BigQuery vs PostgreSQL

Quick verdict

BigQuery is a fully managed, serverless data warehouse on Google Cloud. You create datasets and tables and run SQL; Google allocates the compute, measured in slots (virtual CPUs), for each query. Its main dialect is GoogleSQL. Storage and compute are billed separately, and compute is charged either per TiB scanned (on-demand) or per slot-hour through the Standard, Enterprise and Enterprise Plus editions. It is a proprietary Google Cloud service.

PostgreSQL is the open source object-relational database server under the PostgreSQL License. You run it on a server or use a managed service; on Google Cloud that means Cloud SQL for PostgreSQL or AlloyDB for PostgreSQL. It is built for transactional work, with enforced constraints, MVCC, indexes and a large extension ecosystem. postgresql.org lists 18.6 as the current release.

Side by side

Key differences

Serverless warehouse versus a database server

With PostgreSQL you choose a machine size, and that capacity is there whether you use it or not; a large query competes with your application for the same CPU, memory and disk. With BigQuery there is no machine to choose. On the on-demand model you pay for the bytes each query processes; on editions you pay for slot capacity, with autoscaling and (on Enterprise and Enterprise Plus) optional baseline slots and commitments. Google lets you mix editions and on-demand per project.

That changes what "expensive" means. In PostgreSQL a badly written query is slow; in BigQuery on-demand it is also billed by data scanned, so SELECT * over a wide table, or a query that ignores the partition column, costs more. In our view, teams moving from PostgreSQL should set partitioning, clustering and cost controls (such as maximum bytes billed) from the start rather than after the first invoice.

Constraints, DML limits and transactions

BigQuery lets you declare primary and foreign keys, but only as NOT ENFORCED. Google states that BigQuery does not enforce them, that you must ensure the data conforms, and that queries over tables with violated constraints might return incorrect results. Uniqueness becomes the job of your load process.

-- BigQuery (GoogleSQL)
CREATE TABLE shop.customers (
  customer_id INT64,
  email       STRING,
  PRIMARY KEY (customer_id) NOT ENFORCED
);

-- PostgreSQL
CREATE TABLE customers (
  customer_id bigint PRIMARY KEY,
  email       text NOT NULL UNIQUE
);

BigQuery supports INSERT, UPDATE, DELETE and MERGE, and multi-statement transactions. But its documentation sets limits that matter for application-style workloads: it runs up to 2 mutating DML statements (UPDATE, DELETE, MERGE) per table concurrently and queues up to 20 more, and it advises against large numbers of individual row updates or inserts, recommending batching or the Storage Write API. PostgreSQL is built for exactly that pattern of many small concurrent changes.

GoogleSQL versus PostgreSQL SQL

Both are close to standard SQL for basic queries, but enough differs that queries rarely port unchanged. Upserts use MERGE in BigQuery and usually INSERT ... ON CONFLICT in PostgreSQL:

-- BigQuery (GoogleSQL)
MERGE shop.stock AS t
USING shop.stock_updates AS s
ON t.sku = s.sku
WHEN MATCHED THEN UPDATE SET qty = s.qty
WHEN NOT MATCHED THEN INSERT (sku, qty) VALUES (s.sku, s.qty);

-- PostgreSQL
INSERT INTO stock (sku, qty)
SELECT sku, qty FROM stock_updates
ON CONFLICT (sku) DO UPDATE SET qty = EXCLUDED.qty;

"Latest row per group" shows another difference. BigQuery has a QUALIFY clause that filters on window functions; PostgreSQL does not, but has DISTINCT ON:

-- BigQuery (GoogleSQL)
SELECT customer_id, order_id, ordered_at
FROM shop.orders
QUALIFY ROW_NUMBER() OVER (PARTITION BY customer_id ORDER BY ordered_at DESC) = 1;

-- PostgreSQL
SELECT DISTINCT ON (customer_id) customer_id, order_id, ordered_at
FROM orders
ORDER BY customer_id, ordered_at DESC;

Other differences worth knowing: table names are qualified as project.dataset.table and quoted with backticks; types are named INT64, FLOAT64, STRING, BYTES, ARRAY and STRUCT; dividing two INT64 values with / returns a FLOAT64 (PostgreSQL integer division truncates); and division by zero is an error unless you use SAFE_DIVIDE or IEEE_DIVIDE. Nested and repeated fields (arrays of structs) are a normal BigQuery modelling choice, where PostgreSQL would use a child table or jsonb.

