Archil provides an infinitely scalable, shareable cloud file system that mounts directly onto existing S3 buckets, delivering up to 30× faster read/write performance for AI model training, analytics, and agentic workloads. Its usage‑based billing charges only for actively accessed data, enabling up to 90% cost savings compared with traditional block storage, and it integrates with common AI platforms via standard file system APIs.
Funding
$500K raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Founders
Product
Problem
AI and analytics applications require fast, active access to large datasets, but existing cloud storage solutions like S3 are optimized for inactive data, forcing developers to combine multiple storage services and incur high data transfer costs and latency.
Solution
Archil offers infinitely scalable, shareable cloud volumes that mount directly onto S3 datasets, delivering up to 30× faster data access for AI model training and agentic workloads. The system provides intelligent data management that tracks active usage, allowing customers to pay only for the data they are actively processing, which can reduce storage costs by up to 90% compared with traditional block storage such as EBS. By presenting a single, high‑performance file system interface, Archil eliminates the need for complex storage stacks and simplifies data pipelines for AI teams.
Target Audience
Primary customers are AI/ML engineers, data scientists, and DevOps teams building large‑scale model training, analytics pipelines, and autonomous agent applications that require fast, cost‑effective access to cloud data.
Features
- Infinite, shareable volumes that attach to existing S3 buckets without data duplication
- Up to 30× acceleration of read/write operations for active AI workloads
- Usage‑based billing that charges only for actively accessed data, enabling up to 90% cost savings versus EBS
- High‑performance file system optimized for model training, analytics, and agentic applications
- Seamless integration with common AI platforms and frameworks through standard file system APIs