ClickHouse vs PostgreSQL: which to choose

ClickHouse vs PostgreSQL: when each one is the right choice

Keep PostgreSQL for transactional work and move only the analytics out when reporting starts to slow operational writes. Many teams reach this point when dashboards and ad hoc reports scan large parts of the same tables that order processing, billing or case management write to. ClickHouse is an Apache-2.0 licensed, column-oriented SQL database built for online analytical processing. Column storage speeds filtering and aggregation over many rows, but it makes retrieving single rows slower, so it is a poor home for the lookups and frequent small updates that PostgreSQL handles well.

Choose ClickHouse when the dominant query reads many rows, filters on a few columns and aggregates them, for example daily totals across years of events. Keep PostgreSQL when the workload is mixed, when application code depends on transactions and single-row lookups, or when data volume is modest enough that indexes and read replicas solve the reporting problem. The decision turns on four factors: query shape, data volume, team skills and operating capacity. Ask your DBA and application owners to list the ten slowest reporting queries with their row counts, then ask which of them would still be slow after tuning PostgreSQL indexes or moving reports to a replica.

Operating capacity matters as much as performance. ClickHouse publishes stable releases roughly monthly and LTS releases twice a year, with each LTS line supported for a year, so an estate that runs ClickHouse must plan upgrades on that cadence, not on a multi-year schedule. PostgreSQL ships a new major version about once a year and supports each for five years, so its upgrade rhythm is slower. Confirm with your platform team whether they can absorb the faster ClickHouse cycle, and get a written scope for backup, upgrade testing and monitoring before adding a second database engine.

Which database fits which analytics scenario

This table maps common workloads to the database that usually fits better, tied to column storage versus row-oriented access. Read it by matching your query shape and write pattern first, then use the measure column to confirm the answer at your own volume before choosing.

Scenario	Better fit and reason	Depends on volume or query shape?	What to measure
Dashboards over large event or log tables	ClickHouse. Column storage reads only the few columns a dashboard aggregates, which suits scans over many rows.	Yes. At small row counts and simple filters, PostgreSQL with good indexes and partitioning often performs well enough.	P95 query latency on your real dashboard queries at production row counts, and rows scanned per query.
Ad hoc aggregations across years of history	ClickHouse. Grouping and filtering over many rows is the workload column-oriented storage is built for.	Yes. Ad hoc queries with unpredictable filters and joins on wide tables are where the gap usually shows.	Bytes read and runtime for a sample of analyst queries, including joins to dimension tables.
Operational order or account records	PostgreSQL. Row-oriented access returns a complete record quickly, and constraints, transactions and foreign keys are core to its design.	Rarely depends on volume. Record lookups by key favor row storage at almost any size.	Point lookup latency by primary key, and whether your integrity rules need foreign keys and transactional guarantees.
Mixed transactional and reporting work on one instance	PostgreSQL for the first stage. Reporting queries can compete with transactions for CPU, memory and I/O, so isolate reporting early if the load grows.	Yes. Light reporting on a small database rarely needs separation. Heavy scans during business hours usually do.	Transaction latency during peak reporting runs, and whether reports slow writes.
High-frequency single-row updates	PostgreSQL. ClickHouse is not designed for frequent in-place updates of individual rows, and column storage makes single-row retrieval slower.	Rarely depends on volume. Row updates are a poor match for ClickHouse at any scale.	Update rate per second, and how often your design needs to change an existing row rather than append a new one.
Append-heavy ingestion with few updates	ClickHouse. Insert-oriented ingestion of immutable facts fits its storage model.	Yes. Batch size and insert frequency shape ingestion efficiency, so test the batch pattern your pipeline will produce.	Insert throughput at your batch sizes, and merge behavior over time.
Analytics that must stay consistent with live records	Often both. Keep records in PostgreSQL and replicate or load facts into ClickHouse for analysis, accepting some lag.	Yes. Freshness requirements decide how complex the copy pipeline needs to be.	Acceptable data lag for each report, and the failure behavior of the copy job.

