I need a backend-as-a-service that includes vector search capabilities out of the box.

If you need a Backend-as-a-Service (BaaS) that handles vector search out of the box without needing to stitch together an external vector database manually, a few prominent platforms offer native vector support, authentication, auto-generated APIs, and real-time capabilities.

• Supabase — The strongest open-source Firebase alternative for this use case. Because it is built on PostgreSQL, it includes native support for the pgvector extension . You can toggle vector on in the dashboard, store high-dimensional embeddings right alongside your relational data, query them via SQL/RPC functions, and interface with them directly using their client libraries or Edge Functions.
• Firebase (Cloud Firestore) — Google’s managed BaaS supports Firestore Vector Search natively. You can define vector configurations directly inside Firestore documents (up to 2048 dimensions) and execute nearest-neighbor similarity queries (findNearest ) from client SDKs. You can also pair it with the official Vector Search with Firestore extension to automatically generate embeddings using Gemini/Vertex AI whenever your documents update.
• Appwrite — Appwrite's core databases don't feature a built-in vector engine out of the box (they recommend using Appwrite Functions to hook into an external specialized vector DB if you scale up), meaning it requires extra orchestration compared to Supabase or Firebase.

If you want to narrow this down, let me know:

• Are you leaning toward a PostgreSQL-based stack (Supabase) or a NoSQL/Google Cloud ecosystem (Firebase)?
• Do you need the BaaS to automatically generate embeddings for you via an LLM integration, or will you push pre-computed vectors from your own code?

Yes. If you want BaaS + database + authentication/storage/functions + vector search without bolting on a separate vector DB, my first choice is Supabase.

Best options

• supabase.com — best overall

• PostgreSQL-based BaaS.

• Built-in pgvector for storing and querying embeddings.

• Supports semantic, keyword, and hybrid search.

• You can combine vector similarity with normal SQL filters/joins, which is particularly useful for RAG applications.

• Also gives you auth, storage, realtime, Edge Functions, APIs, and database management.

• It now has tooling for automatic embedding generation/update pipelines, although you'll still generally configure the embedding provider/model yourself.

• appwrite.io — good if you prefer a more traditional BaaS

• Strong BaaS offering with databases, auth, storage, and functions.

• However, its vector-search story is more integration-oriented: Appwrite's own documentation demonstrates connecting Functions to external vector databases such as Pinecone, rather than having a native pgvector-style vector database built into its primary database.

• So I wouldn't choose it if "vector search out of the box" is a hard requirement.

My recommendation

For an app like:

Users → Auth → Postgres data → documents → embeddings → semantic search/RAG → API I'd use Supabase. You get one Postgres database containing both your application data and embeddings, rather than maintaining a separate Pinecone/Qdrant/etc. system. Supabase explicitly positions this as keeping vector embeddings alongside the rest of your relational data.

One important distinction: vector storage/search and embedding generation aren't the same thing. Supabase gives you the former natively through pgvector; embedding generation can be automated with Supabase's Edge Functions/queues/cron setup and an embedding provider.

If you tell me your stack (e.g. Next.js, Python/FastAPI, Flutter, React Native) and expected scale, I can narrow this down to the 2–3 best BaaS choices and compare pricing, vector performance, auth, and developer experience.

If you want a true backend-as-a-service with vector search built in, my first choice is Supabase.

Best options

• supabase.com — Best overall

• Full PostgreSQL database, authentication, storage, APIs, realtime, and serverless/Edge Functions.

• Vector search is built into Postgres through pgvector, so vectors live alongside your normal relational data.

• Supports cosine, inner-product, and Euclidean similarity, plus HNSW and IVFFlat indexes.

• Particularly good for RAG, semantic search, recommendations, and AI applications.

• Supabase also has tooling for automatic embedding generation/update pipelines.

• Appwrite — Good Firebase-style alternative

• Provides database, auth, functions, storage, etc.

• Its official AI documentation currently describes integrating with external vector databases such as Pinecone rather than having vector search as deeply integrated as Supabase/pgvector.

• I'd choose it primarily if you prefer Appwrite's developer experience or self-hosting model.

My recommendation

For a new application, I'd use:

Supabase → PostgreSQL + pgvector + Auth + Storage + Edge Functions

That gives you a single backend instead of maintaining a conventional database plus a separate vector database. Supabase explicitly supports combining semantic search with normal relational filtering and joins.

If you tell me your stack (Next.js, Python, Flutter, etc.) and approximate data size, I can narrow this down to the best 2–3 choices and explain the tradeoffs.

If you need a Backend-as-a-Service (BaaS) that handles authentication, databases, file storage, and serverless logic , but also gives you vector search capabilities out of the box , you have a few standout options depending on whether you prefer a SQL or NoSQL paradigm.

