Alluxio

What Alluxio does

Alluxio is a venture-backed data orchestration and distributed caching platform company founded in 2015 at UC Berkeley AMPLab as part of the Berkeley Data Analytics Stack (BDAS). Headquartered in the San Francisco Bay Area with 51-100 employees, the company develops a memory-first tiered caching layer that sits between compute frameworks (PyTorch, TensorFlow, Ray, Spark, Presto, Trino) and persistent storage (Amazon S3, Google Cloud Storage, Azure Blob, HDFS), exposing data through S3-compatible, POSIX, and Python FSSpec interfaces. The platform supports deployments up to 1,500+ nodes and is reported to deliver sub-millisecond data access and up to 1 TB/s throughput in production environments.

The company operates two commercial product lines — Alluxio AI (targeting AI/ML training, inference, and feature-store workloads) and Alluxio DA (targeting analytics workloads on Presto, Trino, and Spark) — alongside an Apache 2.0 open-source edition that has attracted more than 1,000 contributors from over 300 institutions. Alluxio monetizes primarily through annual enterprise subscriptions (quote-based, with pricing not publicly disclosed), self-serve free trials for product-led adoption, and paid professional services. Its customer base of 100+ production users spans AI-native firms (Fireworks AI, Dyna Robotics), hyperscalers and large internet platforms (Meta, Salesforce, Uber, Baidu, Tencent, Comcast, Expedia, Bilibili, RedNote), financial services (Barclays, an undisclosed hedge fund, Blackout Power Trading), and other enterprises, with active co-marketing and integrations across AWS, Google Cloud, Azure, and Oracle Cloud marketplaces.

Name

Alluxio

Legal name

Alluxio

Website

https://alluxio.io

Company type

Private

Founded year

2015

Operating status

Operating

Headcount range

51–100 employees

Short description

Alluxio is a venture-backed data orchestration company that provides a distributed caching layer between compute (PyTorch, Spark, Trino) and object storage (S3, GCS, HDFS) to accelerate AI training, inference, and analytics workloads for 100+ enterprise customers.

Ownership category

akta.pro rank

Product category

Data Orchestration and Distributed Caching Software

NAICS

Software Publishers (5132), Computing Infrastructure Providers, Data Processing, Web Hosting, and Related Services (51821), Computer Systems Design and Related Services (5415)

SIC

Services-Prepackaged Software (7372), Services-Computer Programming, Data Processing, Etc. (7370)

akta.pro primary industry

Edge Storage & Distributed Caching (KV/Object, Edge Cache) (HDABAGAI)

akta.pro secondary industries

Database/Application Data Layer Modernization (DB migration, ORM, caching) (BPAEAFAL), AI Server Systems & HGX/Accelerator Platforms (HDAAAAAB)

Keywords

• Distributed caching software
• AI data orchestration
• Multi-cloud data layer
• GPU data acceleration
• Unified data namespace

Headquarters

HQ city

San Mateo

HQ country

United States

HQ region

North America

Offices1 record

Markets served

GTM type

B2B

Offering type

Software

Cost components

Personnel, Technology or R&D, Marketing or Sales, Infrastructure

Revenue model

1. Enterprise Licensing: Alluxio Enterprise Edition provides production-grade features including enterprise security, data replication, administrative interface, and enterprise-grade support. Subscription-based licensing for enterprise deployments.
2. Open Source: Alluxio Open Source Edition available under Apache License 2.0, with community support via Slack and GitHub. Enterprise support and professional services available as paid add-ons.
3. Professional Services: Professional services including deployment assistance, implementation support, and training for enterprise customers.

Pricing tiers

Go-to-market motion2 records

Distribution channels4 records

Marketing channels6 records

Core offering

Alluxio provides a distributed caching and data orchestration platform that sits between compute frameworks (PyTorch, TensorFlow, Spark, Ray, Presto, Trino) and persistent storage systems (S3, GCS, Azure Blob, HDFS, Ceph, MinIO). It caches hot data on local NVMe/SSD of each compute node, exposing it via S3-compatible API, POSIX, or Python FSSpec interfaces, to accelerate AI training, inference cold starts, checkpointing, and analytics queries. The product is offered as Alluxio AI (for AI/ML workloads) and Alluxio DA (for analytics workloads), with an open source Apache 2.0 edition and a commercial enterprise edition.

