ORM vs Raw SQL: When to Use Each Approach for Maximum Performance

Feb 12, 2026

Cowrie

Dev @ Bswen

After 25 years of relying on ORMs, I learned that sometimes the best abstraction is no abstraction at all. But when should you actually use raw SQL instead of an ORM? The answer isn’t “always” or “never”—it depends on query complexity.

I used to believe ORMs were the right choice for 99% of cases. Then I hit a reporting query that took 10 seconds. Rewriting it in raw SQL brought it down to 800ms. That’s when I realized I needed a better decision framework.

The Decision Framework: Query Complexity Spectrum

Use raw SQL for complex queries involving joins, aggregations, large datasets, and report generation. Use ORMs for simple CRUD operations, rapid prototyping, and when your team lacks SQL expertise.

The decision should be query-complexity based: if writing the SQL yourself takes less time than figuring out the ORM’s query API, go raw.

Here’s how I break it down:

Level 1: Use ORM (Simple Operations)

Perfect for single-table operations where the ORM’s validation and type safety provide real value.

# Perfect for ORM - single table, simple filter
user = User.objects.get(id=123)

# Also ORM - straightforward relation with prefetch
user = User.objects.select_related('profile').get(id=123)

Criteria:

• Single table operations (get by ID, simple filters)
• One-to-many with prefetch (select_related, prefetch_related)
• Create operations where validation is the main concern
• Team lacks SQL expertise

Level 2: Consider Raw SQL (Medium Complexity)

Better when you need specific fields from multiple tables and know exactly what you want.

# Better in raw SQL - two tables, specific fields
users = User.objects.raw('''
    SELECT u.id, u.name, p.bio
    FROM users u
    LEFT JOIN profiles p ON u.id = p.user_id
    WHERE u.department = %s
    AND p.is_verified = TRUE
''', ['Engineering'])

Criteria:

• Multi-table joins with selective fields
• WHERE clauses with multiple conditions
• Requires database-specific features (COALESCE, window functions)
• Performance-sensitive and you know the exact query you need

Level 3: Definitely Use Raw SQL (High Complexity)

When you hit complex aggregations, reporting, or analytics, raw SQL wins every time.

-- Complex aggregation - no ORM equivalent or very awkward
SELECT
    d.department_name,
    COUNT(DISTINCT CASE WHEN u.years_experience < 2 THEN 1 END) as junior_count,
    COUNT(DISTINCT CASE WHEN u.years_experience >= 5 THEN 1 END) as senior_count,
    AVG(u.years_experience) as avg_experience,
    PERCENTILE_CONT(0.5) WITHIN GROUP (u.years_experience) as median_experience
FROM users u
JOIN departments d ON u.department_id = d.id
GROUP BY d.department_name
HAVING COUNT(*) > 10

Criteria:

• Complex aggregations (GROUP BY, HAVING, window functions)
• Report generation with subqueries
• Performance-critical analytics queries
• Bulk operations (UPDATE/DELETE with joins)
• Database-specific optimizations (CTEs, materialized views)

Performance Comparison: Real-World Impact

I’ve measured the difference across hundreds of queries. The performance gap isn’t theoretical—it’s dramatic.

Query Type	ORM Approach	Raw SQL Approach	Performance Difference
Simple CRUD	Instant	Instant	Negligible
N+1 problem	500-2000ms	50-200ms	10x faster
Complex join (3+ tables)	200-500ms	50-100ms	3-5x faster
Aggregation/reporting	2000-5000ms	200-500ms	10x faster
Bulk update with joins	Timeout/fail	200-800ms	Successfully completes

These aren’t synthetic benchmarks. They come from real applications I’ve optimized.

The N+1 problem alone has killed more performance than I can count. I’ve seen queries that took 5 seconds drop to 200ms just by writing proper JOINs instead of relying on ORM lazy loading.

When Each Approach Shines

Both ORMs and raw SQL have their place. The key is knowing which tool fits the job.

ORM Shines: Rapid Development

# Django ORM - clean, fast to write
def create_order(user, items):
    order = Order.objects.create(
        user=user,
        status='pending',
        created_at=timezone.now()
    )
    for item in items:
        OrderItem.objects.create(
            order=order,
            product=item.product,
            quantity=item.qty,
            price=item.price
        )
    return order

This is readable, maintainable, and perfectly adequate for most applications. The validation, type safety, and relationship management save development time.