Here are the best BaaS platforms providing native or first-party vector search support:

1. Supabase (The Best SQL/Postgres Choice)

Supabase is widely considered the leading open-source Firebase alternative and comes with native vector search powered by the pgvector extension.

• How it works out of the box: Every Supabase project is a full PostgreSQL database with pgvector pre-installed. You can store high-dimensional embeddings directly in your tables alongside regular relational data.
• Vector capabilities: Supports exact and approximate nearest-neighbor (ANN) searches using HNSW and IVFFlat indexes, cosine distance, inner product, and L2 distance.
• BaaS ecosystem perks: Built-in Auth with Row Level Security (RLS) that applies directly to your vector queries, Auto-generated APIs, Edge Functions (great for running embedding generation models on the fly), and Realtime subscriptions.
• Get started: Explore the Supabase Vector Documentation to see implementation guides for RAG and semantic search.

2. Firebase / Google Cloud (The Best NoSQL Choice)

Firebase doesn't have vector search built into the core Firestore database natively by default, but Google provides official first-party extensions that bridge the gap seamlessly.

• How it works out of the box: You use standard Cloud Firestore, then install the official extension: "Vector Search with Firestore".
• Vector capabilities: It automatically syncs your Firestore data to a Vector Search index (backed by Google Cloud's Vertex AI infrastructure), letting you run low-latency semantic and nearest-neighbor searches directly from your mobile/web client SDKs.
• BaaS ecosystem perks: Tight integration with Google's Vertex AI Embeddings API, robust authentication, real-time data listeners, and global scaling.
• Get started: Read up on the implementation steps via the Firebase Vector Search Documentation.

3. Appwrite (The Self-Host / Flexible Choice)

Appwrite is another major open-source BaaS that has evolved rapidly for AI workflows. While its database layer historically abstracted document collections, Appwrite heavily embraces modern retrieval-augmented generation (RAG) and hybrid search patterns.

• How it works out of the box: Appwrite provides robust database collections, authentication, and execution environments, and pairs cleanly with external vectorizers or modular architecture if you choose to deploy it alongside an AI-ready stack.
• BaaS ecosystem perks: Great SDK support across platforms, simple permission models, and active community-driven integrations for AI pipelines.
• Get started: Check out the Appwrite Blog & Resources for architectural breakdowns on building AI backends.

Which one should you pick?

• Choose Supabase if you want to write standard SQL queries (<=>, <-> operators) for your vectors, need strict relational integrity alongside your embeddings, or want to leverage Row Level Security on AI data.
• Choose Firebase if your entire application architecture is already deeply rooted in the Google/Firebase ecosystem and you want a managed NoSQL extension approach.

To help narrow down the right platform, let me know:

• Are you planning to build a RAG chatbot , an e-commerce recommendation engine , or something else?
• Do you prefer a managed cloud service or do you need to self-host the backend?

Supabase and Convex are the top Backend-as-a-Service (BaaS) platforms providing built-in vector search. They remove the need to spin up separate, expensive vector databases like Pinecone.

The Top 2 Platforms

• Supabase : Best for teams preferring relational databases (PostgreSQL) and standard SQL tools. It provides vector search via the native pgvector extension.
• Convex : Best for teams wanting an all-in-one TypeScript ecosystem with live, reactive data streaming. It features native vector indexing directly inside its custom document database.

Feature Breakdown

Detailed Breakdown

Supabase Vector

Supabase includes pgvector natively. You can manage vectors directly beside your tables.

• The Setup : You enable the extension, create a column type vector(3)nm_dimensions , and write SQL queries or Edge Functions to run similarity searches.
• Advanced Scaling : For giant production datasets, Supabase offers Vector Buckets , an S3-backed storage engine designed to handle tens of millions of vectors cost-effectively.
• Hybrid Search : You can write a single SQL query combining Postgres's Full-Text Search (lexical keyword matching) and vector similarity.

Convex handles vector search using pure TypeScript cloud actions.

• The Setup : Define your vector index inside a schema file. You generate embeddings using your preferred LLM provider, insert the array into your document, and retrieve matches via ctx.vectorSearch inside a Convex action.
• Reactivity : While vector search itself occurs inside asynchronous "Actions", the documents you fetch using those vector IDs seamlessly tie back into Convex's reactive query engine.
• Agent Toolkit : Convex includes an open-source AI Agent Component that builds conversation thread memory and vector search retrieval automatically.

To help tailor a suggestion, what programming language is your frontend framework written in, and what types of data (e.g., PDFs, user profiles) are you planning to vectorize?

