Supabase vs Convex vs Firebase: Backend for AI Apps in 2026

Building an AI product in 2026 is fundamentally different from building a traditional web app. Your backend is not just storing rows and serving JSON. It is orchestrating LLM calls, storing and querying high-dimensional vector embeddings, streaming tokens to clients in real time, managing conversation state across sessions, and doing all of this without melting your budget at scale.

Supabase, Convex, and Firebase each approach this problem from a different angle. Supabase gives you full PostgreSQL with pgvector, Edge Functions, and an open-source escape hatch. Convex gives you a reactive database with built-in vector search and server functions designed around real-time subscriptions. Firebase gives you Firestore with tight Vertex AI integration, Cloud Functions, and the Genkit framework for AI orchestration.

We have shipped AI products on all three platforms at Kanopy. Some of those decisions worked out perfectly. Others cost teams months of refactoring. This guide captures what we learned so you can skip the expensive mistakes.

If you have already compared two of these platforms, check out our deeper dives on Supabase vs Firebase and Convex vs Supabase vs Neon for real-time backends. This article focuses specifically on AI workloads: vector search, LLM integration, streaming, and cost at scale.

If your AI app does anything with semantic search, retrieval-augmented generation (RAG), or recommendation, you need a backend that can store and query vector embeddings efficiently. This is where the three platforms differ significantly.

Supabase: pgvector on Full PostgreSQL

Supabase runs real PostgreSQL, which means you get access to the pgvector extension. You can create columns with the vector data type, build HNSW or IVFFlat indexes, and run cosine similarity, inner product, or L2 distance queries using standard SQL. Your embeddings live alongside your relational data in the same database, in the same transaction boundary.

This matters for RAG applications. When a user asks a question, you can run a single query that joins your vector similarity results with metadata filters, permission checks, and user context. No separate API call to a vector database, no eventual consistency between your primary data and your embeddings. You write one SQL statement that handles the entire retrieval step.

The pgvector extension supports up to 2,000 dimensions per vector as of early 2026, which covers OpenAI's text-embedding-3-small (1536 dimensions) and most open-source models. For higher-dimensional models, you can use dimensionality reduction or the half-precision vector type to cut storage in half. Supabase also supports the pgvector-scale extension for diskann indexes, which handle larger datasets more efficiently than HNSW at the cost of slightly lower recall.

Convex: Built-In Vector Search

Convex ships native vector search as a first-class feature. You define a vector index on a table, insert documents with embedding fields, and query them using the vectorSearch API. No extension to install, no index tuning, no SQL. It is a single function call that returns the nearest neighbors.

Convex vector search supports up to 4,096 dimensions and uses an approximate nearest neighbor algorithm under the hood. You can combine vector search with filter expressions to scope results by metadata fields, though these filters happen after the ANN retrieval, not during it. For most RAG applications, this works fine. If you need complex pre-filtering on dozens of metadata columns, Supabase's SQL-based approach gives you more control.

The real advantage of Convex vector search is the developer experience. There is no separate vector database to manage, no sync pipeline between your primary data and your embeddings, and no index configuration to optimize. You define the index in your schema, and Convex handles the rest. For teams that want to ship fast and iterate, this removes a significant operational burden.

Firebase: No Native Vector Search (Yet)

Firestore does not have a native vector data type or vector search capability built into the database engine. If you need vector search on Firebase, you have two options: use a third-party vector database like Pinecone or Weaviate alongside Firestore, or use Vertex AI's Matching Engine through Google Cloud. Both approaches mean your embeddings live in a separate system from your primary data, with all the sync and consistency challenges that entails.

Firebase Genkit does provide abstractions for working with vector stores, and the Vertex AI integration makes it relatively easy to generate embeddings and run similarity queries. But you are stitching together multiple services rather than querying a single database. For simple AI apps that only need basic semantic search, this works. For complex RAG pipelines with multi-step retrieval and metadata filtering, the separation between Firestore and your vector store creates friction that compounds over time.

