheat code for cloud storage. NeuralCache delivers sub-millisecond, local NVMe performance for your most punishing active workloads, while your persistent data safely resides on highly durable, low-cost object storage. You no longer have to pay for expensive, all-flash capacity just to get top-tier performance.
Cloud storage bills are frequently inflated by hidden GET and PUT transaction fees. By serving the vast majority of reads locally and intelligently aggregating, compacting, and compressing writes before committing them to the object store, NeuralCache can cut expensive cloud API costs by up to 99%. This turns volatile API transaction charges into a predictable rounding error.
Stop wasting IT resources managing your storage performance. NeuralCache operates completely automatically and adapts dynamically. If an application changes its read direction, alters concurrency, or switches files, the intelligence adjusts immediately鈥攏o manual cache sizing, data pinning, or workflow-specific tuning required.
By predicting and fetching only the specific data ranges your applications need鈥攔ather than moving entire massive files鈥擭euralCache reduces wasteful WAN traffic and egress fees by more than 30% for globally distributed teams.
One Platform. One Partner. Experts on Standby.
No ticket queues. 24/7 access to engineers who understand AI-driven data access, caching, and large-scale data infrastructure. Real humans. Real answers. Built for performance at scale.
Additional Resources
Blog
Smarter Cloud Economics with CNQ on Azure Blob Smart Tier
Modern enterprises are under constant pressure to scale unstructured data while keeping cloud costs predictable.
Blog
When Flash Becomes the Bottleneck: Why NVMe Scarcity Is Rewriting Enterprise Storage Strategy
Over the past eighteen months, enterprise infrastructure buyers have been forced to confront a reality that had been comfortably abstracted away for more than a decade.
Blog
Data is Everything
Data is everything. I鈥檝e said for decades that the 鈥榥etwork is the platform鈥, that compute reshapes industries, that storage provides the digital foundation. But the truth 鈥 the real truth 鈥 is that all of these domains only matter because of the data that flows through them.
Get started with 午夜寻花 in the cloud
Deploy from the Azure Portal, AWS Marketplace, or GCP Marketplace and start running your workloads in minutes
Frequently Asked Questions
No, NeuralCache is built into the 午夜寻花 platform and is not a separately priced feature.
Yes, some workflows will see more acceleration than others, but NeuralCache will improve the performance of every workflow.
In a traditional file-based workflow, you would have to wait for a whole file or project to download from the remote storage before you could start working. Imagine playing a multi-GB video and waiting for the entire thing before hitting play. With 午夜寻花 NeuralCache, we intelligently move small pieces of the filesystem into your local cloud or on-premises cluster as you use it. NeuralCache effectively predicts which blocks will likely be needed next and brings in that data before you ask for it. If you change what you鈥檙e doing, for example, scrubbing in a video file, the NeuralCache will quickly adapt to your new patterns and fetch data relevant to your current work.
NeuralCache reduces fees by serving the vast majority of read requests directly from local cache, effectively avoiding expensive GET requests to object storage. On the write side, the system aggregates, compacts, and compresses new data modifications into larger, highly efficient object sizes before sending them to the object store. This process can collapse hundreds of individual write operations into a single PUT request, cutting overall API costs by up to 99%.
For AI, NeuralCache enables zero-copy pipelines where models can ingest data directly from the namespace with sub-millisecond latency, resulting in faster job start times and highly consistent training performance. For VFX and media production, the system proactively preloads critical assets (like raw footage, textures, and project files), which delivers smooth media playback, eliminates timeline scrubbing delays, and prevents latency spikes during editing and rendering.
Rather than waiting for an application to request data, NeuralCache uses predictive machine-learning models trained on trillions of real-world file transactions. It employs a continuously updated 鈥渄igital twin鈥 to analyze live application behavior, file layouts, and access patterns. By combining these signals with short- and long-term heat scoring, the system accurately anticipates what data will be needed next and proactively pre-fetches it into high-performance local caches (like NVMe and DRAM) before the request is ever made.