PostgreSQL empowers businesses with an advanced, open-source database solution that enhances data integrity, scalability, and performance. Experience a significant reduction in operational costs while driving innovation and agility in your organization.
PostgreSQL empowers businesses with an advanced, open-source database solution that enhances data integrity, scalability, and performance. Experience a significant reduction in operational costs while driving innovation and agility in your organization.
Key capabilities and advantages that make PostgreSQL Database Solutions the right choice for your project
Boost your application performance with fast query execution and efficient indexing, leading to improved user satisfaction and retention.
Easily scale your database as your business grows, ensuring you can handle increased workloads without compromising performance.
Protect sensitive business data with robust security features, reducing the risk of data breaches and enhancing trust with customers.
Reduce total cost of ownership with an open-source model, enabling more budget allocation for innovation and growth.
Leverage a vibrant community and extensive resources for support and extension, ensuring you have the tools you need to succeed.
Ensure high data accuracy with advanced transaction management, leading to better decision-making and operational efficiency.
Discover how PostgreSQL Database Solutions can transform your business
Enhance customer experiences with real-time data analytics and seamless transaction processing, driving sales and customer loyalty.
Utilize PostgreSQL for secure, fast, and reliable transaction processing, enabling compliance and trust in financial operations.
Manage patient data with integrity and security, improving healthcare delivery and operational efficiencies.
Real numbers that demonstrate the power of PostgreSQL Database Solutions
DB-Engines Ranking
Consistently ranked among the most popular databases.
Rising in rankings
Active Contributors
Large community of active contributors.
Steadily growing
Extensions Available
Rich ecosystem of PostgreSQL extensions.
Continuously expanding
Years in Production
One of the most battle-tested databases in existence.
Proven stability
Our proven approach to delivering successful PostgreSQL Database Solutions projects
Evaluate your current database needs and identify opportunities for improvement.
Seamlessly transition to PostgreSQL with minimal disruption to your operations.
Fine-tune your database for peak performance and scalability tailored to your business.
Continuously monitor database performance to ensure reliability and efficiency.
Access expert support to resolve issues and enhance database capabilities.
Leverage the latest features and community contributions to drive ongoing business growth.
Find answers to common questions about PostgreSQL Database Solutions
PostgreSQL enhances data processing speed and accuracy, allowing for better decision-making and operational efficiency, ultimately leading to cost savings and improved performance.
Let's discuss how we can help you achieve your goals
When each option wins, what it costs, and its biggest gotcha.
| Alternative | Best For | Cost Signal | Biggest Gotcha |
|---|---|---|---|
| MySQL (MariaDB) | WordPress/PHP stacks, read-heavy workloads with simple schemas, and teams with decades of MySQL ops experience. | RDS MySQL ~20% cheaper than RDS Postgres; PlanetScale $39+/mo (indicative). | Weaker support for JSONB, partial indexes, CTEs, and modern SQL features. Replication model (statement-based by default) has more footguns than Postgres's streaming replication. |
| SQLite + Turso/LiteFS | Single-writer apps, edge deployments, and read-heavy workloads that fit on one node. Near-zero ops cost. | Turso free tier → $29/mo → scale-to-zero pricing (indicative). | Single-writer bottleneck — high-concurrency writes will serialize. No stored procedures, limited ALTER TABLE, weaker type enforcement. Fine for embedded / edge, risky for multi-writer SaaS. |
| MongoDB | Document-shaped data, flexible schemas for early-stage products, horizontal scale-out via sharding. | Atlas free tier → $57+/mo M10 → usage-based beyond (indicative). | Weaker transactional guarantees vs Postgres (ACID only within single documents historically; multi-doc transactions exist but slower). Teams often regret 'schemaless' once the data model matures. |
