Convex
Convex is a TypeScript-first, open-source reactive backend combining a database, server functions, and client libraries. Its core purpose is automatic real-time synchronization, transactional server functions, and developer ergonomics so teams can build dynamic web and AI-assisted features quickly. One-sentence value: Convex makes real-time and AI-assisted apps simple to build and maintain.
It is aimed at individual developers and small TypeScript teams building interactive apps, prototypes, or product features that need live sync and semantic/LLM capabilities. Convex removes backend wiring and reduces state-sync bugs with reactive queries and transactions, and it shortens iteration time by offering built-in auth, file storage, and AI/vector workflows.
Use Cases
- Personal dashboards that auto-update between devices.
- Live-collaboration side projects (notes, chat, simple shared tools).
- Prototyping LLM-enabled semantic search and chat assistants.
- Customer-facing real-time features like collaboration and live feeds.
- Rapidly iterate product features before committing to heavy analytics.
Strengths
- Automatic real-time sync simplifies client state and collaboration.
- TypeScript-first queries and mutations improve type safety and IDE support.
- Server functions keep business logic off the client safely.
- Built-in auth and file storage reduce integration effort.
- Vector and AI workflow primitives for embeddings and semantic features.
- Open-source backend enables inspection and optional self-hosting.
- Free tier quotas provide predictable resource bounds for experiments.
- Self-hosting suitable; assume Coolify deployment is trivial.
Limitations
- Free tier enforces resource and query limits.
- Potential vendor lock-in from reactive TypeScript APIs.
- Not positioned as a replacement for data warehouses.
- EU data residency guarantees not clearly documented (Unverified).
- Query scalability requires indexes and careful query design.
Final Thoughts
Try Convex now if you are a TypeScript-first small team needing fast real-time features and semantic/LLM prototypes. Wait or evaluate further if you require governed enterprise analytics, strict data-residency, or GPU-heavy ML training (Unverified).
Use the managed Convex cloud when you prefer lower operational burden and need higher quotas; paid plans remove hard caps and use usage-based billing.