AI apps that call LLMs need to stream responses token by token. Users expect to see text appear incrementally, not wait five seconds for a complete response. How your backend handles this streaming pattern directly affects perceived performance and user experience.

Supabase: Realtime Channels and Edge Functions

Supabase offers two paths for streaming AI responses. The first is Realtime Broadcast, which lets your Edge Function push arbitrary payloads to subscribed clients over WebSocket channels. You call the LLM from an Edge Function (running on Deno), stream tokens as they arrive, and broadcast each chunk to the client. The second path is database-driven: write each token or chunk to a row in a table, and use Supabase Realtime's Postgres Changes feature to push updates to subscribed clients.

The broadcast approach is faster and more lightweight, since you skip the database write for each token. The database approach gives you a persistent record of the streaming process, which is useful for debugging and conversation history. Most teams we work with use broadcast for the live stream and write the final completed response to the database.

Supabase Edge Functions run on Deno Deploy, which means cold starts are typically under 100ms. For AI workloads, the main constraint is the 150-second execution time limit on the Pro plan. Long-running LLM calls that involve multi-step agent workflows can bump into this ceiling.

Convex: Reactive Queries and Actions

Convex's reactive architecture makes streaming AI responses straightforward. You write a Convex action (server function that can call external APIs), stream tokens from the LLM, and write intermediate results to the database using mutations. Because every Convex query is a live subscription, any client viewing that data automatically receives updates as the database changes. There is no separate WebSocket channel to set up.

The pattern looks like this: your action calls the LLM, accumulates tokens into chunks, and writes each chunk to the database via a mutation. The client has a reactive query on the conversation document, so it re-renders automatically as chunks arrive. Convex handles the subscription management, cache invalidation, and data delivery. You just write data and read data.

For long-running AI jobs (multi-step agents, chain-of-thought pipelines), Convex actions can run for up to 10 minutes on the Pro plan. Convex also supports action scheduling, so you can queue background AI processing jobs that run independently of the user's session.

Firebase: Cloud Functions and Firestore Listeners

Firebase's approach is similar to Supabase's database-driven pattern. You run your LLM call in a Cloud Function (Node.js or Python), write intermediate results to a Firestore document, and the client listens to that document with an onSnapshot listener. Firestore's real-time listeners push updates when the document changes.

The main consideration is Firestore's pricing model: every document write costs money, and every client that reads that document is charged for the read. If you are writing 50 token chunks per response and 10 clients are watching, that is 50 writes plus 500 reads per response. This adds up quickly. Firebase also offers callable functions with streaming support via the Genkit framework, which is more cost-efficient for one-to-one streaming but requires using the Firebase client SDK.

Beyond vector search and streaming, each platform has developed specific features aimed at AI workloads. Here is what you get out of the box.

Supabase AI Toolkit

Supabase provides several AI-focused capabilities. Edge Functions can call any LLM provider (OpenAI, Anthropic, Google, open-source models via Ollama) with no vendor lock-in. The pg_embedding and pgvector extensions handle embedding storage. Supabase also offers an AI SQL editor that generates queries from natural language, though this is a developer tool rather than a runtime feature.

For embedding generation, you can run models directly in Edge Functions using the Transformers.js library, or call external APIs. Supabase's approach is "bring your own model": the platform provides the infrastructure, and you choose the AI providers. This flexibility is valuable if you want to switch between GPT-4o, Claude, and open-source models without changing your backend architecture.

Row-level security (RLS) in Supabase is particularly useful for AI apps that handle sensitive data. You can enforce per-user access controls on embedding tables, ensuring that a RAG query only retrieves documents the requesting user is authorized to see. This is database-level security, not application-level, which means a bug in your application code cannot accidentally leak embeddings across users.

Convex AI Features

Convex has invested heavily in AI developer experience. The platform includes built-in vector search (as discussed above), action scheduling for async AI jobs, and a component system that lets you install pre-built AI modules. The Convex AI chat component, for example, gives you a complete conversational AI backend with message history, streaming, and vector-powered context retrieval in under 50 lines of code.

