Database Types: Engineering for Vibe Coders
Vibe coders often begin their prototypes with whatever storage is easiest: a JSON file, a local SQLite database, or sometimes simply variables held in memory. This works for the first hour. Sometimes even for the first day. But as your data grows, as users interact with your system, or as AI-generated content becomes more complex, the cracks start showing.
Understanding database types helps you choose the right structure for your prototype and prevents major rework later. You do not need to become a database expert. You simply need to know the categories, their strengths, their weaknesses, and which problems each one solves.
This article walks through the major database types you are likely to encounter and prepares you to choose wisely before you begin building.
1. Relational databases (SQL)
Relational databases like PostgreSQL, MySQL, and SQL Server are the most common type of database. They use structured tables with well-defined relationships between them. They are ideal when your data has clear structure, consistency requirements, and long-term durability needs.
Typical use cases:
- User accounts, profiles, permissions
- E-commerce data such as orders and products
- Enterprise systems with strict consistency rules
- Analytics dashboards
- Any situation where reliable queries matter
Strengths:
- Strong consistency
- Mature tooling
- Easy to query with structured data
- Good for long-term growth
Limitations:
- Slow to change if your schema is unstable
- Rigid for AI experiments with changing data formats
- Not ideal when storing unstructured documents
🟢 Pre-prototype habit:
Sketch a list of entities in your app. If they form clear tables with predictable columns, choose a relational database early instead of ad-hoc JSON blobs.
2. Document databases (NoSQL)
Document databases like MongoDB and Couchbase store flexible, semi-structured documents, usually in JSON form. They are popular for fast-moving prototypes and AI-powered workflows that produce variable shapes of data.
Typical use cases:
- LLM-generated content
- Rapid prototyping where schema evolves frequently
- User-generated content
- Unstructured or semi-structured datasets
Strengths:
- Very flexible
- Easy to start without defining schemas
- Good for evolving prototypes
- Stores documents in their natural structure
Limitations:
- Weaker consistency guarantees
- Harder to query when documents diverge in shape
- Not suited for strong relational data
🟢 Pre-prototype habit:
Write down the types of data your prototype needs to store. If the fields are changing often, start with a document database instead of a relational one.
3. Key-value stores
Key-value databases like Redis and DynamoDB store simple pairs: a key and a value. They are incredibly fast and ideal for caching, session management, and real-time workloads.
Typical use cases:
- Storing session tokens
- Caching expensive queries
- Rate limiting
- Storing flags, feature toggles, or quick lookups
Strengths:
- Very fast
- Simple model
- Great for high-throughput use cases
Limitations:
- Not intended for complex data
- No ability to perform relational queries
- Poor for long-term, structured storage
🟢 Pre-prototype habit:
Identify any data you need to look up quickly or store temporarily. Plan a key-value store for these instead of overloading your primary database.
4. Time-series databases
Time-series systems like InfluxDB or Prometheus store data indexed by time. They excel when you need to track events, metrics, or logs over time.
Typical use cases:
- Application metrics
- IoT sensor data
- Logging pipelines
- Real-time dashboards
- Monitoring and observability systems
Strengths:
- Optimized for time-ordered data
- Efficient storage for large event streams
- Built-in querying for trends and patterns
Limitations:
- Not intended for general-purpose data
- Data often expires automatically
- Requires clear event-based structure
🟢 Pre-prototype habit:
If you think your app will track trends or store events over time, identify which metrics you will need before coding. Choose a time-series database early rather than forcing this data into SQL or a document store.
5. Vector databases
Vector databases like Pinecone, Qdrant, and Weaviate store high-dimensional embeddings used for similarity search. They are essential for modern AI applications.
Typical use cases:
- RAG systems
- Semantic search
- Image similarity and classification
- Multimodal AI workflows
Strengths:
- Optimized for similarity scoring
- Fast nearest-neighbor search
- Built for LLM and multimodal embeddings
Limitations:
- Not designed for general-purpose data
- Requires consistent embedding generation
- Metadata models vary between providers
🟢 Pre-prototype habit:
Decide early whether your app needs semantic search or vector similarity. If so, plan ahead for embedding generation and choose a vector database before writing your retrieval logic.
6. Graph databases
Graph databases like Neo4j store relationships first, and data second. They are ideal when connections matter more than individual records.
Typical use cases:
- Social networks
- Knowledge graphs
- Recommendation engines
- Fraud detection
- Rich relational queries
Strengths:
- Excellent for relationship-heavy data
- Flexible graph traversal
- Ideal for knowledge and context modeling
Limitations:
- Harder learning curve
- Overkill for simple CRUD systems
- Not great for flat data
🟢 Pre-prototype habit:
Map your data as a diagram. If the relationships between items are more important than the items themselves, plan for a graph database before you commit to SQL.
Quick pre-prototype checklist
| Question | Why It Matters |
|---|---|
| Is my data structured or flexible? | Guides SQL vs NoSQL |
| Do I need fast lookups or caching? | Suggests a key-value store |
| Am I storing metrics over time? | Points to a time-series database |
| Will I use embeddings or semantic search? | Requires a vector database |
| Are relationships central to my data model? | Suggests a graph database |
| Will my schema change often? | NoSQL may be a better fit |
| Do I need consistency guarantees? | SQL may be required |
Closing note
Choosing the right database type is not about becoming a database architect. It is about avoiding painful rework later. When you choose a database that matches your data patterns, your prototype becomes simpler, more reliable, and easier to grow.
🟢 Pre-prototype habit:
Write down what shape your data will take, how stable it is, and how you plan to query it. Use that sketch to choose a database type that supports your design instead of fighting against it.
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