The question isn't 'Can Bubble build AI apps?' but 'Should Bubble build your AI app?' According to Bubble's co-CEO Josh Haas, AI-generated code produces 'millions of lines that isn't production-ready, isn't hardened, has bugs and is going to be an unmaintainable nightmare.' But Bubble's no-code approach has its own limitations. Most Bubble AI projects succeed or fail based on understanding what modern AI development actually requires and whether Bubble's architecture can deliver it.
Standard AI API integrations with simple workflows
Bubble shines when integrating mainstream AI APIs like OpenAI's GPT models, Claude, or other REST-based AI services. As documented in UserLoop's implementation, teams can integrate OpenAI successfully by using Bubble's API Connector with proper prompt formatting. If your AI application involves straightforward request-response patterns sending a prompt, receiving a completion, displaying results, Bubble handles this elegantly.
Think AI-powered content generators, chatbots with non-streaming responses, document summarization tools, or automated email writers. The sweet spot is applications where AI processing happens in discrete chunks with 5-30 second response times that users will tolerate.
Rapid prototyping and MVP development
Companies like Byword launched an SEO content platform producing 100k+ articles in just 4 weeks using Bubble, while Synthflow went from MVP to funding in six months. For founders validating AI product concepts, Bubble's speed is unmatched. You can test market demand, iterate on AI prompts, and refine user workflows without building full infrastructure.
The visual editor lets non-technical founders participate directly. As one Bubble user noted: "It's not just a website builder. Bubble is a platform with a backend and a database that has everything."
Backend-centric AI applications with straightforward frontends
When your AI complexity lives in business logic, data processing, and workflow orchestration, not in real-time UI interactions, Bubble works well. Internal AI tools, admin dashboards with AI analysis, automated reporting systems, or AI-assisted data entry applications all fit this pattern.
Bubble's Josh Haas explained: "Each line of code is a maintenance burden. Each line of code can have a bug in it. When you can express things at this more human-friendly level, I think you should." For teams without deep technical resources, this approach makes AI accessible.
AI applications with stable, predictable data models
If your AI application involves well-understood entities, customers, documents, conversations, analysis results, Bubble's PostgreSQL-backed database handles it efficiently. As one developer documented, Bubble's database can manage standard AI data structures including conversation histories, embeddings metadata, and generated content
When You Should Look Elsewhere:
Real-time AI streaming and WebSocket-based features
This is Bubble's most significant AI limitation. Modern AI applications expect ChatGPT-style streaming responses where text appears word-by-word in real-time. As a Bubble forum user noted when trying to integrate real-time AI transcription: "Since Bubble doesn't support WebSockets, I'm exploring other solutions" Bubble.
While plugins exist for streaming (like the ChatGPT Assistant plugin), they require complex workarounds. The OpenAI Assistant API integration requires continuous polling" repeatedly checking the API every second to see if the run status has changed to 'completed'" which significantly increases workload unit consumption Planet No Code.
If your AI product is the conversation interface, think ChatGPT clones, real-time voice AI, live transcription apps—Bubble's architecture fights you at every step. As one tutorial explains, implementing streaming eliminates "30-50 second wait times" but requires specialized plugins and advanced workflow techniques that go beyond Bubble's native capabilities Planet No Code.
Advanced RAG (Retrieval-Augmented Generation) architectures
The AI landscape has evolved dramatically. Modern AI applications increasingly rely on RAG, combining large language models with specialized knowledge retrieval. This requires:
- Vector databases (Pinecone, Weaviate, Qdrant, Milvus) for semantic search
- Embedding generation pipelines converting documents to vector representations
- Hybrid search capabilities combining vector similarity with metadata filtering
- Chunking strategies breaking documents into optimal retrieval units
Bubble doesn't natively support any of this. While Bubble's database processes approximately 100 rows per second, competitors handle thousands of rows per second—a critical limitation for RAG applications managing large knowledge bases Momen.
You can integrate external vector databases via API calls, but you're essentially building a parallel data infrastructure outside Bubble while using Bubble just as a frontend wrapper and workflow coordinator. At that point, you're working against the platform rather than with it.
Complex AI agent workflows and multi-step reasoning
Modern AI applications increasingly involve agents that:
- Execute multi-step reasoning chains
- Make autonomous decisions about tool usage
- Maintain complex state across extended interactions
- Orchestrate multiple AI models simultaneously
As Planet No Code explains: "The OpenAI Assistant API is still in beta, which introduces additional considerations for production applications... the API's behavior and available features may evolve" Planet No Code. Building sophisticated agent frameworks (like LangChain or LlamaIndex implementations) requires orchestration capabilities that Bubble's workflow engine isn't optimized for.
When your AI logic involves branching decisions, recursive loops, or dynamic tool selection, you'll spend more time fighting Bubble's visual programming constraints than actually building AI features.
High-performance, data-intensive AI processing
Bubble's database processing speed is capped at approximately 100 rows per second. The platform relies on JSONB storage which lacks the robustness of traditional relational databases and doesn't comply with ACID principles—critical for ensuring reliable database transactions Momen.
If your AI application processes thousands of documents, analyzes large datasets, or requires sub-second query responses for AI-augmented search, Bubble creates bottlenecks. We can safely say Bubble apps can scale comfortably to 100k daily users. The complexity of your app, the efficiency of your workflows, the optimization of your database structure, and how well you manage resources all influence its scalability.