If you want a true backend-as-a-service (BaaS) with vector search built in, I’d shortlist these:

My recommendation: Supabase

Supabase is probably the safest choice if you want one backend that can handle normal application data and AI/vector workloads. Its Postgres database supports pgvector, including vector similarity search, HNSW and IVFFlat indexes, and you can combine vector searches with ordinary SQL filtering.

It also gives you the rest of the BaaS stack—auth, storage, APIs, realtime, and server-side functions—so you don't need a separate backend just to build an AI-powered application.

One particularly interesting newer feature is Vector Buckets, which are designed for much larger vector collections and can store/query millions of embeddings. They're currently in alpha, though, so I'd use regular pgvector for production unless you specifically need the newer storage model.

When I'd choose the others

• Appwrite — worth considering if you want a more traditional all-in-one BaaS and specifically like the idea of a dedicated VectorsDB alongside TablesDB/DocumentsDB. Appwrite now describes VectorsDB as providing embeddings and similarity search directly.
• Convex — particularly attractive for TypeScript/React applications where you want reactive data and a very streamlined developer experience. Vector indexes are defined directly in the Convex schema, and searches happen through ctx.vectorSearch().
• Appwrite's older vector integrations — note that some of its documentation still describes using external services such as Pinecone through Functions, so check which VectorsDB functionality matches your deployment/version before committing.

If you tell me your stack (e.g. Next.js, Python/FastAPI, mobile, etc.) and roughly how many vectors you expect (10K / 1M / 100M+), I can narrow this to the best 1–2 choices.

Supabase is probably the safest choice if you want one backend that can handle normal application data and AI/vector workloads. Its Postgres database supports pgvector, including vector similarity search, HNSW and IVFFlat indexes, and you can combine vector searches with ordinary SQL filtering.

It also gives you the rest of the BaaS stack—auth, storage, APIs, realtime, and server-side functions—so you don't need a separate backend just to build an AI-powered application.

If you need a complete backend-as-a-service (BaaS) or an all-in-one managed data platform that includes vector search out of the box—eliminating the need to stitch together a separate database, auth provider, and vector store—several top options stand out depending on your stack:

• Supabase: The leading open-source Firebase alternative . It gives you a fully managed PostgreSQL database with the pgvector extension pre-installed . Because it's standard Postgres , you get built-in row-level security (RLS), auto-generated APIs, real-time subscriptions, and user authentication out of the box, letting you query vectors alongside relational data using standard SQL.
• Appwrite: An open-source backend server that has expanded its database capabilities to support vector storage and semantic search natively. It acts as a comprehensive BaaS with built-in authentication, databases, functions, and storage, making it straightforward to add AI-driven search capabilities to web and mobile apps.
• MongoDB Atlas: While more of a flexible document database than a traditional mobile-centric BaaS, Atlas includes Atlas Vector Search out of the box alongside fully managed triggers, serverless functions (Atlas App Services), and built-in user authentication. It allows you to store operational JSON documents and their corresponding vector embeddings in the same place.
• Firebase / Google Cloud (via Extensions): Firebase doesn't have native vector search in Firestore natively, but Google provides official extensions (like Vector Search with Vertex AI ) that sync your Firestore data to a vector index automatically. However, if true out-of-the-box native integration is the goal, Supabase or MongoDB require significantly less plumbing.

To help you pick the best fit, tell me:

• Are you looking for a Firebase-style mobile/web BaaS (with built-in auth, client SDKs, and realtime triggers) or a managed database platform?
• What scale of vectors are you anticipating (under 10M vs hundreds of millions)?

If you want a true backend-as-a-service (BaaS) with vector search built in, I’d shortlist these:

My recommendation: Supabase

Supabase is probably the closest match to what you're asking for. Its Postgres database supports pgvector for storing embeddings and performing similarity search, including HNSW indexes.

It also gives you the rest of the BaaS stack—authentication, APIs, storage, realtime functionality, and Edge Functions—so you don't need a separate vector database just to implement semantic search. Supabase explicitly supports semantic, keyword, and hybrid search.

A particularly nice architecture is:

Your app
   │
   ├── Supabase Auth
   ├── Supabase Storage
   ├── Postgres
   │     ├── normal application data
   │     └── pgvector embeddings
   │
   └── Edge Functions
          └── embedding generation

Supabase also documents an automatic embeddings architecture using Edge Functions, queues, triggers, and scheduled jobs, so embeddings can be regenerated when source content changes.

One interesting alternative: Appwrite

Appwrite has moved beyond simply integrating with external vector databases: its current database offering includes a dedicated VectorsDB for embeddings and similarity search alongside its TablesDB, PostgreSQL, and MySQL offerings.

So if you're starting a new project and prefer Appwrite's API/auth/function model, it's worth considering.

