The Best Data Platform for Generative AI

AIStor is the only object storage platform built to match the scale, speed, and efficiency required by generative AI, from LLM model training and fine-tuning to inference and retrieval augmented generation (RAG).

Trusted by leading organizations building next-gen AI at scale, across the following use cases:
Data Ingestion
Data Preprocessing
Large Language Model (LLM) Training
LLM Fine-Tuning
Retrieval Augmented Generation (RAG)
Model Serving & Inference
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Data Ingestion

Data ingestion is the first roadblock

Every AI initiative begins with data, but the ingestion layer is often the first roadblock. Organizations must be able to capture any data type, at any scale, and deliver it reliably into an S3-compatible object store. Without this foundation, downstream preprocessing and model training stall before they even begin.

The challenge is that most data pipelines are not built for AI-scale. They fail to handle the massive, heterogeneous data streams that frontier models demand. Bottlenecks in ingestion translate directly into delays in experimentation, inefficiencies in training, and higher infrastructure costs.

Without an ingestion architecture that is both scalable and S3-compatible, enterprises risk:
  • Slow or inconsistent data flow into training pipelines.
  • Lock-in with proprietary storage systems that limit flexibility.
  • Missed opportunities to leverage the broader AI ecosystem.
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Scale, compatibility, and interoperability

MinIO AIStor enables organizations to ingest any data type at any scale, ensuring pipelines can keep up with AI-scale workloads. By supporting multiple protocols and storing everything in a fully S3-compatible object store it guarantees interoperability across the AI ecosystem without risk of lock-in. With high-throughput, parallelized performance, AIStor eliminates bottlenecks and delivers data efficiently into training pipelines.
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Universal, scalable ingestion

‍AIStor can take in any data type—structured, unstructured, or semi-structured—at any scale, ensuring that data pipelines don’t collapse under AI-scale workloads.
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Multiple protocols

S3 is the simplest way to send data to AIStor, but other messaging options such as SFTP and Kafka, are also supported.
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High-performance data flow

Built for extreme throughput and parallelism, AIStor eliminates ingestion bottlenecks, delivering data quickly and efficiently into training pipelines.

And for those looking for a hyperscale solution, pair AIStor with F5 BIG-IP:
With MinIO AIStor and F5, your AI Factory ingests more, scales faster, and runs smarter.
Source: https://www.f5.com/company/blog/accelerate-your-ai-deployments-with-f5-and-minio
Data Preprocessing

The preprocessing bottleneck in enterprise AI

For enterprises building with LLMs, success hinges on trust, relevance, and scale in preprocessing. Yet most organizations struggle to meet these demands. As internal data sources—from meeting assistants to collaboration tools—generate massive volumes of transcripts and documents, the scale problem quickly compounds. Without the ability to efficiently store, organize, and transform this data, enterprises risk falling behind.

Preprocessing is not optional “housekeeping.” It requires cleaning and normalizing documents, removing sensitive information, adding business context, and enforcing compliance. Without these steps, data pipelines produce noisy, insecure, or irrelevant training corpora—leading to weaker, riskier, and misaligned models.

In short, enterprises face a critical challenge: how to preprocess rapidly growing data streams at scale while maintaining trust and business relevance.
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Preprocessing at scale with MinIO AIStor

AIStor provides the exascale, high-throughput storage foundation required to manage data used for fine-tuning and retrieval augmented generation. It enables organizations to preprocess massive data streams, eliminate noise and duplication, balance across domains and languages, and enrich content with metadata. By powering efficient preprocessing at scale, AIStor enables enterprises to build curated, diverse, and trustworthy corpora—driving the quality, safety, and competitiveness of their cutting-edge models.
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Exascale, high-throughput storage

Keeps massive data pipelines flowing without bottlenecks, ensuring GPUs used for creating embeddings for a vector database stay utilized.

Native S3 compatibility

Delivers seamless interoperability with AI/ML frameworks and preprocessing tools, enabling scalable parsing, cleaning, and enrichment of data.
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Built-in resilience and security

Provides durability, encryption, and fine-grained access controls to safeguard sensitive data while maintaining compliance at scale.

Large Language Model (LLM) Training

The bottleneck of frontier model training

Training frontier models is the most resource-intensive stage in the AI development lifecycle. It requires GPUs to process massive datasets—too large for any single machine to store—within distributed environments designed for extreme performance.

In this environment, speed is the critical differentiator. Every delay in training slows experimentation, hyperparameter tuning, and architectural iteration, directly impacting the accuracy and competitiveness of resulting models. For hyperscalers, even minor inefficiencies can compound into major innovation bottlenecks.

Frontier training pushes infrastructure to breaking points:
  • ‍Data scale: Datasets exceed memory limits, demanding seamless distribution.
  • GPU utilization: Training requires continuous throughput to avoid costly idle cycles.
  • Capacity bottlenecks: Infrastructure constraints result in slower experiments which translates into poor model performance.
Unless these infrastructure challenges are solved, organizations training frontier models risk falling behind in the race to frontier AI.