But "comfortably to 100k users" with standard workflows is very different from handling AI-intensive operations at scale.
Applications requiring code ownership and portability
As one Medium analysis notes: "Bubble.io's official communications and strategic direction do not prioritize code export. The company's mission focuses on 'erasing the distinction between software use and software creation' by abstracting away code, not necessarily by providing it as an output" Medium.
If you need to:
- Export your codebase for investor due diligence
- Migrate to custom infrastructure as you scale
- Have a technical exit strategy beyond Bubble
- Integrate with enterprise systems requiring source code access
Bubble simply doesn't support this. Unlike platforms like WeWeb (Vue.js), Draftbit (React Native), or FlutterFlow (Flutter/Dart) that export code, Bubble enforces complete vendor lock-in Momen.
The Hybrid Architecture Approach
Making Bubble Work in Modern AI Development:
The most successful production Bubble AI applications use hybrid architectures that play to Bubble's strengths while routing around its limitations.
Bubble as orchestration layer + specialized AI services
Use Bubble to manage:
- User authentication and permissions
- Basic data storage and CRUD operations
- Workflow coordination and business logic
- UI presentation for non-real-time features
Route AI-intensive operations to:
- Supabase or PostgreSQL for vector storage and complex queries
- Pinecone/Weaviate for semantic search and RAG
- External compute (AWS Lambda, Cloud Run) for heavy processing
- Dedicated streaming services for real-time AI responses
As KnowCode Tech explains: "This isn't about replacing Bubble—it's about complementing it. No-code platforms like Bubble are incredibly powerful, but they have limitations. An external database helps you work around those limitations strategically" Knowcode.
Plugin-based extensibility for AI capabilities
Bubble's plugin marketplace includes OpenAI/GPT plugins, image processing tools, voice recognition, and various AI integrations. The API connector enables unlimited third-party connections including Stripe, Twilio, SendGrid, and OpenAI Sommo.
For AI-specific needs:
- Use dedicated streaming plugins for real-time responses
- Integrate vector database services via REST APIs
- Connect to specialized AI services (ElevenLabs for voice, Stable Diffusion for images)
- Build custom plugins for unique AI workflows
Clear architectural boundaries
Define what lives in Bubble versus external services:
In Bubble:
- User-facing UI and basic interactions
- Simple data models and user records
- Workflow triggers and coordination
- API call orchestration
External:
- Vector embeddings and semantic search
- Real-time streaming connections
- Heavy compute operations
- Complex AI orchestration logic
✓ Is AI a feature or the product? If AI is a supporting feature (automated summaries, content suggestions), Bubble works well. If AI is the product (ChatGPT competitor, AI agent platform), you'll fight Bubble constantly.
✓ Can you tolerate non-streaming AI responses? If 10-30 second response times are acceptable, Bubble is fine. If you need real-time streaming text, prepare for complex workarounds.
✓ Is your AI workflow request-response or stateful/streaming? Simple API calls to OpenAI work great. Complex agent frameworks with persistent state and tool-use don't.
✓ Do you need RAG or semantic search? If no, Bubble's fine. If yes, plan for external vector databases and accept the integration complexity.
✓ What's your data processing volume? Under 100 rows/second with straightforward queries? Bubble works. Higher throughput or complex analytical queries? You'll hit performance walls.
✓ Are you comfortable with complete vendor lock-in? Bubble offers no code export. Your AI application lives on Bubble forever or gets rebuilt from scratch.
✓ Do you have 6-12 months for the Bubble learning curve? Despite being "no-code," Bubble requires 1-3 months for basics and 6-12 months for proficiency, more complex than alternatives like Glide, Softr, or Adalo..
If you answered yes to most of these, Bubble can accelerate your AI development significantly. If you're hesitating on several, consider whether traditional development or alternative platforms might be faster long-term.
The Modern AI Development Context
What's Changed in 2024-2025:
The AI development landscape has evolved rapidly, and Bubble's roadmap reflects this reality.
In June 2025, Bubble announced native mobile app development combining "AI's rapid prototyping capabilities with 'productionized building blocks'—secure, scalable components that have been battle-tested across the platform's millions of existing applications" The New Stack.
According to Bubble's 2024 State of No-Code report, 64.29% of no-code users believe most developers will primarily use no-code solutions by 2030 Bubble. The platform has grown to 7 million applications with $1+ billion transacted in 2024.
But the gap between no-code platforms and cutting-edge AI development is widening:
- Vector databases are now standard for production AI (market growing from $1.73B in 2024 to projected $10.6B by 2032)
- RAG architectures require specialized infrastructure Bubble doesn't provide
- AI agent frameworks (LangChain, LlamaIndex) assume full-code environments
- Real-time streaming is expected, not optional
- Hybrid search (combining semantic and keyword search) is table stakes
As Haas notes: "What you're going to get is millions of lines of AI-written code that isn't production-ready, isn't hardened, has bugs and is going to be an unmaintainable nightmare" The New Stack. But Bubble's alternative, complete abstraction and vendor lock-in—creates its own set of constraints for AI development.
The future likely involves platforms that combine no-code speed with code export capabilities and native support for modern AI infrastructure. Until then, technical leaders must make pragmatic choices about when Bubble's tradeoffs make sense for their specific AI applications.
