{"slug": "moss-sub-10-ms-semantic-search-runtime", "title": "Moss: Sub-10 ms semantic search runtime", "summary": "Moss launches a semantic search engine with sub-10 ms retrieval latency, eliminating the need for vector databases and enabling real-time AI applications in browser, edge, device, or cloud environments. The tool is used by teams building voice AI, copilots, and real-time systems where milliseconds impact user experience.", "body_md": "Preparing your content\n\nPreparing your content\n\nBuilt for Production AI Systems\n\nFix it with <10ms search. No vector databases. No latency bottlenecks. Runs directly in browser, edge, device, or cloud.\n\nUsed by teams running voice AI, copilots, and real time systems\n\nwhere milliseconds directly impact user experience.\n\n<10ms\n\nEnd to end retrieval latency\n\nUp to 100x faster than vector databases\n\n250K+ installs\n\nUsed by developers building production AI systems\n\nAcross voice, copilots, and real time applications\n\n100% local execution\n\nOffline indexing and querying\n\nNo external vector database required\n\nUsed in production by teams building real time AI systems\n\nRethinking retrieval\n\nNo external retrieval layer. No network hops. Eliminate latency at the source.\n\nBrowser. Edge. Device. Cloud. Deploy where performance matters most.\n\nEnable real time conversational experiences. No lag. No infrastructure overhead.\n\nDeveloper Experience\n\nAdd <10 ms retrieval to your AI stack in a few lines of code\n\nWorks with your existing LLM stack including LangChain and Vercel AI SDK.\n\n``` python\nfrom moss import MossClient\n\nclient = MossClient(PROJECT_ID, PROJECT_KEY)\n\ndocs = [{\"text\": \"How do I track my order?\"}]\n\nawait client.add_docs(\"my-index\", docs)\n```\n\nBenchmarks\n\nBenchmark run on 100K documents. Includes embedding inference and end to end retrieval latency. [View benchmark script](https://github.com/usemoss/moss/tree/main/benchmarks)\n\nIntegrations\n\nDrop Moss into your existing stack across voice, LLM frameworks, and frontend AI\n\nUse Cases\n\nFor systems where retrieval is on the critical path and latency directly impacts user experience\n\n<10 ms context retrieval for real time conversation. Your agent responds instantly without latency or network overhead.\n\nFAQ\n\nAnswers to common questions about latency, architecture, and production deployment", "url": "https://wpnews.pro/news/moss-sub-10-ms-semantic-search-runtime", "canonical_source": "https://www.moss.dev", "published_at": "2026-07-08 07:26:56+00:00", "updated_at": "2026-07-08 07:29:32.040508+00:00", "lang": "en", "topics": ["ai-tools", "ai-infrastructure", "natural-language-processing", "ai-products"], "entities": ["Moss", "LangChain", "Vercel AI SDK"], "alternates": {"html": "https://wpnews.pro/news/moss-sub-10-ms-semantic-search-runtime", "markdown": "https://wpnews.pro/news/moss-sub-10-ms-semantic-search-runtime.md", "text": "https://wpnews.pro/news/moss-sub-10-ms-semantic-search-runtime.txt", "jsonld": "https://wpnews.pro/news/moss-sub-10-ms-semantic-search-runtime.jsonld"}}