Singlebase

Developers building AI applications today typically need to stitch together five or more separate platforms to get a document database, vector database, authentication, file storage, RAG pipeline, and LLM access working together. This fragmentation adds complexity for indie developers, small teams, and agencies trying to prototype or ship AI products quickly. The page frames this directly with the line 'Building AI apps shouldn't require five different platforms,' positioning integration overhead as the core cost developers face.

Singlebase combines Document DB, Vector DB, Auth, File Storage, Deep RAG, Chunking, Vectorization, Premium LLM access, Doc-IQ, AI AgentBase, and Documents-to-Markdown conversion into one unified backend platform accessed through a single SDK (singlebase.js). A code example shows ingesting a file with vectorize and summarize options and querying it via a RAG endpoint in just a couple of lines. The platform gives access to multiple frontier models (Gemini, Claude, GPT, DeepSeek, Qwen, Mistral, Llama, Nova, MiniMax) and offers specific products including a Vectorstore for semantic search, a Datastore document database, Authentication with social login and two-factor support, a Knowledge Base with versioning and AI-powered suggestions, a Generative AI platform, Filestore object storage, Similarity Search, and Images processing. Data can be accessed through REST, GraphQL, or SQL APIs, with SDKs for JavaScript, Python, and PHP, and the platform emphasizes data ownership and portability plus automated backups.