Alternatives
Products that do what Object store built for AI workloads does
High performance, AI-native objectstore with S3 API included
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Hi HN, We're Luke and Phillip, and we're building Spice.ai OSS - a lightweight, portable runtime, built in Rust and powered by Apache DataFusion to locally materialize, accelerate, and query data tables sourced from any database, data warehouse or data lake. Phillip and I first introduced Spice on Show HN in September 2021. Since then, we’ve been schooled and humbled in every way building 100TB+ data and ML systems for the https://spice.ai cloud platform. Along with our customers, we struggled with getting fast, low-latency, high-concurrency SQL query within a budget, accessing and…
2024 · github.com
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We started CallFS after yet another late-night “why did the uploads vanish?” incident. Our small team had stitched together rsync, a fragile NFS mount, and an S3 bucket—none of it observable, all of it waiting to bite us. So we wrote a single-process file service in Go that: • Speaks the S3 API (so existing tooling works). • Stores hot data on local disks for speed; cold data can sit in any S3-compatible bucket. • Exposes Prometheus metrics and JSON logs by default, because “what happened?” shouldn’t be guesswork. • Ships as a ~25 MB static binary—no external deps, MIT license. Today it’s…
2025 · github.com
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Hey HN, it’s been just over a year since we launched HelixDB (https://news.ycombinator.com/item?id=43975423), a project a friend and I started in college. It’s an OLTP graph database built on object-storage, with native vector search and full-text search (FTS). Why graph, vector and FTS? Graph databases provide a natural cognitive model for data, vectors allow for a semantic understanding of the entities and relationships in the graph, and FTS provides more specific filtering. Many AI-driven applications attempt to combine all of these functionalities by stitching together…
Jun 2026 · github.com
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2022 · shalerb.org
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Hey HN, we want to share HelixDB (https://github.com/HelixDB/helix-db/), a project a college friend and I are working on. It’s a new database that natively intertwines graph and vector types, without sacrificing performance. It’s written in Rust and our initial focus is on supporting RAG. Here’s a video runthrough: https://screen.studio/share/szgQu3yq. Why a hybrid? Vector databases are useful for similarity queries, while graph databases are useful for relationship queries. Each stores data in a way that’s best for its main type of query (e.g.…
2025 · github.com
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Hey HN! I built a proof-of-concept for AI memory using Git instead of vector databases. The insight: Git already solved versioned document management. Why are we building complex vector stores when we could just use markdown files with Git's built-in diff/blame/history? How it works: Memories stored as markdown files in a Git repo Each conversation = one commit git diff shows how understanding evolves over time BM25 for search (no embeddings needed) LLMs generate search queries from conversation context Example: Ask "how has my project evolved?" and it uses git diff to show actual…
2025 · github.com
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Hi! One of the creators here. Very proud to finally be able to show you what we've been working on for over a year now. Curious to hear your thoughts! Objectiv is open-source (APLv2) product analytics infrastructure. It's built around a generic but strict event taxonomy, open/common data- and infra tools (currently PG, snowplow, working on bigquery with more to come), and the analyses are done using our pandas-like, SQL speaking modeling library called Bach. As a result, we’re moving towards a vision wherein models can be shared openly, independent of product, platform[1] or data…
2022 · objectiv.io
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we've been working on a KV store for the past year or so which is 2-6x faster than Redis (benchmark link below) yet disk persisted! so you get the speed of in-memory KV stores but with disk persistence. To achieve this we've created our custom filesystem that is optimized for our special usecase and we're doing smart batching for writes and predictive fetching for reads. In addition to basic operations, it also provides atomic inc/dec, atomic json patch, range scans and a unique key monitoring mechanism (pub-sub) over WebSockets which essentially allows you to receive notification on…
2025 · hpkv.io
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Very excited to share Anvil. I built Anvil to take back control when working with parallel coding agents. It comes with one click worktree isolation, and first class spec support. Claude Code and similar coding TUIs are very eager to get into writing code, even before their human baby sitter fully understands the implication of what they are about to build. The core insight with Anvil is that it is much easier to write high quality code which matches the author's intent after iterating on an external plan with your agent. Align on the architecture, implementation, and verification strategy…
Mar 2026 · github.com
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Metal-first MoE inference for Apple Silicon with bounded SSD expert streaming and local OpenAI Chat, Responses, and Anthropic Messages endpoints. - hebrus-labs/hebrus
Jul 2026 · github.com
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2016 · github.com
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Hi HN, I'm Emilie, I have a literature background (which explains the well-written documentation!) and I've been learning Rust and distributed systems by building minikv over the past few months. It recently got featured in Programmez! magazine: https://www.programmez.com/actualites/minikv-un-key-value-st... minikv is an open-source, distributed storage engine built for learning, experimentation, and self-hosted setups. It combines a strongly-consistent key-value database (Raft), S3-compatible object storage, and basic multi-tenancy. Features/highlights: - Raft…
Feb 2026 · github.com
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2018 · phantombuster.com
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Hi HN, we're Kiran and Vijay! Over the past two years, we have built a columnar storage engine for observability: logs, metrics, and traces. Today, it's exciting for us to show what we've built on top of that foundation: LLM Agent Observability. Given how non-deterministic agents are, storing all traces without sampling was critical for us. But these traces tend to be in the MBs, sometimes GBs - we needed to store them inexpensively. We also needed the queries and analyses to be fast. To meet both these goals, we store them in S3 in our own parquet-like file format, and query them using AWS…
Jul 2026 · oodle.ai
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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…
Sep 2025
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Disclaimer it is a heavily AI assisted project. The goal was not to be the most performative but the kind that's easier to learn from. I wanted to share this in case there are people who had the same idea or wanted to see something like this.
Jun 2026 · github.com
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Dec 2025 · github.com
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2025 · spiceai.org
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