Product overview

Alluxio offers two core product lines: Alluxio AI for AI/ML workloads and Alluxio DA (Data Analytics) for enterprise data analytics. Alluxio AI is a distributed caching layer specifically designed for AI workloads, caching hot data on local NVMe/SSD between object storage and compute frameworks (PyTorch, TensorFlow, Ray, Spark). Alluxio DA provides similar caching capabilities for analytics workloads with native integration for Presto, Trino, and Spark query engines. Both products share the same underlying distributed caching technology, unified namespace abstraction, and multi-tier storage architecture. The company also offers open source and enterprise editions, with the enterprise edition adding security, replication, and professional support. Alluxio is deployed between storage and compute, supporting S3 API, POSIX, and Python SDK interfaces for framework connectivity.

Differentiator

Problem solved

Functional benefit

Brands

• Alluxio AI: Distributed caching layer for AI/ML workloads including model training, inference cold starts, and feature stores. Provides sub-millisecond data access and terabyte-per-second throughput for AI workloads.
• Alluxio DA

Products and services

• Alluxio AI Distributed caching layer for AI/ML workloads that sits between object storage (S3, GCS, Azure Blob, HDFS) and compute frameworks (PyTorch, TensorFlow, Spark, Ray, vLLM). Caches hot data on local NVMe/SSD of each compute node to deliver sub-millisecond data access and terabyte-per-second throughput for training, checkpointing, and inference cold start elimination.
• Alluxio DA (Data Analytics) Enterprise data platform that provides distributed caching for analytics workloads, enabling interactive big data analytics on any file or object store. Supports Presto, Trino, Spark, and other query engines with up to 10x faster query performance through intelligent data caching and unified data access across hybrid and multi-cloud environments.
• Alluxio Enterprise Edition Commercial edition of Alluxio providing enterprise security, data replication, administrative interface, enterprise-grade support, and professional services. Subscription-based licensing for production enterprise deployments.
• Alluxio Open Source Open source edition of Alluxio distributed under Apache License 2.0, providing the core distributed caching and data orchestration capabilities. Community-supported via Slack and GitHub, with over 1,000 contributors from more than 300 institutions.

Quantifiable outcome

• 1 TB/s throughput achieved (Fireworks AI)
• +18 more outcomes

Named customers22 records

Segments3 records

Ideal customer profiles4 records

Technology focussed Yes

API detail

Has API

Yes

API docs

API detail

Core technology

AI maturity

App detail

Integration17 records

AI capability4 records

Feature6 records

Partnerships

Ten partnerships are on record, tiered core and foundational.

• Cray (HPE)coreStrategic or Co-development PartnerCray Urika-GX analytics platform integration with Alluxio Enterprise Edition, providing memory-speed access to data across disparate storage systems for supercomputing environments. Joint solution enables high-frequency insights with Cray's HPC platform combined with Alluxio's virtual distributed storage.
• Google Cloud PlatformcoreTechnology or IntegrationAlluxio integrated with Google Cloud Storage, Dataproc, and GCP compute services. Enables hybrid cloud data access without data copies, burst processing from on-premise to GCP, and 2x faster query performance. Alluxio can be deployed on GCP to enable hybrid workloads for on-premise HDFS.
• Amazon Web Services (AWS)coreTechnology or IntegrationAlluxio provides AWS CloudFormation template for deployment on EC2, integration with EMR for Spark, PyTorch, Ray, Trino, Presto on S3. Enables multi-region data access with up to 90% cloud storage cost savings and ~50% S3 egress cost reduction.
• Presto (Trino Project)coreTechnology or IntegrationAlluxio provides distributed caching layer between Presto and data sources, reducing I/O latency and improving query performance up to 10x. Enables cross-region, multi-cloud, and hybrid-cloud data sharing with other compute engines.
• TrinocoreTechnology or IntegrationAlluxio enhances Trino SQL query engine capabilities with high-performance data access layer, application portability, unified data access, and cost reductions. Alluxio SDK cache enables cross-region, multi-cloud, and hybrid-cloud data sharing.
• Apache SparkcoreTechnology or IntegrationAlluxio provides data access acceleration for Spark workloads, enabling applications to run without code changes while accessing data at memory speed from remote storage systems.
• PyTorchcoreTechnology or IntegrationAlluxio integrates with PyTorch for AI/ML workloads, providing distributed caching for model training and serving with Python SDK and FSSpec support.
• TensorFlowcoreTechnology or IntegrationAlluxio integrates with TensorFlow for AI/ML workloads, enabling efficient data access for training and inference workloads.
• RaycoreTechnology or IntegrationAlluxio integrates with Ray distributed computing framework for AI/ML workloads, supporting scalable AI applications.
• UC Berkeley AMPLabfoundationalStrategic or Co-development PartnerAlluxio originated from UC Berkeley AMPLab as the data access layer of the Berkeley Data Analytics Stack (BDAS). Project foundation in academic research from one of the leading computer science institutions.

