AI-powered backend platform with Vector DB, DocumentDB, Auth, and more to speed up app development.
Singlebase is a backend platform for AI applications that combines document database, vector database, authentication, file storage, and AI services into a single unified system. Core capabilities include a managed document/relational datastore, a vector store for semantic and similarity search, retrieval-augmented generation (RAG) with chunking and vectorization, a knowledge base management system, and object/image storage. The platform provides access to multiple LLM providers including Gemini, Claude, GPT, DeepSeek, Qwen, Mistral, Llama, Nova, and MiniMax. Data can be accessed through REST, GraphQL, or SQL APIs, with SDKs available for JavaScript, Python, and PHP.
The product is positioned as a Firebase alternative aimed at developers building AI-powered applications who would otherwise need to combine separate database, auth, storage, and AI infrastructure services. It targets solo developers and indie hackers prototyping AI apps, growing teams building RAG-based products, and small agencies or power users running data-intensive, high-frequency AI workloads.
Singlebase offers three paid plans: Solo at $20/month for a single developer seat, Growth at $50/month for up to 3 developers with expanded LLM access and RAG pipeline resources, and Pro at $100/month for up to 10 developers with premium LLM access (OpenAI, Gemini, Anthropic), document-to-markdown conversion, semantic search, and priority support.
No free tier is mentioned; plans start at a paid Solo tier for $20/month.
Solo is $20/month for 1 developer seat, Growth is $50/month for up to 3 developers, and Pro is $100/month for up to 10 developers.
It provides access to models from Google DeepMind (Gemini), Anthropic (Claude), OpenAI (GPT), DeepSeek, Alibaba Cloud (Qwen), Mistral AI, Meta AI (Llama), AWS (Nova), and MiniMax.
Singlebase offers SDKs for JavaScript, Python, and PHP, plus REST, GraphQL, and SQL APIs.
It targets indie hackers and solo developers prototyping AI apps up through growing teams and small agencies managing data-heavy, AI-intensive projects.
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