PostgreSQL development company
From the first schema to the bloat and vacuum problems that appear years later, Krapton builds PostgreSQL databases that stay fast as tables grow: indexed for real query patterns, migrated without locking production, and backed up in a way you have actually restored.
- Senior engineers · IST + EST overlap
- NDA on day 1
- 24-hour reply
- Projects shipped
- 50+
- Code ownership
- 100%
- Client rating
- 4.8/5
- First reply
- 24h
PostgreSQL work that survives production growth
Krapton is a PostgreSQL development company for products whose data model, query speed or migration risk has become a business problem. Most PostgreSQL trouble is not exotic. A migration adds an index without CONCURRENTLY and blocks writes; a long-running transaction stops autovacuum from removing dead tuples and tables bloat; every serverless function opens its own connection until max_connections runs out; an ORM loads rows one query at a time. Our engineers start with pg_stat_statements and EXPLAIN (ANALYZE, BUFFERS) to find where time actually goes, then fix the cause with a composite or partial index, a rewritten query, declarative partitioning for time-series tables or PgBouncer in transaction mode. We build on Postgres’s own strengths, such as JSONB with GIN indexes, row-level security for tenant isolation, PostGIS and pgvector, before adding another datastore. We are also candid when PostgreSQL is not the answer: heavy analytical scans over billions of rows usually belong in ClickHouse, BigQuery or Snowflake, fed by logical replication or Debezium.
PostgreSQL development services
Schema and data modeling
Normalized schemas with real constraints, foreign keys and check rules, JSONB only where structure genuinely varies, and type choices such as timestamptz, numeric and identity columns that avoid painful corrections later.
Query and index tuning
Slow queries found with pg_stat_statements and auto_explain, then fixed through composite, partial or covering indexes, rewritten joins and fresher planner statistics, verified with EXPLAIN (ANALYZE, BUFFERS) before and after.
Zero-downtime schema changes
Changes split into expand-and-contract steps, indexes built CONCURRENTLY, lock_timeout set on every migration, and backfills batched so Flyway, Liquibase or Prisma Migrate runs never stall production writes.
Upgrades and database moves
Major-version upgrades through pg_upgrade or logical replication cutovers, and migrations from MySQL, SQL Server or Oracle using pgloader, ora2pg or AWS DMS, with row-count and checksum validation.
Extensions and advanced features
PostGIS for geospatial queries, pgvector for embeddings and semantic search, TimescaleDB for time series, tsvector full-text search, and row-level security policies that isolate tenants inside one schema.
High availability and backups
Streaming replicas, Patroni or managed failover on RDS, Aurora, Cloud SQL or Azure Flexible Server, and point-in-time recovery with pgBackRest or WAL-G that is restore-tested on a schedule.
Why teams choose Krapton for PostgreSQL
Senior engineers, fixed scope, and code you own — the things agencies promise and rarely deliver.
Measure before changing anything
Every tuning recommendation arrives with the query plan, buffer counts and wait events that justify it, so you can see why an index helps instead of trusting a guess.
Migrations reviewed for locks
Each migration is checked for the lock level it takes, any table rewrite and the length of its backfill. Risky changes ship in phases with rollback steps written in advance.
Postgres first, new datastores later
We reach for JSONB, LISTEN/NOTIFY, SKIP LOCKED job queues, full-text search and pgvector before proposing another system, which keeps operations manageable for small teams.
Restores proven, not assumed
A backup counts only after a timed restore into a scratch environment. Recovery point and recovery time are documented as measured, not copied from a provider’s marketing page.
How a PostgreSQL project runs with Krapton
01
Workload audit
We collect pg_stat_statements output, table and index sizes, bloat estimates, vacuum history and connection patterns to learn how your database behaves today.
02
Design and plan
Schema changes, indexes, partitioning or upgrade steps are written up with lock analysis, expected impact and rollback, then agreed with your team before execution.
03
Staged execution
Changes run first against a production-sized copy or branch, then in production during an agreed window, with dashboards tracking replication lag, locks and latency.
04
Monitor and transfer
Alerts for replication lag, transaction ID age, connection saturation and slow queries are configured, and your engineers receive a runbook for each of them.
The PostgreSQL stack we build with
PostgreSQL development: questions answered
What does a PostgreSQL development company do that an application team does not?
It treats the database as a system to design and operate, not just a place the ORM writes to. That covers schema and constraint design, index strategy based on real query statistics, safe migration practice, replication and backup setup, and capacity planning for connections and storage. Application teams often manage these adequately until data volume grows; specialist help pays off once the gaps start costing uptime or speed.
When should we not use PostgreSQL?
PostgreSQL is the wrong primary store when the workload is mostly large analytical scans across billions of rows, where columnar engines such as ClickHouse, BigQuery or Snowflake are far faster. It is also a stretch for write volumes beyond what one primary can absorb, unless you adopt Citus or shard in the application. For a cache or ephemeral session data, Redis is simpler. Most transactional products fit Postgres comfortably.
How do you migrate from MySQL or SQL Server to PostgreSQL?
We migrate the schema first, then the data, then application queries, validating at each stage. Tools such as pgloader or AWS DMS move the data, while type differences, case sensitivity, sequences and stored procedures are converted by hand where needed. Continuous replication keeps the target in sync during testing, and cutover happens only after row counts, checksums and application test suites match on both sides.
How do you scale PostgreSQL as traffic grows?
Usually by removing waste before adding hardware. Query and index fixes come first, then PgBouncer to cap connection count, read replicas for reporting traffic, and declarative partitioning for large time-based tables. Caching hot reads in Redis helps some workloads. Only when a single primary truly cannot keep up do we evaluate Citus or application-level sharding, because both add lasting operational cost.
How do PostgreSQL development services handle major-version upgrades?
Each upgrade is rehearsed on a restored copy of production before the live cluster is touched. pg_upgrade in link mode is fast for self-managed servers; managed services offer in-place upgrades with downtime, or blue-green and logical replication cutovers with a much shorter pause. Extensions such as PostGIS must support the target version, and planner changes are checked by replaying the top queries from pg_stat_statements.
Ready to build? Tell us what you have in mind.
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