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SaaS Platforms

Multi-tenant applications

AI Applications

Vector search & RAG

Mobile & PWAs

Edge performance

E-commerce

Product catalogs

Analytics

Business intelligence

Agency Projects

Client applications

SaaS Applications

Multi-Tenant SaaS Made Simple

Perfect database-per-tenant architecture without complexity

Why Database-Per-Tenant with QryBit?

  • Complete customer data isolation
  • Simplified data export per customer
  • No complex permission systems needed
  • Scale from 1 to 10,000+ customers
  • Unlimited databases within storage limit
  • Predictable costs regardless of tenant count

Case Study: ProjectFlow SaaS

Started with 10 customers on Byte tier (10GB)
Grew to 500 customers still within Mega tier (25GB)
Average database size: 45MB per customer
Zero architecture changes during scaling

Example SaaS Applications

• Customer relationship management (CRM)
• Project management tools
• E-commerce platforms
• Content management systems
• Business intelligence dashboards
• Team collaboration tools

Technical Architecture

class TenantManager:
    def create_tenant(self, tenant_id):
        # Create dedicated database for tenant
        database = qrybit.create_database(f"tenant_{tenant_id}")
        # Initialize schema
        database.execute_schema(self.base_schema)
        return database

    def get_tenant_database(self, tenant_id):
        return qrybit.get_database(f"tenant_{tenant_id}")

    def migrate_tenant(self, tenant_id, migration):
        # Apply migration to specific tenant
        db = self.get_tenant_database(tenant_id)
        db.execute(migration)

"Create one database per customer, scale infinitely within your storage tier. When you need more space, upgrade your tier, not your architecture."

QryBit Success Pattern
AI Applications

AI Applications with Vector Storage

Combine structured data with semantic search capabilities

Why Vector Storage + SQLite?

  • Built-in vector storage columns
  • Similarity search with SQL
  • Perfect for RAG applications
  • Semantic search capabilities
  • No separate vector database needed
  • Combine structured and unstructured data

AI Use Case Examples

Document Search Systems

Semantic search across knowledge bases

Recommendation Engines

Product and content recommendations

Chatbots & Q&A

AI assistants with knowledge retrieval

Vector Implementation

-- Create table with vector column
CREATE TABLE documents (
  id INTEGER PRIMARY KEY,
  title TEXT,
  content TEXT,
  category TEXT,
  created_at DATETIME,
  embedding VECTOR(1536)
);

-- Semantic search with filters
SELECT title, content FROM documents
WHERE category = 'technical'
  AND created_at > date('now', '-30 days')
  AND vector_distance(embedding, ?) < 0.5
ORDER BY vector_distance(embedding, ?)
LIMIT 10;

AI Application Patterns

RAG (Retrieval-Augmented Generation)
  1. 1 Store documents with vector embeddings
  2. 2 Query similar documents for context
  3. 3 Combine with structured metadata
  4. 4 Generate responses with full context

Proven Architecture Patterns

Battle-tested approaches for different application types

Single Database Growth

1
Best For: Simple applications, small teams
Pattern: Start with one database, optimize schema
Scaling: Vertical scaling within storage limits
Example: Personal blog → Content management system

Database-Per-Tenant

2
Best For: SaaS applications, multi-tenant systems
Pattern: One database per customer/tenant
Scaling: Horizontal scaling by tenant count
Example: CRM with 100 customers = 100 databases

Regional Distribution

3
Best For: Distributed applications, compliance requirements
Pattern: Databases by geographic region
Scaling: Regional data locality and performance
Example: E-commerce with US, EU, Asia databases

Microservice Databases

4
Best For: Microservice architectures, team separation
Pattern: Database per service or team
Scaling: Service-level database isolation
Example: User service, Order service, Inventory service

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