Postgres and SQL Server both handle production relational workloads at scale. Both support ACID transactions, rich SQL, replication, and cloud-managed deployments. The choice between them is rarely about raw capability and almost always about licensing cost, ecosystem lock-in, existing infrastructure, and what happens when your analytical queries outgrow either of them.

This post covers where each database genuinely excels, where each has real limitations, and when you need to layer something else on top for analytics.

The licensing difference

The most immediate difference is cost structure. Postgres is open source under the PostgreSQL License. The database itself costs nothing. You pay for managed hosting (RDS, Aurora, Supabase, Neon, etc.) or you operate it yourself.

SQL Server uses commercial per-core licensing. Microsoft publishes two main editions for production use: Standard and Enterprise. Enterprise Edition is required for features like Always On Availability Groups with multiple secondaries, advanced columnstore index capabilities, and in-memory OLTP at scale. The per-core licensing model means costs scale directly with compute.

This difference shapes team decisions at both ends: small teams default to Postgres because the database itself is free, and large enterprise organizations often have SQL Server because existing Microsoft licensing agreements make it the path of least resistance.

Where Postgres excels

Open ecosystem and extensions

Postgres's extension system changes what the database can do. Production-grade extensions teams depend on:

• PostGIS: full geospatial queries with geometry types, spatial indexes, and distance functions. The de facto standard for GIS in relational databases.
• pgvector: vector similarity search for AI and embedding workloads, increasingly critical for retrieval-augmented generation pipelines.
• TimescaleDB: time-series optimizations including continuous aggregates, compression, and retention policies built on top of standard Postgres.
• Citus: horizontal sharding for distributed Postgres, used at scale by multi-tenant SaaS.
• pg_partman: automated partition management for large tables.

SQL Server has no equivalent extension ecosystem. Its analytical extensions (SSAS, SSRS, SSIS) are separate products with separate licensing and operational overhead.

SQL standard compliance

Postgres has strong SQL standard compliance. Features that work reliably in Postgres and have edge-case behavior differences in SQL Server's T-SQL dialect:

• FILTER clause for conditional aggregation: count(*) FILTER (WHERE status = 'active')
• LATERAL joins for correlated subqueries with set-returning functions
• Full recursive CTE support with standard syntax
• RETURNING clause for INSERT/UPDATE/DELETE
• GENERATED ALWAYS AS for computed columns

T-SQL uses different syntax for many of these, including TOP instead of LIMIT, GETDATE() instead of now(), ISNULL() instead of COALESCE(), and IDENTITY instead of sequences. Code written against T-SQL is not portable to standard SQL without modification.

JSONB for semi-structured data

Postgres's JSONB type stores JSON as a binary format with full GIN indexing:

CREATE TABLE events (
    id          UUID DEFAULT gen_random_uuid() PRIMARY KEY,
    user_id     UUID NOT NULL REFERENCES users(id),
    event_type  TEXT NOT NULL,
    occurred_at TIMESTAMPTZ NOT NULL DEFAULT now(),
    properties  JSONB
);

CREATE INDEX ON events USING GIN (properties);

-- Query nested JSON with index support
SELECT user_id, count(*)
FROM events
WHERE properties @> '{"plan": "enterprise"}'
  AND occurred_at >= now() - INTERVAL '30 days'
GROUP BY user_id;

SQL Server has JSON support but stores it as NVARCHAR. There is no native JSON type with binary storage and proper indexing. JSON querying relies on JSON_VALUE(), JSON_QUERY(), and computed columns with indexes, which is more verbose and less performant than Postgres JSONB for JSON-heavy workloads.

Where SQL Server excels

Columnstore indexes for analytical queries

SQL Server's columnstore indexes are a genuine differentiator. Introduced in SQL Server 2012 and improved in each version since, they store column data in compressed columnar format directly on tables that also have rowstore indexes. This means you can run analytical aggregations on the same table serving OLTP traffic without a separate analytical database:

-- SQL Server: add a columnstore index to an OLTP table
CREATE NONCLUSTERED COLUMNSTORE INDEX idx_orders_cs
ON orders (order_date, customer_id, amount, status, region);

-- This query now uses columnstore execution
SELECT
    region,
    DATEPART(MONTH, order_date) AS month,
    COUNT(*)                    AS order_count,
    SUM(amount)                 AS total_revenue
FROM orders
WHERE order_date >= '2025-01-01'
GROUP BY region, DATEPART(MONTH, order_date);

Postgres has no columnstore index equivalent. BRIN indexes help for range queries on sequential data, and partial indexes help for filtered queries, but columnar storage is only available via extensions like Citus or third-party solutions.

Enterprise HA and operational tooling

SQL Server's Always On Availability Groups provide synchronous and asynchronous replication to multiple readable secondaries with automatic failover. The tooling for monitoring, alerting, and administering SQL Server at enterprise scale (SQL Server Management Studio, Azure Data Studio, SQL Server Agent for job scheduling) is mature and widely deployed.