How column storage changes what each database does well

ClickHouse and PostgreSQL store and read data in fundamentally different ways, and that choice shapes schema design, indexing, and day-to-day operations. The cards below cover the storage model, the query patterns each favors, and the operational work each one places on your team inside your own infrastructure.

Column storage for analytical scans

ClickHouse is a column-oriented SQL database built for online analytical processing. Storing each column separately speeds filtering and aggregation across many rows, because queries read only the columns they touch. Ask your team how many queries scan wide date ranges and how many fetch one record by key, since that ratio decides which engine carries the load.

Single-row lookups stay slower

The same column layout makes retrieving a single full row slower in ClickHouse, because values for one record sit in separate column files. PostgreSQL, a general-purpose relational database, is built for mixed transactional access where a row is fetched, updated and locked quickly. If your application reads or updates individual customer records constantly, keep that system of record in PostgreSQL.

Schema design follows the query

ClickHouse tables are typically designed around the sort key and partitioning that match your most common filters, so the schema is tuned to specific query patterns from the start. PostgreSQL schemas are normalized for integrity and changed through migrations as the application evolves. Before migrating, document the top queries and confirm which schema each engine must serve.

Indexing habits differ

PostgreSQL relies on B-tree and other indexes to find individual rows quickly, and teams add them as access patterns emerge. ClickHouse relies on sort keys and sparse primary indexes that narrow reads to ranges of data, and it does not behave like a row store for point lookups. Test your heaviest queries on realistic volumes in a staging environment before deciding which index strategy you need.

Release and support cadence

ClickHouse publishes stable releases roughly monthly and LTS releases twice a year, with each LTS supported for a year. PostgreSQL ships one major version about once a year and supports each for five years, with PostgreSQL 18 supported until November 14, 2030. Your upgrade planning must match these different cycles, so confirm which ClickHouse line and PostgreSQL major version your compliance scope requires.

Operating both in your environment

Running ClickHouse beside PostgreSQL means two engines to deploy, back up, patch, monitor and restore inside your own cloud accounts, data centers or air-gapped networks. A common split keeps PostgreSQL as the transactional source and replicates analytical data into ClickHouse. Ask for the data movement design, backup and restore tests for each engine, and the ownership model for upgrades before work starts.

Capability differences to verify against your workload

This table sets ClickHouse's column-oriented analytical design against PostgreSQL's general-purpose relational design across the capabilities that most often decide the choice. Use the supplied architectural facts as a starting point, then confirm the rows marked for verification in each vendor's current documentation and in a proof of concept built on your own data volumes and query patterns.

Capability	ClickHouse	PostgreSQL
Query model	Online analytical processing (OLAP) SQL, optimized for scanning and aggregating many rows	General-purpose relational SQL, suited to mixed transactional and reporting queries
Storage layout	Column-oriented, which speeds filtering and aggregation over many rows	Row-oriented storage as its core layout, which favors reading and writing whole records
Single-row retrieval	Slower by design, since column storage must reassemble individual rows	Fast point lookups by key when indexes fit the access pattern
Licensing	Apache-2.0	PostgreSQL License, a liberal license similar to BSD or MIT
Transaction guarantees	Confirm current transactional behavior in ClickHouse documentation before relying on multi-statement consistency	Mature transactional model; confirm isolation settings for your application
Update and delete behavior	Confirm how row-level updates and deletes are applied and how quickly they take effect in your version	Standard row-level updates and deletes with immediate consistency semantics
Joins at scale	Confirm join types, memory limits and performance on your largest tables in a proof of concept	Broad join support; confirm plan behavior on your largest queries with EXPLAIN
Extensions	Confirm available functions, engines and integrations for your stack	Large extension ecosystem; confirm each extension's license and support status
Typical use	Analytical store for event, log and reporting data at high volume	System of record for applications, with analytics added as workloads grow

What each license permits and what is paid for

The core ClickHouse database is licensed under Apache-2.0, a permissive open-source license. In practice, your team can download, run, modify and redistribute the database without paying a license fee to the project. The licensing question that matters for ClickHouse is therefore not whether you can use the software, but how you will keep it current and who will operate it.