Convex actions are designed for the kind of multi-step workflows AI apps require. You can call an LLM, process the response, store results, trigger follow-up actions, and schedule retries on failure. All of this runs in server-side TypeScript with automatic error handling and logging. The platform also supports HTTP actions for building webhook endpoints that external AI services can call.

One unique Convex feature for AI apps is its caching behavior. Because Convex tracks which data each query reads, it can cache query results and only invalidate them when relevant data changes. For AI apps that repeatedly query the same context window or user profile, this automatic caching reduces redundant database reads without any manual cache management.

Firebase Genkit and Vertex AI

Firebase's AI story centers on two products: Genkit and Vertex AI integration. Genkit is an open-source framework for building AI features in Node.js or Go. It provides abstractions for prompt management, model calling, retrieval, indexing, and evaluation. You define "flows" that chain together AI operations, and Genkit handles the orchestration, tracing, and error handling.

The Vertex AI integration gives Firebase apps direct access to Google's Gemini models, plus embedding generation, image analysis, and other Google Cloud AI services. If you are already in the Google Cloud ecosystem, this integration is seamless. You call Gemini from a Cloud Function with a few lines of code, and billing goes through your existing Google Cloud account.

Firebase also introduced Extensions for AI, including a pre-built "chatbot" extension that handles conversation management with Firestore, and a "multimodal tasks" extension for processing images and documents. These extensions reduce boilerplate, but they lock you into Google's AI models and Firestore's data patterns. If you want to use Anthropic's Claude or an open-source model, you are back to writing custom Cloud Functions, and Genkit's provider abstraction helps but does not eliminate the switching cost entirely.

AI apps are expensive to run. LLM API costs dominate your bill at scale, but your backend costs can spiral if you pick a platform with a pricing model that punishes AI workloads. Here is how each platform's pricing plays out at three scale milestones.

10,000 Monthly Active Users

At 10K MAU, all three platforms are affordable. Supabase Pro costs $25/month and includes 8GB database storage, 250MB of Edge Function invocations, and 5 million Realtime messages. Convex's Pro plan is $25/month with generous compute and storage limits that comfortably handle this scale. Firebase's Blaze plan is pay-as-you-go: expect roughly $20 to $50/month depending on your read/write patterns and Cloud Function invocations.

At this stage, the pricing differences are negligible. Pick the platform with the best developer experience for your team. Do not optimize for cost at 10K users.

100,000 Monthly Active Users

At 100K MAU, pricing models start to diverge. The critical variable for AI apps is how many reads and writes each user interaction generates.

Supabase charges based on compute (database size and CPU), bandwidth, and Edge Function invocations. A typical AI app at 100K MAU runs $100 to $300/month on Supabase, depending on database size and function usage. The per-row pricing for database storage is predictable, and pgvector queries consume standard database compute.

Convex pricing at 100K MAU depends on document bandwidth and function compute time. A chatbot-style AI app that stores conversation history and runs vector searches typically lands at $150 to $400/month. Convex's reactive queries add some overhead because every subscription triggers re-computation when data changes, but the automatic caching offsets this for read-heavy workloads.

Firebase at 100K MAU is where you need to watch closely. Firestore charges per document read ($0.06 per 100K reads) and per write ($0.18 per 100K writes). An AI chatbot that writes 20 token chunks per response and has 5 listeners per conversation generates a lot of billable operations. Teams we have worked with see Firebase costs of $200 to $800/month at this scale, with the variance driven almost entirely by how aggressively they stream tokens through Firestore documents.

1,000,000 Monthly Active Users

At 1M MAU, Supabase and Convex both offer enterprise pricing with volume discounts. Supabase's compute-based model scales more predictably than Firebase's per-operation model. A large-scale AI app on Supabase might run $800 to $2,000/month for the backend (excluding LLM API costs). Convex at this scale is typically $1,000 to $3,000/month, though teams with heavy real-time usage should benchmark carefully.

