Our PostgreSQL development services and consulting cover 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
Every PostgreSQL database we audit has 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 128MB on a 64GB server. PostgreSQL can do almost anything. Most installations let it do almost nothing well.
Dead tuples accumulate, bloat grows, query plans degrade — all quietly, until performance falls off a cliff. We tune autovacuum thresholds per table based on actual write volume, not one setting applied uniformly across the entire database.
Range partitioning for time-series, list partitioning for multi-tenant, hash partitioning for even distribution. Added before the table becomes unmanageable, not after queries start timing out and the maintenance window isn't long enough anymore.
PostgreSQL forks a process per connection. At 500 concurrent connections, that overhead alone becomes the bottleneck. PgBouncer or Supavisor in front of every production database — connection pooling isn't optional once you're past a handful of clients.
pgvector for AI embeddings, PostGIS for geospatial, TimescaleDB for time-series, Citus for horizontal sharding. The right extensions turn PostgreSQL into a specialized database without needing a separate one running alongside it.
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 honestly 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
Eight ways this work shows up for real clients. Each one 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. MySQL, Oracle, or SQL Server data remodeled for PostgreSQL. Performance baselined, extensions validated, zero-downtime cutover.
TimescaleDB for IoT and metrics. PostGIS for location queries. PostgreSQL as the single database for relational, time-series, and geospatial workloads at once.
Role-based access control, encryption at rest and in transit, audit logging, and privilege review — security built into the database layer, not bolted on afterward.
Hands-on training for engineering teams on query tuning, indexing strategy, and the operational discipline that keeps a database healthy long after we hand it off.
Major version upgrades, extension compatibility testing, bloat remediation, and ongoing monitoring — so a tuned database doesn't quietly drift back into the mess we were hired to fix.
The playbook
Patterns from real production databases, not pgAdmin tutorials.
P01
Enabled from day one. Top queries by total time, mean time, and call count reviewed weekly. The single most valuable PostgreSQL tuning tool that most teams never turn on.
P02
Write-heavy tables get aggressive thresholds. Read-heavy tables get relaxed ones. Not one global setting pretending every table behaves 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 a table reaches the size where the maintenance window stops being long enough.
P05
HNSW indexes for sub-50ms similarity search at millions of vectors. Quantization with halfvec for memory-constrained deployments. One database for relational and vector workloads together.
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
Industries we serve
We've delivered PostgreSQL development services for healthcare, finance, telecommunications, and SaaS platforms — anywhere mixed workloads, AI-native features, or regulatory data handling genuinely matter.
Engagement shape
Database by database. The current environment stays live the entire time we work.
Two senior PostgreSQL engineers. pg_stat_statements analysis, vacuum health check, index audit, connection pooling review. A ranked, dollarized plan.
Top queries tuned, PgBouncer configured, autovacuum thresholds adjusted, bloat remediated. Measurable improvement visible in week two.
Partitioning implemented, extensions configured, HA tested, cloud migration baselined. Your applications keep running throughout.
Monitoring configured, vacuum healthy. Runbook handed to your team — or we stay on for PostgreSQL support and maintenance.
Stack
Our default PostgreSQL development stack — picked for production.
Engagement
No hourly billing for thinking time. Fixed quote or a transparent monthly rate.
Fixed scope, fixed price, senior-only team. Audit to tuned production in 6-10 weeks.
FIXED SCOPE
Senior DBAs and backend engineers specializing in PostgreSQL, embedded in your team. Pause, resize, or exit with 30 days' notice.
PER ENGINEER
A standing partner for data-intensive applications — performance tuning, pgvector architecture, cloud migration, HA design, hiring help.
PROCUREMENT-FRIENDLY
Founder-direct
Thirty minutes with the founder — a senior PostgreSQL engineer, the relevant playbook, and a candid read on whether PostgreSQL is the right database, or your workload needs something else.