| CockroachDB / YugabyteDB (distributed Postgres-compatible) | Multi-region, strong-consistency workloads at >10K TPS with Postgres-wire compatibility. | Cockroach Serverless free → Dedicated $295+/mo → Enterprise custom (indicative). | Not 100% Postgres-compatible — some extensions (PostGIS, pg_vector) are missing or lag. 5–10× more expensive than single-region Postgres. Only worth it if global consistency is a revenue requirement. |
| DynamoDB (AWS) | Hyperscale key-value workloads, serverless apps, and teams deep in AWS wanting auto-scaling + pay-per-request. | Pay per RCU/WCU + storage; $0 base, can spike to $1K+/mo at scale. | No SQL, no joins, no schema flexibility without careful data modeling up-front. Wrong choice for typical SaaS CRUD — right choice for specific access patterns at scale. |
Managed Postgres (Supabase/Neon/RDS) vs. self-hosted on EC2/DO. Supabase Pro: $25/mo + $0.125/GB after 8GB storage = ~$40–$180/mo for typical SaaS. Self-hosted Postgres on DO $24/mo droplet: ~$30/mo but 4–8 hours/month of DBA time ($600–$1,600 loaded). Crossover: below ~$300/mo managed spend, managed wins on TCO. Above ~$800/mo managed (~1TB database, heavy usage), self-hosted pays back in 6–10 months if you have DBA capacity. pgvector vs. dedicated vector DB (Pinecone/Weaviate/Qdrant). pgvector on an existing Postgres handles ~1M embeddings at 50–200ms p95 query latency on a 4-vCPU instance. Dedicated vector DBs (Pinecone Serverless $0.33/M reads + $0.18/M writes) handle 10M+ embeddings with sub-50ms p99. Crossover: below ~2M embeddings with <100 QPS, pgvector is plenty and saves you a service + $50–$300/mo in Pinecone spend. Above 10M embeddings or strict sub-50ms p99, dedicated vector DB wins. Aurora Postgres vs. RDS Postgres vs. Supabase. For a 100GB DB at 500 TPS: RDS db.m6g.large multi-AZ = ~$210/mo + storage; Aurora 2 writer/reader = ~$360/mo + $0.20/M I/Os; Supabase Pro + Compute Large = ~$110/mo. Supabase wins on cost and DX for teams <50 engineers. Aurora wins above ~2K TPS or when you need 15 read replicas + failover <30s. RDS is the 'boring middle' — reliable, no magic, predictable pricing.
Specific production failures that have tripped up real teams.
SELECT COUNT(*) on a 50M-row table takes 45 secondsA team's analytics dashboard froze whenever a user opened the 'total orders' widget. Root cause: Postgres's MVCC model means COUNT(*) scans every live tuple — there's no O(1) row count cache. Fix: use an approximate count (reltuples from pg_class) for large tables, maintain a materialized orders_count column, or switch to estimated counts via EXPLAIN. Rule: never put unbounded COUNT(*) in a user-facing request path.
idle_in_transaction connections hold table locks for hours, blocking DDLA migration to add a column hung for 45 minutes in production. Root cause: a long-running Sidekiq job held an open transaction on the target table; ALTER TABLE queued behind it and blocked all subsequent queries. Fix: set idle_in_transaction_session_timeout = '5min', set lock_timeout = '10s' before DDL, and run heavy migrations with CONCURRENTLY where supported. Rule: always check pg_stat_activity for idle in transaction before long deploys.
A team's 80GB table ballooned to 340GB over 6 months despite a modest row count. Root cause: autovacuum couldn't keep up with UPDATE-heavy traffic; pg_stat_user_tables.n_dead_tup showed 60M dead tuples. Fix: tune autovacuum_vacuum_cost_limit higher, set per-table autovacuum_vacuum_scale_factor = 0.05 on hot tables, and schedule VACUUM FULL during maintenance windows. Prevention: monitor dead-tuple ratio in your alerting (Datadog/Grafana) — alert above 20%.
max_connections=100 bottlenecks a Node app under loadA Node app with 8 PM2 workers × 20-connection Pool = 160 connections tried to hit a Postgres with default 100 max_connections. Half the requests hung, p99 latency exploded. Fix: use pgbouncer (transaction-pooling mode) to multiplex app connections to a small, stable pool; typical ratio is 500 app connections → 20 Postgres. Rule: any prod app with >50 concurrent workers needs pgbouncer or Supabase's built-in pooler. Don't raise max_connections — each connection costs ~10MB RAM.
A team used gen_random_uuid as PK and saw write latency creep from 2ms to 12ms over a year. Root cause: random UUIDs scatter B-tree index inserts, causing constant page splits. Fix: use UUID v7 (time-ordered, coming in extension pg_uuidv7 or application-side libs) or use bigserial for internal PKs + UUID for external IDs. Rule: PKs should be sequential for OLTP write performance.