How MinIO AIStor solves frontier training bottlenecks

AIStor is built for hyperscale training. A high-performance, distributed object store that scales linearly with your workloads, AIStor eliminates I/O choke points and keeps GPUs fed at exascale speed. Run it in a cluster, scale out seamlessly, and deliver the throughput and latency frontier model training demands. With AIStor, your GPUs stay busy, your experiments move faster, and your models stay ahead.
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Scales seamlessly for massive datasets

AIStor handles petabyte- to exabyte-scale datasets with high-performance object storage, ensuring data can be distributed across clusters without bottlenecks.
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Delivers sustained GPU throughput

Built for extreme parallelism, AIStor keeps GPUs continuously fed with training data, preventing idle cycles and maximizing infrastructure ROI.
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Optimized for speed and iteration

High-throughput, low-latency pipelines enable rapid experimentation with models, hyperparameters, and architectures—critical for staying competitive at the frontier.

Large Language Model (LLM) Fine-Tuning

Slow data holds back LLM fine-tuning

Fine-tuning LLMs requires distributing enormous datasets across GPU clusters — fast. But SAN/NAS storage systems with bolt-on S3 API access can’t keep up. The result? Underutilized GPUs, bloated training timelines, and higher infrastructure costs.
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How AIStor keeps GPUs fed

AIStor’s object-native architecture is so lean and fast that your network is the bottleneck, never your object store.  AIStor easily saturates even 400Gbps networks for large object reads, streaming training data as fast as your GPUs, and your network can consume it.
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High throughput

Linear client-facing read throughput of ~45GiB/s per AIStor node ensures every GPU gets the data it needs. At a mere 1.0 EiB scale and 484 nodes, that’s 21.8 TiB/s.
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Efficient checkpointing

Ultra-fast write throughput at 50-70% of reads enables frequent checkpointing without delay or impact on training workflows — even with large multi-gigabyte files.

Retrieval Augmented Generation (RAG)

RAG systems are starved for data

Retrieval-Augmented Generation (RAG) is only as good as the data you can feed it. But for most organizations, custom corpora are growing faster than their pipelines can keep up. Transcripts, notes, documents, and structured content are piling up — and the infrastructure can’t deliver it fast enough to the embedding engines or LLMs.

The result? Slow prompt responses, irrelevant completions, and missed opportunities to inject proprietary context and knowledge into your results.
How AIStor powers RAG workloads

AIStor fuels every phase of the RAG pipeline with fast, scalable, and efficient object storage from ingest to embedding to serving.

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Scalable corpus storage

Capture everything with limitless scale in a single namespace. Transcribed meetings, agent notes, documents, audio, and more.

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Ultra-fast semantic search

Enable your vector database to retrieve embeddings instantly via the industry’s highest-throughput, lowest-latency object read performance. 

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Real-time prompt generation

Stream high-relevance proprietary data and context directly into LLMs again via high-throughput, low-latency object read performance.

Model Serving & Inference

Extended inference context windows cripple even “modern” storage

The evolution of LLMs toward extended context windows—now exceeding 100,000 tokens—is constrained by even modern storage. Inference with long-context models depends on token streaming, which in turn requires sustained low-latency retrieval of objects. RAG compounds this pressure: frameworks such as LangChain and LlamaIndex issue thousands of fine-grained read requests during a single user session, querying embedding indexes that may contain tens of billions of vectors. At this scale, storage throughput and access latency become the limiting factors, starving GPUs, driving up costs, and reducing productivity.

How AIStor improves model inferencing and development

From the fastest KV cache offload to inference logging, AIStor ensures your models run at full throttle and helps you improve them over time. 

Any-length context windows

AIStor delivers consistent, low TTFB access keeping inference pipelines fully saturated, ensuring efficient GPU utilization and consistent application performance.

KV cache-ready architecture

Coming soon: native support for networked KV cache offload to maximize inference performance, especially for long and slow thinking approaches. 

Inference logging and observability

Capture and store every prompt and result without delay to enable rich observability and model iteration leading to increased accuracy.

Built for production AI

AIStor delivers unmatched scale and performance to handle every generative AI workload, all from the same object store.

Why AIStor?

AIStor outperforms legacy multi-protocol systems with their bolt-on S3 API gateways because it was built from the ground up for AI:
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Object-native, single-layer

Architecture enables linear performance per node, delivering 21.8TiB/s at 1.0 exabyte scale across 484 nodes.

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True exabyte scalability

With proven production capability not just marketing claims. A leading autonomous vehicle manufacturer is already running AIStor in 1088 node, single namespace production clusters.

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Hyperscale unit-economics

made possible by saturating even 400Gbps networks with the least amount of storage infrastructure.

Proven at scale

AIStor powers leading AI teams training massive models — including those in:
Large-scale computer vision
Foundation model development
Generative RAG systems in enterprise SaaS

Trusted by leaders building next-generation generative AI at scale.

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Ready to scale AI without bottlenecks?

Start with AIStor — the only object store that keeps up with your AI ambitions.
Contact sales
hello@min.io275 Shoreline Dr, Ste 100, Redwood City, CA 94065, United States