SQL Server also integrates natively with Active Directory, which is a practical requirement in many enterprise environments. Postgres can integrate with AD via pg_hba.conf and LDAP configuration, but the integration requires more configuration and is less seamless in practice.

Azure integration

If your organization runs on Azure, SQL Server and Azure SQL Database integrate deeply with Azure services: Azure Data Factory for ETL, Azure Synapse Analytics for data warehousing, Azure Monitor for database telemetry, and Managed Identity for authentication. This integration is difficult to replicate with Postgres in an Azure-native deployment.

Where both hit limits: analytics at scale

SQL Server's columnstore indexes close some of the analytics performance gap, but both Postgres and SQL Server are OLTP databases with analytics as a secondary concern.

Identifying scale problems in Postgres before they become production incidents applies equally to SQL Server: watch for long-running queries blocking OLTP traffic, memory pressure from large analytical aggregations, and index bloat from frequent updates on wide tables.

SQL Server columnstore indexes help, but they have constraints: clustered columnstore tables don't support primary keys or unique constraints, update performance on columnstore tables is lower than rowstore, and the optimizer doesn't always choose the columnstore path for complex queries. For ad-hoc analytical queries across multiple years of data with dynamic filters, a dedicated columnar OLAP database is still faster.

The pattern that works at scale: keep Postgres or SQL Server for transactional writes and OLTP reads, and stream the data you need for analytics into ClickHouse® via real-time data processing pipelines. ClickHouse's columnar storage and vectorized execution handle analytical aggregations over billions of rows in ways neither Postgres nor SQL Server can match on the same hardware.

Choosing between them

Schema differences that matter in practice

Before choosing, teams often underestimate the SQL dialect gap between T-SQL and standard Postgres SQL. Migrating a Postgres application to SQL Server, or vice versa, requires touching more code than expected.

Common T-SQL patterns with no direct Postgres equivalent:

-- T-SQL: SELECT with TOP instead of LIMIT
SELECT TOP 100 order_id, amount FROM orders ORDER BY amount DESC;

-- T-SQL: ISNULL instead of COALESCE
SELECT ISNULL(discount_pct, 0) AS discount FROM orders;

-- T-SQL: GETDATE() instead of now()
SELECT * FROM orders WHERE created_at >= DATEADD(DAY, -7, GETDATE());

-- T-SQL: string aggregation
SELECT
    customer_id,
    STRING_AGG(product_name, ', ') WITHIN GROUP (ORDER BY product_name) AS products
FROM order_items
GROUP BY customer_id;

Postgres uses LIMIT, COALESCE, now(), and string_agg() for the same operations. The patterns are equivalent but the syntax differs enough that application query strings need rewriting for any migration.

Stored procedures, triggers, and scheduled jobs (SQL Server Agent vs pg_cron) are also dialect-specific and require rewriting. For greenfield projects, standardizing on a single dialect from the start avoids this cost. For existing deployments, the dialect lock-in is a real migration barrier in both directions.

Adding real-time analytics to Postgres or SQL Server

Both databases support CDC as the mechanism for streaming changes to an analytical layer. Postgres uses logical replication slots. SQL Server uses SQL Server Agent jobs, change tracking, or SQL Server replication depending on version and edition.

Postgres CDC captures every committed row change using logical replication, making inserts, updates, and deletes available to downstream consumers within seconds. SQL Server CDC is available in Enterprise and Developer editions and works via the transaction log. Both produce a reliable stream of changes that can feed a ClickHouse analytical layer without impacting OLTP performance.

For analytics at scale, the architecture that avoids query routing complexity is to stream everything to ClickHouse and treat your OLTP database as the source of truth for writes, with ClickHouse as the source of truth for reads. Dashboards, reports, and user-facing analytics all go through ClickHouse.

Tinybird for Postgres and SQL Server teams

Tinybird is managed ClickHouse that connects to both Postgres and SQL Server. CDC connectors capture changes from your existing OLTP database and sync them to Tinybird datasources in real time. SQL Pipes define the analytical queries. HTTP endpoints expose them to dashboards, products, and internal tools.

The contrast with building a CDC pipeline yourself: configuring logical replication in Postgres or setting up SQL Server CDC, managing a Kafka cluster, writing consumer code that handles schema evolution, standing up ClickHouse, and building an API layer on top is a multi-week project. Resend built a complete Marketing Analytics feature processing 100TB per month in two weeks using Tinybird, measuring 62ms p90 query latency in production without caching.

For clickhouse streaming analytics use cases where you need sub-second freshness on top of either database, the Kafka connector ingests change events as they are produced. For use cases where seconds-level freshness is sufficient, the Events API accepts direct HTTP writes from your application.

Each analytical query you write becomes a parameterized HTTP endpoint. Your Postgres or SQL Server database stays exactly where it is. The analytical layer scales independently. Your team writes SQL and ships features instead of maintaining infrastructure.