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Moss
Sub-10ms semantic search for voice agents and copilots, distributed in-browser, on-device or in the cloud
0Visit Moss
https://moss.dev
About Moss
Moss is real-time semantic search built for the latency budget of conversational AI. Voice agents and copilots break when retrieval is slow — a 300ms vector lookup that is fine in a RAG chatbot is fatal in a phone call where the caller hears the pause — so Moss targets sub-10ms retrieval and distributes the index to wherever the agent actually runs, including in-browser and on-device rather than only in a cloud region. You connect your data once and Moss handles indexing and distribution across those surfaces. The integration list reads as a map of the production voice stack: LiveKit, Pipecat, VAPI and ElevenLabs on the voice side, LangChain, DSPy and the Vercel AI SDK on the orchestration side, plus Next.js, VitePress and an MCP server for docs search. Pricing is per-plan plus usage, which is worth reading carefully — every tier is quoted as a platform fee plus usage costs, so the headline number is a floor rather than a bill. The Developer tier is free with $5/month in credits, unlimited local queries and shared infrastructure; Hobbyist at $30 adds the continuous sync engine, unlimited projects and indexes, and session replays; Start-Up at $200 adds hot-path cloud search with 150 concurrent sessions and priority ingest. It is built by InferEdge Inc. in San Francisco.
Key Features
Moss Pros & Cons
✅ Pros
- +On-device and in-browser distribution removes a network hop entirely
- +Voice-stack integrations are the ones that actually matter for latency
- +Free tier includes unlimited local queries, not a query cap
⚠️ Cons
- −Every tier adds usage costs on top, so the listed price is a floor
- −$200/month jump to hot-path cloud search is steep for small teams
- −Narrower than a general vector database if you also need batch analytics
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