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EXPLAIN ANALYZE-driven optimization
Removing unnecessary sequential scans, fixing bad planner statistics and adding multi-column partial indexes.
Technologies — Databases & storage
PostgreSQL performance tuning, indexing & scaling
We find and fix what slows PostgreSQL down (query plans, indexes, connection pooling and vacuum), measured with EXPLAIN ANALYZE before and after.
Core capabilities
Why we build with PostgreSQL
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Connection pooling with PgBouncer
Managing thousands of client connections with transaction-mode pooling to avoid backend memory bloat.
03
Autovacuum & bloat remediation
Tuning autovacuum scale factors, table fillfactors and index repack routines.
Use cases
Where PostgreSQL fits
High-concurrency SaaS databases
Getting more throughput from the hardware you already pay for, before reaching for sharding or bigger instances.
Time-series & financial table partitioning
Declarative range partitioning by date, so queries and maintenance stay fast as tables grow.
-- Add partial index for active subscription lookup
CREATE INDEX CONCURRENTLY idx_subscriptions_active_tenant
ON subscriptions (tenant_id, status)
WHERE status IN ('active', 'trialing');
-- Tune table autovacuum for high-update tables
ALTER TABLE account_balances SET (
autovacuum_vacuum_scale_factor = 0.05,
autovacuum_vacuum_cost_limit = 1000
);Technical FAQs
Frequently asked engineering questions
PostgreSQL runs a separate backend process for every connection, each with its own memory, so thousands of idle connections waste RAM and CPU. We put PgBouncer in transaction mode in front, so many clients share a small pool.
We use pg_repack to rebuild bloated tables and indexes online without taking exclusive table locks.
Ecosystem
Related technologies
Planning a PostgreSQL project?
Tell us about your architecture, backlog and team. We'll reply within one business day with an honest read on whether we can help.