MongoDB can support many application architectures, but the best design depends on the workload. The following examples demonstrate how the document model can fit real systems.

1. E-commerce product catalog

Products often have category-specific attributes. A laptop may have RAM and processor details, while a shirt may have size and material.

{
  sku: "LAPTOP-001",
  name: "Developer Laptop",
  category: "electronics",
  attributes: {
    ramGB: 32,
    storageGB: 1024,
    processor: "Example CPU"
  }
}

Another document can use a different set of attributes:

{
  sku: "SHIRT-001",
  name: "Cotton Shirt",
  category: "clothing",
  attributes: {
    material: "cotton",
    sizes: ["M", "L", "XL"]
  }
}

This structure accommodates product-specific attributes. Frequently filtered attributes may still require carefully selected indexes or a more standardized model.

2. Learning platform

A course document can store its title, tags, difficulty, and other metadata. Enrollments and user progress may deserve separate collections because the number of enrollments can grow independently of the course document.

3. Content management system

Articles can contain structured metadata, author references, publication status, and arrays of tags. An embedded structure may be useful when the article is typically read as one unit.

4. Event and activity tracking

Applications can store events with a timestamp, event type, user identifier, and contextual metadata. Event collections need deliberate retention, indexing, and growth strategies. MongoDB also has specialized time-series collection support for suitable workloads.

5. Inventory management

Products and stock movements have different lifecycles. A product document may represent current product information, while inventory movements are stored separately to preserve a growing history of changes.

6. Logging and operational analytics

MongoDB can store structured application events and support queries or aggregation over them. However, ingestion rates, retention, cardinality, and analytical requirements should be evaluated before selecting it for large-scale observability workloads.

When MongoDB may not be the best fit

MongoDB is not automatically the ideal database for every application. Consider alternatives or a polyglot architecture when:

• The workload depends heavily on relational joins and relational constraints.
• A specialized graph, search, analytical, or time-series system better matches the main access pattern.
• The team already has strong operational capabilities around another database.
• The application's consistency and transaction requirements favor a different architecture.

A production database decision should consider data modeling, performance testing, availability, security, backup and recovery, cost, and operational expertise.