Why DB Design and ERD Have a Major Impact on Your Quote
An explanation of how database design (ERD) affects outsourced software development quotes, timelines, and quality, along with key DB design concepts that clients should understand.
- •DB design is like a building's foundation — if done poorly, the entire structure will need to be torn apart later.
- •The more tables and the more complex the relationships, the higher the development cost (2-5 APIs per table).
- •Always review the ERD during the design phase and verify that the structure allows for future scalability.
Why DB Design Affects Your Quote
The database is the foundational structure of your software. Every feature stores and retrieves data, so the complexity of the DB design directly determines development cost.
Relationship Between Table Count and Cost:
| Table Count | Project Scale | Est. API Count | Cost Range |
|---|---|---|---|
| 5-10 | Small MVP | 15-30 | 5M-15M KRW |
| 10-25 | Mid-Size Service | 30-75 | 15M-40M KRW |
| 25-50 | Large-Scale Service | 75-150 | 40M-100M KRW |
| 50+ | Enterprise | 150+ | 100M+ KRW |
Why is the impact so significant?
1. Each table requires 2-5 CRUD APIs
2. Complex table relationships (JOINs) increase query development effort
3. Data validation, indexing, and migration require additional effort
4. Each table needs a management screen in the admin panel
For example, a "single-level category" requires just 1 table, but a "3-level category (top > middle > sub)" requires 3 tables plus recursive queries, increasing the effort by 3-5x.
Problems That Arise from Poor DB Design
Problem 1: Performance Degradation
A poorly normalized DB slows down dramatically as data grows. "A page that was fast at first now takes 10 seconds once we hit 10,000 records" is a classic symptom.
Problem 2: Inability to Add Features
If the initial design is inadequate, new features either cannot be added or require tearing apart the entire structure. For example, a seemingly simple request like "add options to products" can take 1 day or 2 weeks depending on the DB structure.
Problem 3: Data Inconsistency
Without proper constraints, invalid data can be inserted. Example: a non-existent user ID appearing in the orders table.
Problem 4: Migration Nightmare
Changing the DB structure while the service is live is extremely risky. Existing data must be converted to the new structure, and failure can result in data loss.
Lesson: When DB design is done well from the start, it runs smoothly for 2 years. When done carelessly, a complete overhaul is needed within 3 months.
What Clients Should Check in the ERD
You do not need to design the ERD yourself, but you should verify the following.
Basic Checks:
Performance-Related:
Security-Related:
If the ERD is hard to understand, ask the agency to "explain in simple terms what each table stores and how the tables are related to each other."
Database Selection Guide
| DB | Characteristics | Best Suited For |
|---|---|---|
| PostgreSQL | The relational DB standard, excellent scalability | Most web services (recommended) |
| MySQL | Most widely used, easy to manage | Small to mid-size web services |
| MongoDB | NoSQL, flexible schema | Unstructured data, rapid prototyping |
| Redis | In-memory, ultra-fast | Caching, sessions, real-time rankings |
| Elasticsearch | Full-text search engine | Services where search is the core feature |
Recommendation for SI projects: PostgreSQL is the safest choice. It handles relational data reliably, has excellent JSON support, and is easy to operate on AWS RDS.
MongoDB Caveat: Its schema-free nature makes it fast initially, but management can become difficult as data grows in complexity. It is not suitable for services with heavily relational data.
Want to discuss your project in detail?
Enter your requirements on Freesi, and AI will instantly provide an estimated quote.
Get a Free Quote