When starting a new .NET project, many developers default to Entity Framework (EF) — and for good reason. EF Core is robust, feature-rich, and beautifully abstracts away most database complexity.
But here’s the problem: using Entity Framework everywhere can easily overload your application with unnecessary memory consumption, query overhead, and sluggish performance, especially for simple data-access scenarios.
Are you building lightweight APIs, handling straightforward CRUD operations, or running heavy read operations that don’t need EF’s change tracking and navigation properties?
Then Entity Framework might be too much.
What is a Micro-ORM?
Micro-ORMs like Dapper, RepoDb, and PetaPoco focus on one thing: executing SQL queries and mapping results quickly and efficiently.
They don’t track entities. They don’t manage relationships. They don’t “understand” your domain model.
And that’s exactly why they are so fast.
Think of them like express trains compared to EF’s luxury buses.
Luxury is nice — but when you just need to get from point A to point B quickly, an express train makes more sense.
Why You Should Mix and Match
Use Entity Framework when:
- You need complex object graphs and relationship handling.
- You want automatic change tracking and save operations.
- You are building rich domain-driven applications.
- You love LINQ and want to query against your domain models.
But EF Core also brings:
- Extra memory usage (those tracked entities are heavy, bro).
- Extra query generation (translation from LINQ to SQL isn’t free).
- Extra startup time (Context initialization, model building, etc.).
- Limited bulk operation support (EF hates massive INSERTs/UPDATEs).
Use Micro-ORMs when:
- You need ultra-fast reads.
- You’re executing simple inserts, updates, or deletes.
- You want minimal memory footprint and maximum performance.
- You are dealing with reporting dashboards, read-heavy APIs, or bulk operations.
| Feature | Entity Framework Core | Micro-ORMs (Dapper, RepoDb, PetaPoco, etc.) |
|---|---|---|
| Purpose | Full-fledged ORM (Object-Relational Mapper) — handles database mapping, tracking, migrations, relationships. | Lightweight data access layer — focuses only on executing SQL and mapping results quickly. |
| Data Mapping | Automatic mapping of database tables to C# objects (entities). Supports complex graphs and relationships. | Manual or semi-automatic mapping. You control mapping by writing SQL or using minimal helpers. |
| SQL Generation | Generates SQL automatically from LINQ queries. Developers often don’t write raw SQL unless needed (FromSqlRaw). | You write raw SQL queries manually, giving complete control over how the query looks and performs. |
| Performance (Simple Queries) | Slower, because: LINQ parsing ➔ SQL translation ➔ Change tracking ➔ Entity materialization. | Blazingly fast, because: raw SQL execution ➔ simple object mapping ➔ no overhead. |
| Performance (Complex Queries) | Efficient for relationship-based queries with lazy loading, eager loading (Include), or explicit loading. | Requires manual JOINs in SQL. Complex queries need more developer effort but offer top performance. |
| Memory Usage | High — tracks entities, maintains internal states for change detection, context caching, etc. | Very low — no entity tracking, no change detection, just executes SQL and maps results. |
| Change Tracking | Built-in — tracks entity states (Added, Modified, Deleted) automatically for updates and saves. | No change tracking — you manually manage what to insert, update, or delete. |
| Bulk Operations (Insert/Update/Delete) | Poor native support. Needs third-party libraries like EFCore.BulkExtensions to handle large bulk operations. | Strong support via native batching techniques or using extensions like Dapper Plus, RepoDb bulk APIs. |
| Transactions | Easy — managed by DbContext.Database.BeginTransaction() or implicit SaveChanges behavior. | Manual — you must open, manage, and commit/rollback SQL transactions yourself, or use helper libraries. |
| Query Flexibility | Limited — LINQ is powerful but complex queries (like CTEs, window functions) are awkward without raw SQL. | High — full power of SQL at your fingertips (including CTEs, unions, window functions, stored procedures). |
| Learning Curve | Easier for beginners — no need to learn SQL deeply to start CRUD operations. | Requires good SQL knowledge — you are directly responsible for query efficiency and accuracy. |
| Maintainability (Large Systems) | Excellent — centralized DbContext, models, migrations make large systems easy to manage (if architected properly). | Tricky — if you write bad, repetitive SQL or skip proper abstraction (repositories, services), your code can become messy. |
| Migrations / Schema Evolution | Built-in migrations support. Can scaffold, update, and rollback database schema changes automatically. | No direct support. Schema changes must be manually scripted and applied or managed using separate tools. |
| Support for Relationships | First-class citizen — navigational properties, cascade delete, lazy/eager loading, etc. are native. | Manual — you must JOIN tables and map nested objects manually if needed. No “relationships” out-of-the-box. |
| Connection Management | Managed inside DbContext (opens and closes internally unless otherwise specified). | You control opening/closing connections (usually via using blocks). |
| Customization (Stored Procedures, Views) | Supported, but less friendly. Custom SQL (FromSqlRaw) must be manually handled and mapped. | Very easy — since you control the SQL, using stored procs, views, and functions is natural. |
| Suitability | – Domain-driven apps |
Conclusion
Entity Framework is amazing — but don’t turn it into a hammer for every nail.
Smart architecture means choosing the right tool for the right job.
By embracing micro-ORMs when needed, you can avoid unnecessary overhead, reduce load, and make your applications faster, lighter, and more scalable.
| Scenario | Recommendation |
|---|---|
| Building rich, domain-driven applications | Entity Framework Core |
| Building ultra-fast, read-heavy APIs or simple services | Micro-ORMs |
| Heavy reporting (mass data reads without updates) | Micro-ORMs |
| Complex business rules involving multi-table updates | Entity Framework Core |
| Small tools, background jobs, one-off migrations | Micro-ORMs |
NARESH ASKANI 