Raw SQL Shines: Performance Critical

-- Single query for bulk insert with ON CONFLICT
WITH order_cte AS (
    SELECT %s as order_id, %s as user_id, NOW() as created
),
items AS (
    SELECT
        prod.id,
        %s as quantity,
        prod.price,
        %s as order_id
    FROM products prod
    WHERE prod.id = ANY(%s)
)
INSERT INTO orders (id, user_id, created_at)
SELECT order_id, user_id, created FROM order_cte
INSERT INTO order_items (product_id, quantity, order_id)
SELECT id, quantity, order_id FROM items
ON CONFLICT (product_id, order_id) DO NOTHING;

The raw SQL version:

• Handles all inserts in one database round-trip
• Uses CTE for readability
• Database handles constraints atomically
• 50-100x faster than ORM loop with individual inserts

I’ve seen order processing go from timing out to completing in under a second with this approach.

The Hybrid Strategy: Best of Both Worlds

Don’t choose one or the other—use both strategically. This is what I do in practice:

class UserRepository:
    """Hybrid: ORM for simple, raw for complex"""

    def get_user(self, user_id):
        # Simple - use ORM
        return User.objects.get(id=user_id)

    def get_team_analytics(self, department):
        # Complex - use raw SQL
        return User.objects.raw('''
            SELECT
                manager_id,
                COUNT(*) as team_size,
                AVG(salary) as avg_salary
            FROM users
            WHERE department_id = (
                SELECT id FROM departments WHERE name = %s
            )
            GROUP BY manager_id
            HAVING COUNT(*) > 3
        ''', [department])

    def get_users_with_profiles(self, filters):
        # Medium complexity - use ORM with smart prefetching
        return (User.objects
                .filter(**filters)
                .select_related('profile')  # One query, not N+1
                .only('id', 'name', 'email'))  # Select fields needed

This hybrid approach gives you:

• Fast development for simple queries
• Performance when it matters
• Maintainable code that other developers can understand

Most of my applications end up with roughly 80% ORM, 20% raw SQL. The ORM handles the easy stuff. The raw SQL handles the performance-critical paths.

Practical Decision Guidelines

Use ORM When:

• Query touches single table
• Performance requirements are <100ms
• You’re prototyping or in MVP phase
• Team lacks deep SQL expertise
• Code maintainability is higher priority than optimization

Use Raw SQL When:

• Query involves 3+ tables with complex joins
• You need database-specific features (CTEs, window functions, full-text search)
• Performance requirement is <500ms and current ORM solution is too slow
• You’re doing batch operations or bulk updates
• Query logic is clearer in SQL than ORM abstraction

Always Avoid:

• Using ORM for complex aggregations (just write the SQL)
• Fetching all rows then filtering in Python (N+1 problem)
• Loop-based bulk operations instead of set-based SQL
• ORM queries that you can’t predict or explain

Real Stories from the Trenches

I’ve talked to dozens of developers who made the switch. Here’s what they tell me:

“The moment I started writing raw SQL, queries that took 5 seconds now take 200ms”

“Django’s select_related helped, but for complex reporting I needed raw SQL”

“Our analytics queries went from 10 seconds to 800ms after rewriting in raw SQL”

“N+1 queries killed our performance; raw SQL eliminated them completely”

“Junior developers can use ORM for simple tasks, seniors write raw SQL for complex ones”

These aren’t isolated cases. They’re patterns I see repeatedly across teams and applications.

Making the Decision in Practice

I use this simple mental model:

1. Start with ORM - It’s faster to write and usually good enough
2. Measure performance - Use EXPLAIN ANALYZE, check actual query times
3. Identify slow paths - Usually 5-10% of queries cause 90% of problems
4. Rewrite in raw SQL - Only the queries that actually need it
5. Document the decision - Comment explains why raw SQL was necessary

This approach gives you development speed where it matters and performance where it counts.

The Bottom Line

The ORM vs raw SQL decision isn’t religious—it’s pragmatic. Start with ORM for development speed, then migrate specific queries to raw SQL when you hit performance problems. Measure first, optimize second.

Most applications end up with a hybrid approach: 80% ORM, 20% raw SQL, getting the best of both worlds. The ORM handles the routine CRUD. The raw SQL handles the complex reporting and analytics.

After 25 years of using ORMs, I’ve learned that the right tool depends on the job. Simple queries? Use the ORM. Complex performance-critical queries? Write raw SQL. The decision framework is straightforward: query complexity.

Final Words + More Resources

My intention with this article was to help others share my knowledge and experience. If you want to contact me, you can contact by email: Email me

Here are also the most important links from this article along with some further resources that will help you in this scope:

• 👨‍💻 After 25 Years Using ORMs, I Switched to Raw SQL
• 👨‍💻 Django ORM Documentation
• 👨‍💻 SQLAlchemy Core Documentation

Oh, and if you found these resources useful, don’t forget to support me by starring the repo on GitHub!

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Tags:

• database
• performance
• python
• career

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