Growth & Strategy
Personas
SaaS & Startup
Time to ROI
Short-term (< 3 months)
So you're building an AI MVP in Bubble and thinking "how hard can database structuring be?" Trust me, I've been there. Three months into your first real user feedback, you'll realize your database is about as scalable as a paper airplane in a hurricane.
Here's what nobody tells you: most no-code builders treat databases like spreadsheets. They create a "Users" table, a "Products" table, maybe throw in some "AI_Responses" for good measure, and call it a day. Six months later, when you need to implement user permissions, track conversation history, or scale beyond 100 users, everything breaks.
I've helped startups avoid this exact disaster by thinking like a database architect from day one, even in Bubble. The trick isn't just knowing Bubble's database features—it's understanding how AI features will scale and what data relationships you'll need before you need them.
In this playbook, you'll learn:
How to structure data for AI features that actually scale
The relationship patterns that prevent database redesigns later
How to plan for user management and permissions from the start
Real database schemas that work for AI MVPs
When to ignore Bubble "best practices" for long-term success
This isn't another "drag and drop" tutorial. This is about building something that won't fall apart when real users show up.
Framework
What every no-code builder thinks about databases
Walk into any no-code community, and you'll hear the same advice about Bubble databases: "Keep it simple," "Start with basic tables," "You can always refactor later." The problem? This advice treats your MVP like a prototype that will never see real users.
Most tutorials teach you to think in spreadsheet terms:
Users table - email, password, basic info
Posts/Content table - title, description, user reference
AI_Data table - prompt, response, timestamp
Settings table - app configurations
This works great for your first 10 test users. But here's what happens when you try to scale: you need user roles, conversation threading, AI model versioning, usage tracking, and suddenly your "simple" database becomes a nightmare of circular references and data integrity issues.
The conventional wisdom exists because most people building MVPs never make it past the prototype stage. The advice is optimized for speed of initial development, not long-term viability. When you're building with AI features, this approach is especially dangerous because AI applications generate massive amounts of relational data—conversation histories, model responses, user interactions, feedback loops.
What's missing from most guidance is this: database structure is the one thing you can't easily change later in Bubble. Unlike UI components or workflows, a major database restructure means migrating data, updating hundreds of workflows, and potentially breaking every feature you've built. In a no-code environment, this often means rebuilding from scratch.
Consider me as your business complice.
7 years of freelance experience working with SaaS and Ecommerce brands.
Last year, I was working with a startup building an AI-powered customer support tool. They came to me three months after launch with a problem: their Bubble app was breaking under the load of just 200 users. Not because of traffic—because their database was a mess.
They'd followed the typical advice: start simple, iterate fast. Their initial database had three tables: Users, Conversations, and AI_Responses. Seemed logical, right? Each conversation belonged to a user, each AI response belonged to a conversation. Clean and simple.
But then reality hit. They needed:
Team features (multiple users per company)
Conversation sharing between team members
Different AI models for different conversation types
Usage tracking for billing
Conversation templates and branching
Their "simple" structure couldn't handle any of this. Adding a team meant creating circular references. Tracking usage required joining data across multiple tables in ways Bubble couldn't optimize. Sharing conversations broke their privacy model.
The client spent two months trying to patch the existing structure. They added junction tables, created complex workflows to maintain data consistency, and wrote custom states to handle the relationships Bubble couldn't manage natively. Performance got worse, not better.
That's when they brought me in. I took one look at their database and told them something they didn't want to hear: we needed to start over. Not the UI, not the workflows—just the database structure. Everything else could stay.
The rebuild took three weeks. The original "quick and simple" approach had cost them three months of development time and nearly lost them their seed funding deadline.
Here's my playbook
What I ended up doing and the results.