Firebase at 1M MAU can become genuinely expensive for AI workloads. The per-read and per-write charges compound with streaming patterns. Teams that do not carefully optimize their Firestore usage patterns (batching writes, limiting listeners, using server-side aggregation) can see backend costs of $3,000 to $10,000/month. The saving grace is that Firebase's generous free tier covers ancillary services like Authentication and Hosting, so the total bill is not entirely Firestore.

The bottom line: Supabase's compute-based pricing is the most AI-friendly at scale. Convex is competitive if you stay within its compute and bandwidth tiers. Firebase's per-operation model creates the highest risk of cost surprises for AI workloads that generate many reads and writes per user interaction.

Developer experience determines how fast your team can ship, iterate, and debug. For AI apps that are constantly evolving, DX is not a luxury. It is a competitive advantage.

Supabase DX

Supabase's dashboard is one of the best in the BaaS space. You get a table editor, SQL editor, a visual schema builder, API documentation auto-generated from your schema, and real-time logs for Edge Functions. The local development experience uses Docker to run a full Supabase stack, including PostgreSQL, GoTrue (auth), and the Realtime server. You can write and test Edge Functions locally before deploying.

The TypeScript SDK is solid but can feel verbose for complex queries compared to Convex's. You are writing SQL or using the PostgREST query builder, which is powerful but requires you to think in relational terms. For developers coming from NoSQL backgrounds, the learning curve is real.

Supabase is fully open source. You can self-host the entire stack on your own infrastructure using Docker Compose, Kubernetes, or any container orchestration platform. This is a genuine escape hatch, not a theoretical one. We have helped clients migrate from Supabase Cloud to self-hosted Supabase on AWS without changing application code. If you are building an AI app that handles sensitive data (healthcare, legal, finance), self-hosting gives you complete control over where your data and embeddings live.

Convex DX

Convex has arguably the best developer experience of the three platforms. The TypeScript-first approach means your schema, queries, mutations, and actions are all written in TypeScript with full type safety. The Convex CLI generates types from your schema, so your client code gets autocomplete and compile-time checks for every database operation.

The Convex dashboard provides a data browser, function logs, deployment management, and a query playground. The local development experience uses the Convex dev server, which hot-reloads your server functions as you edit them. This tight feedback loop is excellent for iterating on AI features.

The tradeoff is portability. Convex is a fully hosted platform with no self-hosting option. Your data lives on Convex's infrastructure, and your server functions run on Convex's compute. If you decide to leave Convex, you are rewriting your entire backend, not just migrating data. For some teams, this is an acceptable tradeoff for the DX and productivity gains. For others, it is a dealbreaker. Read our deeper comparison in AI-native architecture for products for more on this tradeoff.

Firebase DX

Firebase's developer experience is mature and well-documented. The Firebase CLI handles project setup, emulator configuration, deployment, and function management. The local emulator suite lets you run Firestore, Auth, Functions, and Hosting locally, which is useful for testing AI pipelines without incurring cloud costs.

The Firebase console provides a Firestore data viewer, authentication management, function logs, and performance monitoring. The SDKs for web, iOS, and Android are polished and battle-tested. If your AI app targets mobile platforms, Firebase's mobile SDKs are significantly more mature than Supabase's or Convex's.

Firebase is partially open source. The client SDKs and some server components are open source, but Firestore, Cloud Functions, and the core infrastructure are proprietary Google Cloud services. You cannot self-host Firebase in any meaningful sense. If you leave Firebase, you are migrating data out of Firestore's document model and rewriting your server-side logic.

Here is a direct comparison of the features that matter most for AI applications.

Database and Vector Search

• Supabase: PostgreSQL with pgvector. SQL-based vector queries with metadata filtering in the same query. HNSW and IVFFlat indexing. Up to 2,000 dimensions (higher with half-precision).
• Convex: Custom reactive database with native vector search. Up to 4,096 dimensions. Filter expressions applied post-retrieval. No SQL required.
• Firebase: Firestore (document database). No native vector search. Requires external vector database or Vertex AI Matching Engine.

