All Open Source Apps

Self-hosted Backend-as-a-Service for Node.js with REST, GraphQL, real-time Live Query, cloud code, and pluggable adapters for any infrastructure.

Auto-magical MLOps platform that tracks experiments, versions data, orchestrates pipelines, and serves models with just two lines of code.

Build, run, and manage agent platforms with a full production stack — SDK, runtime, and control plane included.

Open-source self-hosted PaaS — deploy apps, databases and 280+ services on your own servers with no vendor lock-in

Open-source vector database and search engine built in Rust for production-grade AI applications — from semantic search to RAG pipelines and recommendation systems.

Open-source, embedded vector database built on the Lance columnar format for fast multimodal search across billions of vectors, backed by Y Combinator (W23).

Open-source GenBI engine that lets AI agents turn natural-language questions into governed SQL, charts, and shareable dashboards across 20+ data sources — no vendor lock-in, no black-box prompts.

Embedded KV store built on object storage with zero replication cost

A graph-vector database built from scratch in Rust that unifies graph traversal, vector search, key-value, and relational storage into a single platform for AI applications.

Open-source time-series database built for real-time ingest, fast SQL queries, and embedded Python automation — powered by Apache Arrow and Parquet.

The dbt-native data observability CLI that turns your existing dbt tests and metadata into anomaly detection, lineage graphs, and Slack/Teams alerts — no separate platform required.

A self-hosted Rust PaaS that replaces Vercel, Sentry, PostHog, Pingdom, Resend, and E2B with one binary — plus 440+ CLI operations agents like Claude Code can drive directly.

A pretrained decoder-only foundation model by Google Research that delivers zero-shot time series forecasting with calibrated quantile prediction intervals — no training required.

A RocksDB-based NVMe/S3 cache purpose-built for AI inference workloads — a faster Redis replacement optimized for batch, low-latency, zero-copy reads and writes between data pipelines and inference apps.

Federated AI search and RAG across 100+ enterprise sources—no data extraction, no vector database required.