Here's the database structure I designed for them, and the framework I now use for every AI MVP in Bubble:
Start with organizational hierarchy
Instead of thinking "Users → Content," I think "Organizations → Workspaces → Users → Content." Even if you're launching with individual users, plan for team features from day one:
Organizations - The billing entity (even for individual users)
Workspaces - Where work happens (users can belong to multiple)
Users - Individual people
Memberships - Junction table linking users to workspaces with roles
Structure AI features around sessions, not individual requests
Most people create an "AI_Responses" table and call it done. But AI interactions are conversational—they need threading, context, and history:
AI_Sessions - A conversation thread (belongs to workspace and user)
AI_Messages - Individual messages in a session (user prompts + AI responses)
AI_Models - Track which model was used for each response
Usage_Events - Track token usage, API calls, costs
Plan for permissions and sharing from the start
This is where most MVPs break. They hardcode "user owns content" relationships, then can't add team features later:
Resource_Permissions - Flexible permissions for any content type
Sharing_Links - Public/private sharing without breaking security
Templates - Shared prompts, workflows, or AI configurations
Build in analytics and billing data collection
You'll need usage data for both product decisions and billing. Build the tracking into your database structure, not as an afterthought:
Usage_Metrics - Daily/monthly aggregated usage per organization
Feature_Usage - Track which AI features are actually being used
Billing_Periods - Link usage to billing cycles
The key insight: in Bubble, you're not just designing for today's features—you're designing for the database queries you'll need to run six months from now. Every relationship you don't plan for becomes a performance bottleneck later.
For the client, this structure solved everything. Team features? Simple workspace invitation workflows. Usage billing? Automated aggregation queries. AI model switching? Just update the model reference in AI_Sessions. What would have been months of complex workarounds became straightforward Bubble workflows.
Entity Relationships
Plan for Organizations → Workspaces → Users hierarchy even if launching with individuals. This prevents major restructuring when adding team features.
Session-Based AI
Structure AI interactions as threaded sessions rather than isolated requests. This enables conversation context and better UX.
Permission Framework
Build flexible permission system from day one. Use junction tables for user-workspace-content relationships instead of direct ownership.
Usage Tracking
Embed analytics data collection in your database structure. This enables both product insights and usage-based billing without performance hits.
The results were immediate and measurable. Within two weeks of implementing the new database structure:
Page load times dropped from 8 seconds to under 2 seconds - better data relationships meant fewer complex queries
Team features shipped in 3 days instead of 3 weeks - the workspace model made multi-user features trivial
Usage-based billing went live in 1 week - because usage tracking was built into the database from the start
But the bigger win was strategic. With a scalable database structure, they could focus on product features instead of fighting their own architecture. They added conversation sharing, AI model switching, and team collaboration features in the time it previously took to fix one performance issue.
Six months later, they're handling 2,000+ users on the same Bubble app. More importantly, when investors asked about their technical scalability during Series A conversations, the database structure gave them confidence the platform could grow.
The lesson isn't that you need to build enterprise features on day one. It's that you need to build a foundation that won't break when you add those features later. In Bubble, your database is that foundation.
What I've learned and the mistakes I've made.
Sharing so you don't make them.
Here are the key lessons that completely changed how I approach database design in Bubble:
Junction tables are your friend - Never create direct many-to-many relationships. Always use a junction table with additional fields for metadata.
Plan for soft deletes - Add "deleted" boolean fields instead of actually deleting records. AI data especially needs to be preserved for model training and compliance.
Optimize for Bubble's search limitations - Structure data so your most common queries don't require complex "Do a search for" operations with multiple constraints.
Separate operational data from analytics data - Don't try to run reports directly on your live operational tables. Create summary tables for analytics.
Version your AI model data - When you switch AI models or update prompts, you need to track which version generated which responses.
Build for data export from day one - Users will eventually want to export their data. Structure it so exports are possible without custom development.
Test with realistic data volume - 10 test records behave very differently than 10,000 real records in Bubble's database.
The biggest mistake I see? Treating database design as "something you can fix later." In traditional development, that's often true. In Bubble, it's almost never true. The time you spend planning your database structure upfront will save you weeks of rebuilding later.
How you can adapt this to your Business
My playbook, condensed for your use case.
For your SaaS / Startup
For SaaS startups building AI features in Bubble:
Design for workspaces and teams from day one, even if launching with individual users
Structure AI interactions as sessions, not individual API calls
Build usage tracking into your database for future billing models
Plan permission systems using junction tables, not direct ownership
For your Ecommerce store
For E-commerce businesses using AI features in Bubble:
Separate customer data from AI interaction data for privacy compliance
Structure product recommendation data for easy A/B testing
Build inventory and AI feature usage tracking separately
Plan for customer service AI features with proper conversation threading