Frequently Asked Questions

Traditional vector databases require servers, complex deployments, and ongoing maintenance. Memvid eliminates that complexity - everything is in one portable file. Share it via email, USB, or Git. Works offline. No vendor lock-in. Perfect for edge deployments, local-first apps, and teams that value simplicity.

Sub-5ms search latency on consumer hardware. Our hybrid search engine combines BM25 lexical matching with semantic vector search, delivering 4.2x better accuracy-per-millisecond than competing solutions. See our benchmarks for detailed performance data.

Yes! Memvid is fully offline-capable. The .mv2 file contains everything needed - data, embeddings, and indices. No internet connection required for search, retrieval, or RAG operations. Perfect for air-gapped environments, edge devices, and privacy-conscious applications.

Absolutely. We provide native SDKs for Python, Node.js, and Rust. Plus a CLI for quick operations and an MCP server for direct integration with AI models like Claude. The SDK lets you add memory to any agent framework in just a few lines of code.

Free tier includes 1GB per file - enough for millions of documents. Paid plans go up to 100GB per file, and Enterprise has unlimited capacity. The embedded WAL ensures crash-safety at any scale, with automatic checkpoint management.

The core engine (memvid-core) and CLI are open source under the MIT license. You can self-host forever for free. The cloud dashboard, enterprise features, and some advanced capabilities are part of our commercial offering.

Memvid includes a Model Context Protocol (MCP) server that lets AI models like Claude directly access your .mv2 files. Start the MCP server, point it to your memory file, and your AI can search, retrieve, and reason over your knowledge base in real-time.