API & ORM Performance: The Slow Query Is Often in Your App
80% of slow API performance issues we investigate trace back to the application layer: N+1 queries from ORM misconfiguration, connection pool exhaustion, missing pagination, or SELECT * where 4 columns would do. We find them and fix them.
Why Your API Is Slow (It's Usually Not the Database)
When an API endpoint is slow, the default assumption is that the database needs a better index or a faster instance. That's wrong about 80% of the time.
The actual cause is usually the application layer: an ORM that fires one query per row instead of one query per request, a connection pool sized for 10 threads when you have 200 concurrent requests, or a serializer loading 40 fields when the client displays 3.
These are application code problems, not database problems. Adding a read replica or upgrading your instance doesn't fix them—it just spreads the same inefficiency across more hardware.
We instrument your endpoints, correlate query counts to response times, and fix the ORM calls, connection handling, and query patterns that are actually causing the slowness. Then we measure the result.
- Your slow query log is empty but the API is still slow
- Response time grows linearly with the number of items returned
- Database CPU is low but API p95 latency is high
- Upgrading the DB instance didn't improve response times
- GraphQL queries that look simple take 2+ seconds
- Connection timeout errors under moderate concurrent load
What Our API Performance Audit Delivers
Same-Day N+1 Diagnosis
N+1 queries are detectable in hours with the right tooling. We instrument your highest-traffic endpoints and produce a ranked list of query count by endpoint—sorted by database load—within one working day.
Fix the App Layer, Not Just the DB
Adding an index to a query that fires 200 times per request saves 5%. Fixing the ORM call that causes 200 queries to become 2 saves 98%. We find and fix the actual problem.
Measured Impact, Not Estimates
Every change we make is measured before and after: query count per endpoint, p95 response time, database CPU. You see the before/after numbers, not our assessment of what we think helped.
The Six Issues We Find Most Often
ORM fires one query per row to load related data. 20 items = 21 queries.
API loads 10,000 rows to return 20. Memory spikes, slow queries.
More app threads than DB connections. Requests queue and timeout.
Loading 40 columns when the endpoint uses 4. Wasted I/O and memory.
ORM queries that look simple produce full table scans at scale.
Blocking the event loop waiting for DB responses in async frameworks.
ORMs & Frameworks We Support
Frequently Asked Questions
Get a Same-Day N+1 Diagnosis
Share your slowest API endpoints, your ORM and framework, and access to query logs. We'll identify whether you have N+1 problems, connection pool issues, or a genuine database bottleneck—and give you a prioritized fix list with estimated impact.
Stop Upgrading Hardware to Fix a Code Problem
A larger RDS instance costs $400–800 more per month. Fixing the N+1 query that's causing the load costs a fraction of that—once. We find the actual problem before you spend money on infrastructure that won't help.