Connecting them: federated queries and Datastream CDC

Federated queries. BigQuery's EXTERNAL_QUERY function sends a query to a Cloud SQL for PostgreSQL or Cloud SQL for MySQL instance (and, through a separate documented feature, to AlloyDB) and returns the result as a table you can join with BigQuery data, without copying it. The connection's location must match the instance location or be a multi-region of the same jurisdiction. The inner query runs on your operational database and uses its resources, so this suits lookups and small extracts rather than scanning large tables repeatedly.

-- BigQuery: join a warehouse table with live Cloud SQL data
SELECT c.customer_id, c.segment, o.first_order
FROM shop.customers AS c
JOIN EXTERNAL_QUERY(
  'my-project.us.pg_conn',
  '''SELECT customer_id, MIN(created_at) AS first_order
     FROM orders GROUP BY customer_id''') AS o
ON o.customer_id = c.customer_id;

Datastream. Google's managed change data capture service replicates PostgreSQL into BigQuery. Its documentation lists Cloud SQL for PostgreSQL, AlloyDB, Amazon RDS and Aurora PostgreSQL and self-managed PostgreSQL among supported sources, alongside MySQL, Oracle, SQL Server and others. In the default merge mode, BigQuery tables mirror source tables that have primary keys, with changes applied in the background within a configurable max_staleness that trades freshness against cost; tables without a primary key, and streams in append-only mode, keep every change event instead. Datastream cannot add or remove a primary key on a table already being replicated.

Do you need a warehouse yet?

If your reports run acceptably on PostgreSQL or a read replica, adding BigQuery brings a second dialect, a pipeline and a usage-based bill. Options in between include partitioning and materialised views in PostgreSQL, columnar extensions, DuckDB reading PostgreSQL directly, or a columnar server such as ClickHouse; see DuckDB vs PostgreSQL, ClickHouse vs PostgreSQL and the general overview in data warehouse vs database.

BigQuery earns its place when data from several systems needs joining, history grows large, many people query it, or analytical load is hurting the application. If you are comparing warehouses, see BigQuery vs Redshift and Snowflake vs PostgreSQL.

Pricing and licensing

BigQuery bills compute and storage separately. Compute is either on-demand, charged per TiB of data processed by each query (rounded up to the nearest MB, with a minimum of 10 MB per table referenced and per query; cached results and failed queries are not charged), or capacity-based through the Standard, Enterprise and Enterprise Plus editions, charged per slot-hour with autoscaling; Enterprise and Enterprise Plus add 1-year and 3-year commitments. Storage is charged per GiB for active and long-term data, on a logical or physical billing model. Streaming inserts and the Storage Write API have their own rates.

As one example, the BigQuery pricing page lists on-demand queries for US regions at USD 6.25 per TiB in October 2026, with the first 1 TiB per month free and the first 10 GiB of storage per month free. Slot-hour prices vary by edition, commitment and region; use Google's pricing calculator. The BigQuery sandbox needs no credit card or billing account and gives the same 1 TiB of monthly query processing and 10 GiB of storage, but tables expire after 60 days and DML and streaming are not supported. Datastream is billed separately under its own pricing.

PostgreSQL is free under the PostgreSQL License. On Google Cloud, Cloud SQL and AlloyDB are billed by provisioned compute, storage and related resources; other providers price managed PostgreSQL similarly.

Pricing checked on the vendors' official pages on 7 October 2026. Prices change; confirm before buying.

Where each one leads

Limitations

When to choose each

When neither is right

• You are on AWS: see Redshift vs PostgreSQL and BigQuery vs Redshift.
• You only need analytics on files or inside a pipeline: see DuckDB vs BigQuery and DuckDB vs PostgreSQL.
• You need real-time analytics on events with an open source engine you can self-host: see ClickHouse vs BigQuery.
• You want managed PostgreSQL with stronger analytical features on Google Cloud: see Cloud SQL vs AlloyDB.

Final recommendation

Frequently asked questions

Sources

• BigQuery pricing
• BigQuery: Understand editions
• BigQuery: Sandbox
• BigQuery: Primary and foreign keys
• BigQuery: Data manipulation language
• BigQuery: GoogleSQL operators
• BigQuery: GoogleSQL query syntax (QUALIFY)
• BigQuery: Cloud SQL federated queries
• BigQuery: AlloyDB federated queries
• Datastream overview
• Datastream: BigQuery destination
• PostgreSQL home page (current releases)
• PostgreSQL: INSERT (ON CONFLICT)
• PostgreSQL License

Checked October 2026.

How we research comparisons: our editorial method.