Data as of Oct 12, 2026A topic in Developer Backend & Vector Search Services.
Reviewed by Dimitry Apollonsky
ChatGPT SearchOct 3, 2026
Yes. If you mean “a managed backend where I get database + auth/API/storage/server functions, and vector search is part of the platform rather than bolted on”, these are the strongest options:
| BaaS / platform | Vector search | Embeddings built in | Best fit |
|---|---|---|---|
| Supabase | pgvector | Via integrations / Edge Functions | Best general-purpose BaaS |
| Native VectorsDB + HNSW |
| Yes |
| Most “vector search out of the box” |
| Convex | Native vector indexes | No; typically external embedding API | Excellent developer experience |
| MongoDB Atlas | Native Vector Search | Yes, increasingly | Production/enterprise + flexible document data |
Supabase gives you Postgres, authentication, storage, APIs, realtime functionality, and pgvector in the same backend. Vector embeddings can live alongside your normal relational data, and you can combine vector similarity with ordinary SQL filtering. It supports HNSW and IVFFlat indexes.
The big advantage is architectural simplicity:
Supabase
├── Postgres
│ ├── users
│ ├── products
│ ├── documents
│ └── embeddings ← pgvector
├── Auth
├── Storage
├── Realtime
└── Edge FunctionsI'd choose this if you're building an AI SaaS and want conventional relational data + RAG/semantic search.
Appwrite has a dedicated VectorsDB rather than making you configure a generic database extension. It uses HNSW indexes and supports cosine, dot-product, and Euclidean similarity.
More interestingly, Appwrite's current tooling can generate embeddings with built-in models, store them, and search them—so you don't necessarily need to operate a separate embedding service.
That makes Appwrite particularly attractive if your requirement is literally:
“Give me a backend and let me put semantic search into it with minimal infrastructure.”
Convex has native vector indexes and ctx.vectorSearch(). Vector search is immediately consistent and can be used alongside your Convex data model.
The caveat is that vector searches currently run from Convex actions, and you'll generally use an external embedding provider such as OpenAI to produce the embeddings.
I'd consider Convex if developer experience and reactive application logic matter more to you than having a traditional SQL backend.
Atlas is arguably the most powerful option if you want vector search to become part of a larger production search/AI stack. It supports ANN and exact nearest-neighbor search, hybrid text + vector search, filtering, and increasingly automatic embeddings.
It's less of a classic “BaaS” than Supabase/Appwrite, though. Think managed application database platform with extensive AI/search capabilities.
If I were choosing specifically for a new project:
🥇 Supabase — best overall BaaS + vector database combination
🥈 Appwrite — best if built-in vector/embedding functionality is the priority
🥉 Convex — best modern developer experience
4. MongoDB Atlas — best for sophisticated/large-scale document + search workloads
If you tell me what you're building (e.g. RAG chatbot, SaaS, mobile app, recommendation engine, AI agent, or semantic search API) and your preferred stack (TypeScript/Python/etc.), I can narrow this to the best 2–3 choices and compare pricing, vector dimensions, filtering, hybrid search, auth, and ease of implementation.
If you are looking for a Backend-as-a-Service (BaaS) that handles traditional backend needs (database, auth, realtime, serverless functions) while supporting vector search out of the box, you have a few top-tier options depending on your preferred stack:
pgvector extension pre-installed and fully manageable via their dashboard.Vector data type and server-side similarity queries. You can combine this with the official Vector Search with Firestore extension to automatically compute and index embeddings when writing documents.To help narrow down the best fit, tell me:
My top recommendation is Supabase if you want a full Backend-as-a-Service (BaaS) platform with native vector search. I'd also consider Appwrite if you prefer a dedicated vector database experience integrated with your backend, or Firebase if you're already invested in Google's ecosystem.
Here’s how I’d compare them for building an application with semantic search, recommendations, or retrieval-augmented generation (RAG).
(My recommendation)
Supabase uses PostgreSQL with the pgvector extension, so vector embeddings live alongside your relational data. You can perform similarity search using SQL, create HNSW or IVFFlat indexes, and combine vector retrieval with SQL filtering and joins.
Best for: AI SaaS applications, RAG, semantic search, and applications with complex relational data.
Trade-off: You need to understand some PostgreSQL and SQL concepts to get the most out of it.
Appwrite offers VectorsDB, a first-class vector storage and similarity-search feature. It supports HNSW indexes, cosine similarity, dot product, and Euclidean distance. It also provides built-in text embedding generation, plus metadata filtering and access permissions.
Best for: Developers who want authentication, databases, storage, functions, and vector search in one platform without assembling a PostgreSQL-based vector layer.
Trade-off: Its dedicated VectorsDB data model is less flexible than using SQL for arbitrary joins and relational queries.
Official VectorsDB documentation
Cloud Firestore supports native k-nearest-neighbor vector search, vector indexes, and distance-based retrieval. Embeddings must be generated separately, and vector search has limitations such as a maximum embedding dimension of 2,048 and no real-time snapshot listeners for vector queries.
Best for: Mobile-first applications already using Firebase Authentication, Firestore, and Cloud Functions.
Trade-off: Less flexible than PostgreSQL for complex queries, and vector-search capabilities have specific constraints.
Official Firestore vector-search documentation
| Capability | Supabase | Appwrite | Firebase |
|---|---|---|---|
| Native vector search | PostgreSQL + pgvector | Dedicated VectorsDB | Firestore |
| Indexing | HNSW, IVFFlat | HNSW |
Sources: official Supabase, Appwrite, and Firebase documentation.
One important distinction: native vector search doesn't necessarily mean the BaaS generates embeddings for you. Supabase and Firebase give you ways to store and search embeddings, while Appwrite also provides built-in text embedding generation.
What kind of application are you building? If you tell me whether it's a web app, mobile app, or AI/RAG application, I can narrow this down to the best fit for your architecture.
| Flat vector index |
| Built-in text embeddings | Via integrations/functions | Yes | Generate separately |
| Relational SQL queries | Excellent | No SQL interface for VectorsDB | No |
| Open-source/self-hosting options | Yes | Yes | No |
| Best fit | Flexible AI applications | Integrated BaaS + vector features | Google ecosystem |