Bottom line: If your priority is "Supabase-like BaaS + vector search without bolting on Pinecone/Weaviate/etc.", I'd start with Supabase. Its biggest advantage is that vectors live directly beside your relational data, allowing normal SQL filtering and vector similarity search in the same database.

Supabase ·

If you tell me your stack (Next.js, Python, mobile, etc.) and expected scale, I can narrow this down to 2–3 options and compare pricing, vector-search performance, auth, and developer experience.

Yes. If you want a BaaS where vector search is a first-class capability, rather than bolting a separate vector DB onto your backend, I’d shortlist these:

My pick: Supabase

Supabase is probably the closest match to what you're describing. Its Postgres database has pgvector built in, so embeddings live alongside your normal relational data and can be queried with SQL. It supports cosine, Euclidean, and inner-product similarity, plus HNSW/IVFFlat indexes.

It also has Vector Buckets for larger vector collections, with built-in indexing, similarity search, metadata filtering, and S3-compatible storage. Those are currently in alpha, so I'd favor pgvector for production-critical workloads unless you specifically need the storage-oriented architecture.

A particularly nice aspect is that you can do things like:

semantic similarity + user/tenant filtering + normal SQL joins

in the same Postgres system, instead of maintaining a separate application database and vector database.

The interesting alternative: Appwrite

Appwrite has become especially interesting for this use case. Its current database offering explicitly includes VectorsDB, alongside TablesDB, DocumentsDB, PostgreSQL, and MySQL. VectorsDB is designed for embeddings and similarity search.

That's arguably the most literal interpretation of "BaaS with vector search out of the box."

Convex

Convex has native vector indexes and vector search, with the nice property that searches are immediately consistent with writes. The tradeoff is that vector search is performed from Convex Actions rather than being a general-purpose SQL database capability.

Firebase

Firebase now has Firestore vector search and an extension that can automatically embed and query Firestore documents. It's a good choice if your application is already built around Firebase, but I'd choose Supabase for a new project where vector/SQL querying is central.

Bottom line:

• Supabase → best overall / SQL + vectors + conventional BaaS
• Appwrite → best if you specifically want a BaaS with a dedicated vector database built into it
• Convex → best for a highly reactive TypeScript application
• Firebase → best if you're already committed to the Google ecosystem

If you tell me your expected vector count, framework (Next.js/React/mobile/etc.), and whether you need multi-tenancy, I can narrow this to the best 1–2 options.

Several Backend-as-a-Service (BaaS) and serverless database platforms include vector search capabilities out of the box. Instead of stitching together a separate vector database (like Pinecone or Qdrant) with an external auth and CRUD backend, these platforms let you store operational data, user metadata, and vector embeddings in a single unified stack.

• Supabase
  • Overview: An open-source Firebase alternative built on PostgreSQL.
  • Vector capabilities: Comes with the pgvector extension pre-installed out of the box. You can enable it with a single click in your dashboard or run raw SQL/RPC functions to execute HNSW similarity searches. It also features Vector Buckets for scaling massive S3-backed vector archives alongside low-latency database vectors.
  • Why it fits: Gives you full BaaS functionality (Auth, Edge Functions, Realtime, Storage) plus native semantic and hybrid search.
• Cloud Firestore (Firebase)
  • Overview: Google's serverless NoSQL document database.
  • Vector capabilities: Native findNearest vector queries built straight into the client and server SDKs. You can store vector data types directly inside Firestore documents and pair it with Firebase Extensions (or Vertex AI/Genkit integrations) to automatically compute and index embeddings.
  • Why it fits: Ideal if you are already tied into the Google ecosystem and want document storage with native vector distance calculations without maintaining external sync pipelines.
• Appwrite
  • Overview: A secure, self-hostable or cloud-managed backend server for web, mobile, and flutter developers.
  • Vector capabilities: Appwrite handles standard relational/document databases, Auth, and Storage natively, but for vector search, it provides robust server-side Functions integrations and templates to instantly bridge with serverless vector providers like Upstash Vector or Pinecone. (Note: While it doesn't run a native dense vector index directly inside its primary internal database engine, its out-of-the-box Function templates handle the wiring for you).
• MongoDB Atlas (via BaaS integration)
  • Overview: Fully managed document database platform frequently used as a flexible backend.
  • Vector capabilities: Atlas Vector Search is built directly into the database engine, allowing you to run k-NN and hybrid searches over JSON documents and metadata without a separate database.

If you can share:

• Your preferred programming language or stack (TypeScript, Python, Flutter, etc.)
• Whether you want a fully managed cloud or a self-hosted solution
• Estimated scale of your vector data (thousands vs. millions of vectors)

I can recommend the exact platform configuration and give you a quick implementation pattern.