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Archil's one-click infinite, S3-backed local disks now available

Hacker News

Archil's one-click infinite, S3-backed local disks now available

Hey everyone, I’m Hunter, the founder of Archil. Archil is transforming object storage, like Amazon S3, into infinite, local file systems that provide instant access to massive data sets. Last year, we launched Archil’s NFS-based product publicly on Hacker News ( https://news.ycombinator.com/item?id=42174204 ), and we were absolutely thrilled to see the response of this community. Since our last launch, we took a 10 month company-wide bet to build our own, custom storage protocol to deliver true, local-like performance to cloud instances – by behaving closer to a block storage product. Today, we are excited to announce the public availability of this high-performance protocol. You can try it for free today with a single click at https://disk.new (docs at https://docs.archil.com ). After spending 10 years in the storage industry building products like Amazon’s Elastic File System and buying AWS storage on Netflix’s core storage team, I knew that developers faced tremendous problems using storage. Getting easy-to-use persistent storage on a Kubernetes cluster is non-trivial, determining how much storage an application needs can be impossible if it’s bursty, and companies are tired of overpaying for either very expensive storage (like EFS) or unused capacity (like with EBS). From my time on EFS, I also knew that NFS was not the solution. Users were disappointed to see a 10x performance drop when they moved data from EBS to EFS to share it across multiple instances. This is why we built Archil. Archil is a pay-as-you-go disk that automatically grows with your application. Because it synchronizes your data bidirectionally to sources like your Amazon S3 buckets (and other S3-compatible locations), you can connect it instantly to existing data sets and use your file data directly from S3 itself. When you mount an Archil disk, your instance connects to our managed caching fleet of instances with NVMe devices that provide read-through and write-back caching to your disk’s data sources, like S3. Because we designed a distributed, replicated, highly-durable cache, it’s a safe place for data to be stored before being written back to S3. We only charge users for data that’s actively in the cache, and when you aren’t using your disk, you don’t pay Archil anything. We’re really excited about the early companies that we’re working with who are building cutting edge CI/CD workers, performing satellite image processing, building serverless Jupyter Notebooks, creating AI-native code sandboxes (have you ever tried to run git directly on an NFS share?), building best-in-class AI agents that use file systems instead of MCP tools, and helping to deliver core AI infrastructure like gateways and compute. I’m excited to give you all access today, with no charges in the month of September, and I’d love any feedback on the product that you may have. If you’re interested in working on deep, technical problems in the core infrastructure space, we’re also hiring in San Francisco and would love to hear from you (see https://www.ycombinator.com/companies/archil/jobs/svfkDVv-fo... ).

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Indie HackersFits the IH revenue-focused audience · Strong signals: compatible · Missing: supports, reddit linkedin, podcasting
95%95% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Product HuntOn track for Day 1 leaderboard · Strong signals: agents, agent, mcp · Missing: mac, macos, cursor
87%87% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: excited, exist, existing · Missing: https docs, just released, lua
85%85% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
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46%46% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
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