Scale indicators9 records

Recent moves6 records

Expansion highlights7 records

Direct peers

• Weka: Weka provides a high-performance, distributed file system purpose-built for AI/ML and HPC workloads, competing head-to-head with Alluxio for GPU-driven data acceleration customers. Both vendors target training and inference workloads on NVMe and integrate with PyTorch and cloud object stores.
• VAST Data: VAST Data offers a unified AI data platform combining storage, database, and compute-aware data services for deep learning and analytics. It competes with Alluxio's positioning as the data access layer for AI workloads, particularly for large enterprise and HPC customers.
• MinIO: MinIO is an open-source, S3-compatible object storage system widely deployed for AI/ML workloads and frequently co-located with Alluxio in customer stacks. Both target Kubernetes-native AI data infrastructure, and both rely on open-source community plus enterprise monetization models.
• Hammerspace: Hammerspace provides a global data orchestration and unified namespace layer across on-premises and cloud storage, overlapping directly with Alluxio's global namespace and tiered caching architecture. Both target AI/ML and analytics workloads requiring cross-silo data unification.
• DDN (DataDirect Networks): DDN provides high-performance storage systems purpose-built for AI and HPC, with deep penetration in large-scale GPU clusters. It competes with Alluxio in accelerating training and inference data pipelines, particularly in enterprise and research deployments.
• Lightbits Labs: Lightbits Labs delivers a high-performance, NVMe-over-TCP block storage solution for AI/ML and cloud-native workloads, competing for the same GPU data-access budget as Alluxio. Both vendors target low-latency data delivery to GPU servers.
• Cloudian: Cloudian provides S3-compatible object storage with tiering and hybrid cloud capabilities frequently used in AI/ML pipelines. It competes with Alluxio's S3-compatible, multi-cloud storage orchestration layer for enterprise AI data workloads.

Emerging players

• Fluid (Apache Fluid): Apache Fluid is an open-source data orchestration project for cloud-native AI/ML workloads that caches datasets for schedulers like Kubernetes, Yarn, and Fluid runtime. It overlaps with Alluxio's data acceleration mission but is at an earlier maturity stage and is focused on dataset caching rather than full distributed caching.

Broad incumbents

• NetApp: NetApp is an established enterprise storage incumbent with ONTAP, cloud-tier, and AI-optimized offerings (e.g., AIPod). It competes with Alluxio at large enterprise accounts and offers overlapping hybrid/multi-cloud data management capabilities, though not specialized for GPU clusters.
• Pure Storage: Pure Storage offers all-flash enterprise storage with growing AI workload positioning (FlashBlade for AI). It competes with Alluxio for enterprise AI infrastructure spend, particularly in on-premises and hybrid deployments, with a much larger installed base.

Market position

Strengths5 records

Weaknesses5 records

Competitive moat4 records

Key risks6 records

Key highlights7 records

Customer concentration

• GitHubgithub.com/alluxio/alluxio
• YouTubeyoutube.com/channel/UCpibQsajhwqYPLYhke4RigA
• Xx.com/Alluxio
• LinkedInlinkedin.com/company/alluxio-inc-

Revenue estimate

Valuation estimate

Number of profiles

Profiles7 records

Funding overview

Funding rounds4 records

Investors5 records

Funding detail is available on the Subscription and Enterprise plan.Contact sales →

M&A

Investments

M&A and investment is available on the Subscription and Enterprise plan.Contact sales →

Frequently asked questions about Alluxio