“1. Supabase – PostgreSQL + pgvector + Edge Functions.”
“2. Supabase — best all-round backend”
“One particularly useful feature is its AI inference API, which can generate embeddings and run certain models directly from Edge Functions.”
“- Use Row Level Security for tenant isolation.”
“2. Convex – Reactive database + built-in vector search + server-side functions.”
“1. Convex — best for agent-native development”
“Convex is one of the strongest choices if you want the backend itself to provide much of the agent infrastructure, rather than assembling everything yourself.”
“- Durable execution with retries and scheduling capabilities.”
“3. Firebase – Firestore + Firebase AI Logic + Genkit integrations.”
“- Genkit provides AI workflow orchestration.”
“Important limitation: Firestore stores and searches embeddings but does not generate them itself.”
“4. Postgres / Neon – PostgreSQL + pgvector + serverless functions.”
“5. MongoDB Atlas – Document database + Vector Search + application-side AI orchestration.”
“3. MongoDB Atlas — best for document-centric AI”
“Its Automated Embedding feature is especially interesting because it handles embedding generation and synchronization for you.”
“- Integrations with LangChain, LangGraph, CrewAI, and other agent frameworks.”
“7. Azure – Cosmos DB, Azure OpenAI, and AI Search.”
“8. Google Cloud – Firestore, Vertex AI, and Vector Search.”
“6. AWS – DynamoDB, Bedrock, Lambda, and vector search integrations.”
“4. AWS — best for large-scale infrastructure”
“- AWS: DynamoDB now supports native vector indexes alongside operational data.”
