PostgreSQL development services for teams building on the most advanced open-source relational database. JSONB for document workloads without a separate NoSQL layer. pgvector for AI embeddings without a separate vector database. Full-text search, GIS, time-series — all in one engine. We build on PostgreSQL with the tuning, partitioning, and extension discipline that turns a versatile database into a fast one.
Why Entalogics for PostgreSQL
The PostgreSQL databases we audit always have the same problems — autovacuum configured at defaults that can't keep up with write volume, no partitioning on tables past 100 million rows, connection pooling absent so every request opens a new connection, and `shared_buffers` at the default 128MB on a 64GB server. PostgreSQL can do almost anything. Most installations let it do almost nothing well.
Autovacuum at default settings falls behind on write-heavy tables. Dead tuples accumulate, bloat grows, query plans degrade. We tune autovacuum thresholds per table based on write volume — not one setting for the entire database.
Range partitioning for time-series. List partitioning for multi-tenant. Hash partitioning for even distribution. Added before the table hits 100 million rows — not after queries start timing out and maintenance windows aren't long enough.
PostgreSQL forks a process per connection. At 500 concurrent connections, the server spends more time managing processes than running queries. Connection pooling is not optional in production.
pgvector for AI embeddings. PostGIS for geospatial. TimescaleDB for time-series. pg_stat_statements for query analysis. Citus for horizontal sharding. The right extensions turn PostgreSQL into a specialised database — without needing a separate one.
When PostgreSQL, when not
PostgreSQL handles OLTP, JSONB documents, vector search, geospatial queries, and time-series — in one engine. That versatility creates the trap of using it for everything without tuning it for anything. We'll tell you on the first call which workloads belong in PostgreSQL and which don't.
PICK POSTGRESQL WHEN
CONSIDER ALTERNATIVES WHEN
WE SAY NO WHEN
What we build on PostgreSQL
The shapes of PostgreSQL development we deliver most. Each tuned and production-ready.
Schema design, index strategy, query tuning, autovacuum configuration. The relational database layer your application deserves — fast, correct, and maintainable.
Embeddings stored alongside relational data. HNSW indexes for similarity search. Hybrid queries combining vector distance with SQL filters. RAG without a separate vector database.
Streaming replication, Patroni for automatic failover, PgBouncer for connection pooling. HA that's tested — not just configured.
On-prem to RDS, Aurora PostgreSQL, Cloud SQL, or Supabase. Performance baselined, extensions validated, connection pooling configured. Zero-downtime cutover.
TimescaleDB for IoT and metrics. PostGIS for location queries. PostgreSQL as the single database for relational + time-series + geospatial.
Major version upgrades (14→17), extension compatibility testing, vacuuming strategy, bloat remediation. The current database stays running throughout.
The playbook
PostgreSQL patterns from real production databases — not pgAdmin tutorials.
P01
Enabled from day one. Top queries by total time, mean time, and calls reviewed weekly. The single most valuable PostgreSQL tuning tool.
P02
Write-heavy tables get aggressive autovacuum thresholds. Read-heavy tables get relaxed ones. Not one global setting pretending all tables behave the same.
P03
PgBouncer or Supavisor in front of every production database. Transaction-mode pooling for serverless workloads. No raw connections from application servers.
P04
Range partitioning on time-series tables. List partitioning for multi-tenant. Implemented before the table hits the size where maintenance windows aren't long enough.
P05
HNSW indexes for sub-50ms similarity search at millions of vectors. Quantisation (halfvec) for memory-constrained deployments. One database for relational + vector.
P06
Every new query profiled with EXPLAIN (ANALYZE, BUFFERS). Sequential scans on large tables caught in code review. Index recommendations based on actual execution, not theory.
Signature case
A B2B SaaS platform on PostgreSQL 14 — connection exhaustion at 200 concurrent users, no PgBouncer, autovacuum falling behind on the events table (800M rows, unpartitioned), and queries timing out during peak hours. Added PgBouncer, partitioned the events table by month, tuned autovacuum, and upgraded to PostgreSQL 17 in 8 weeks. Connection limit gone. p99 query latency at 3ms.
Before
PG 14 · connection exhaustion at 200 users · 800M row unpartitioned table · autovacuum behind
After
PG 17 · PgBouncer, 2000+ concurrent · monthly partitions · autovacuum current · p99 3ms
Engagement shape
A typical PostgreSQL engagement. We tune or migrate database by database — the current environment stays live while we work.
Two senior PostgreSQL engineers. pg_stat_statements analysis, vacuum health check, index audit, connection pooling review. A ranked, dollarized RFC.
Top queries tuned, PgBouncer configured, autovacuum thresholds adjusted, bloat remediated. Measurable improvement in week two.
Partitioning implemented, extensions configured, HA tested, cloud migration baselined. Your applications keep running.
Monitoring configured. Vacuum healthy. Runbook handed to your team — or we stay on retainer.
Stack
Our default PostgreSQL development stack — picked for production.
Engagement
No hourly retainer that bills for "thinking time." Pick a lane that matches your stage; everything is fixed-quote or transparently rated.
A defined scope, a fixed price, a senior-only team. From audit to tuned production in 6–10 weeks.
$15k–$30k
FIXED SCOPE
Embedded engineers in your team. Senior DBAs and backend engineers specialising in PostgreSQL. Pause, resize, end with 30 days' notice.
$5k / eng / mo
PER ENGINEER
A long-term partner for data-intensive applications — performance tuning, pgvector architecture, cloud migration, HA design, hiring help.
custom
PROCUREMENT-FRIENDLY
Founder-direct
Thirty minutes with the founder. We'll bring a senior PostgreSQL engineer, the relevant playbook, and a candid read on whether PostgreSQL is the right database — or whether your workload needs something else.
