Make the database stop being the bottleneck.
Index audits, slow-query analysis, N+1 hunts, partitioning, and a read-replica strategy — on Postgres, MySQL, or DynamoDB. We measure the queries before we touch anything, make the changes that matter, and show you the same numbers afterward. The point is a database that scales with you, not a server bill that grows every quarter.
You're paying for a bigger server to hide a missing index.
When the database gets slow, the easy fix is a bigger instance — more CPU, more RAM, a fatter bill — and it works, for a while, until the next plateau. But vertical scaling is almost always papering over something fixable: a query doing a full table scan, an ORM firing a hundred small queries where one would do, a table that should have been partitioned two years ago. We profile the real workload, find the queries that actually hurt, and fix the cause — the index, the plan, the schema — then prove it with measurements before and after. The goal isn't a clever database. It's queries that stay fast as the data grows, on the hardware you already have.
The database became the thing everyone waits on.
Each familiar symptom has the same answer — measure first, fix the cause.
- 01
Database CPU is pinned at the worst times
We profile what's actually running and fix the queries eating the cores.
- 02
Queries time out under load
Index and plan work so the hot paths stay fast when traffic spikes.
- 03
You keep buying a bigger instance
Find the missing index instead of renting more CPU to hide it.
- 04
One endpoint fires a hundred queries
Hunt down the N+1, batch it, and watch the page load collapse.
- 05
A single table is now enormous
Partitioning and a read-replica strategy so size stops being the limit.
Everything a fast database needs.
On Postgres, MySQL, or DynamoDB — tuned for the queries you actually run.
Slow query audit
We pull the slowest, most frequent queries from the logs and read their plans line by line.
Index strategy
The indexes you're missing, the ones you don't need, and the composite ones that change everything.
Schema & partitioning
Tables shaped for how they're queried, and big ones split so size stops slowing you down.
N+1 elimination
The hidden hundred-query endpoints found and batched back into one or two.
Read-replica strategy
Reads moved off the primary, with the routing and lag trade-offs spelled out.
Capacity planning
Connection pooling, headroom, and a plan for the next 10x — not a panic at it.
From slow query to proven fix, measured at both ends.
- Profile01
- Index02
- Tune03
- Replicate04
- Verify05
We profile the real workload to see what hurts, add or fix the indexes, tune the queries and schema, move reads to replicas where it helps, and verify every change against the numbers we captured before we started.
Read the plan, not the guess.
We start where the pain is: the slowest and most frequent queries in your logs, run through EXPLAIN so we can see what the database is actually doing — the sequential scans, the bad join order, the index it's ignoring. Tuning by intuition wastes weeks; tuning by the query plan fixes the right thing the first time.
- EXPLAIN / ANALYZE on the queries that hurt
- Sequential scans and bad plans found, not guessed
- Slow-query log and pg_stat_statements mined
- The 5% of queries causing 95% of the load
The right index beats a bigger box.
Most database CPU problems are a missing index, and most slow databases also carry indexes nobody uses, quietly taxing every write. We audit what you have, add the composite and partial indexes the hot queries actually need, and drop the dead weight — so reads get fast without making writes pay for it.
- The missing index behind the full scan
- Composite and partial indexes for hot paths
- Unused indexes dropped to speed up writes
- Index bloat checked and reclaimed
One query, not a hundred.
The N+1 problem hides in the ORM: a list page that quietly fires one query per row, turning a fast endpoint into a slow one as data grows. We trace the queries an endpoint really makes, collapse the N+1 into a join or a batch, and rewrite the queries that fight the planner — often turning a multi-second page into a sub-100ms one.
- N+1 patterns traced to the endpoint
- Loops of queries batched into one
- Queries rewritten to use the index
- Caching where the same read repeats
Scale out before you scale up forever.
When a table gets too big or the primary can't keep up, the answer isn't always a larger instance. We partition the tables that have outgrown a single file, move read traffic onto replicas with the lag trade-offs made explicit, and add connection pooling so the database isn't drowning in connections it can't serve.
- Partitioning for tables that outgrew one file
- Read replicas with routing and lag spelled out
- Connection pooling (PgBouncer / proxies)
- A capacity plan for the next 10x
Faster queries, proven with numbers.
Measure first
We capture the baseline — slow-query logs, query plans, CPU, and the latencies you live with — before we change a thing.
Find what hurts
We rank queries by total time and frequency, read the plans, and trace the N+1s, so we fix causes, not symptoms.
Index & rewrite
We add and prune indexes, rewrite the queries fighting the planner, and reshape the schema where it's earning its keep.
Partition & replicate
We partition oversized tables, move reads to replicas, and add pooling so the database scales out, not just up.
Verify & hand off
We re-run the same measurements, show before and after, and leave you the queries to watch and the plan for next time.
The database turned into the ceiling on everything.
Database CPU is the bottleneck
The instance is pinned, the app is starved, and the obvious fix keeps being a bigger box rather than a faster query.
Queries time out under load
Things are fine until traffic climbs, then the slow queries pile up, connections run out, and pages start to fail.
You're scaling vertically forever
Every quarter the database needs more CPU and RAM, and the bill grows while the headroom never quite does.
Proven database tools, used with restraint.
The things teams ask first.
Stop renting CPU to hide a missing index.
Send us your slow queries and where the database hurts. We'll measure the baseline, audit the indexes, hunt the N+1s, and lay out the partitioning and replica strategy — then prove the difference with the same numbers, before and after.