Real-Time Capabilities

• Supabase: Postgres Changes (row-level subscriptions), Broadcast (arbitrary messages), Presence (user status tracking). WebSocket-based.
• Convex: Every query is a live subscription by default. Automatic cache invalidation and re-computation. WebSocket-based.
• Firebase: Firestore onSnapshot listeners (document and collection level). Realtime Database for low-latency use cases. WebSocket-based.

Serverless Functions

• Supabase: Edge Functions (Deno). Sub-100ms cold starts. 150-second max execution on Pro. Global edge deployment.
• Convex: Queries, Mutations, Actions (TypeScript). Actions support up to 10 minutes execution. Automatic retries and scheduling.
• Firebase: Cloud Functions (Node.js, Python). Cold starts of 200ms to 2 seconds. 9-minute max execution on second-gen functions. Regional deployment.

AI Framework Integration

• Supabase: Bring your own model. Call any LLM from Edge Functions. pgvector for embeddings. No built-in AI orchestration framework.
• Convex: Built-in vector search. AI chat component. Action scheduling for background AI jobs. Model-agnostic.
• Firebase: Genkit framework for AI orchestration. Native Vertex AI and Gemini integration. Pre-built AI extensions. Strongest with Google AI models.

Self-Hosting and Portability

• Supabase: Fully open source. Self-hosting supported and documented. Standard PostgreSQL data export.
• Convex: Hosted only. No self-hosting option. Data export available but requires backend rewrite to migrate.
• Firebase: Proprietary. No self-hosting. Data export from Firestore available but requires schema translation for other databases.

After building AI products on all three platforms, here are our recommendations based on specific use cases.

Choose Supabase If:

• You are building a RAG application that needs sophisticated vector search with complex metadata filtering. pgvector in PostgreSQL gives you the most flexible query capabilities of the three platforms.
• You want the option to self-host. If you are in a regulated industry or need data residency controls, Supabase's open-source stack lets you run everything on your own infrastructure.
• You want model flexibility. Supabase does not push you toward any specific AI provider. You can switch between OpenAI, Anthropic, Google, and open-source models without changing your backend.
• You need predictable pricing at scale. Supabase's compute-based pricing does not penalize you for AI workloads that generate many reads and writes.

Choose Convex If:

• Real-time collaboration is central to your AI app. Convex's reactive database makes every query a live subscription, which is ideal for collaborative AI tools, multiplayer features, and live dashboards.
• You prioritize developer velocity. Convex's TypeScript-first DX, automatic type generation, and built-in AI components let small teams ship AI features significantly faster than on Supabase or Firebase.
• You need background job scheduling. Convex's action scheduling is purpose-built for async AI workflows like batch embedding generation, scheduled model retraining, and multi-step agent pipelines.
• You do not need self-hosting. Convex is hosted-only, and that is the tradeoff for its developer experience advantages.

Choose Firebase If:

• Your AI app is mobile-first. Firebase's iOS and Android SDKs are the most mature in this comparison, with offline support, push notifications, and deep OS integration.
• You are already in the Google Cloud ecosystem. If your team uses Google Cloud, Vertex AI, and Gemini, Firebase provides the tightest integration with minimal configuration.
• You are building a simpler AI feature (chatbot, content generation, summarization) that does not require complex vector search or real-time streaming to multiple clients simultaneously.
• You need mature authentication and analytics. Firebase Auth and Google Analytics integration are battle-tested at massive scale.

The Honest Take

For most AI-native web applications in 2026, Supabase is the safest bet. PostgreSQL with pgvector gives you the most capable foundation for AI workloads, the open-source license eliminates vendor lock-in risk, and the pricing model does not penalize AI patterns. If developer velocity matters more than portability, Convex is excellent. If mobile is your primary platform and you live in Google Cloud, Firebase still makes sense.

The worst decision is the one you make without prototyping. Spend a day building a minimal AI feature on two of these platforms before committing. The right choice depends on your team's skills, your AI architecture, and your growth trajectory.

Not sure which backend fits your AI product? Book a free strategy call and we will help you evaluate the options against your specific requirements, data model, and scale targets.