Release channels: ClickHouse publishes stable releases roughly monthly and LTS releases twice a year. Each LTS release is supported for a year, so your upgrade planning should follow the LTS line rather than the monthly stream if you want longer support windows.
Current lines: The supplied release information lists 26.9 as the current stable line and 26.8 as the current LTS line. Confirm which line you intend to standardize on before you write runbooks.
Ownership of operations: Running ClickHouse in your own infrastructure means your staff or a services provider handles upgrades, backups, security patching and capacity planning.
Commercial options: Managed or commercial offerings, support contracts and paid training are separate decisions. Their terms are set by the vendor and are not implied by the Apache-2.0 license.

Before you commit, ask the vendor which edition details, support terms and release windows apply to your deployment, and map each LTS support window against your change calendar.

How ClickHouse and PostgreSQL release cycles affect upgrade planning

The release cadence of each database sets the rhythm of your upgrade calendar, and the two products run on very different clocks. ClickHouse publishes stable releases roughly monthly and LTS releases twice a year, with each LTS line supported for a year. The current lines at the time of writing are 26.9 stable and 26.8 LTS, so a team that runs ClickHouse in production will face a steady stream of versions to evaluate. PostgreSQL works differently: it ships a new major version about once a year and supports each major for five years. PostgreSQL 18 was first released on September 25, 2025 and is supported until November 14, 2030, which gives a planning horizon measured in years rather than months.

The practical question is which track to run. A stable track suits a team with a regular release process, automated test coverage for analytical queries and enough engineers to validate a new build each month. An LTS track suits teams that want fewer, larger upgrades and can absorb a yearly cycle of testing. Because each ClickHouse LTS line is supported for only a year, a team that stays on LTS still upgrades at least annually, so the calendar is not much lighter than it first appears. Keep a written record of which track each cluster follows, and check whether the stable and LTS lines you plan to use are still within their support window before scheduling work.

Before you commit to a calendar, confirm the current support dates for both products on the vendor pages, since end-of-support dates and release lines change over time. The ClickHouse FAQ on versions in production explains the LTS policy, and the PostgreSQL versioning policy sets out major version support. Ask your DeployOpen team or your own platform owners to estimate testing capacity for a monthly ClickHouse cycle versus a yearly PostgreSQL major upgrade, and to name who approves each change in a regulated environment.

What drives the running cost of each database

Running ClickHouse and PostgreSQL costs more than their licenses, which are zero for both. The bill comes from the servers and storage they need, the skills to run them, who supports them, and the upgrades each release cycle forces. The cards below cover each component and the questions to settle before asking for a quote.

Compute and storage footprint

Infrastructure is usually the largest recurring line. ClickHouse scans and aggregates large column ranges, so its cost tracks CPU, memory and disk throughput during query peaks. PostgreSQL serves row lookups and transactional writes, so its cost tracks instance size, WAL volume and storage for indexes. Ask who pays for the cloud accounts or data center capacity, whether it is reserved or on demand, and how replicas and backups are sized.

Copies needed for analytics

Many teams keep a PostgreSQL system of record and copy data into ClickHouse for reporting. Each copy adds storage, a replication or ingestion pipeline, and monitoring that someone must run. This cost recurs with data volume and change rate. Confirm how many copies each workload needs, how fresh they must be, and whether the pipeline is inside the quote or owned by the internal team.

Schema and query skills

Columnar design changes how tables are built. ClickHouse depends on choices such as sort keys, partitioning and data types, and on query patterns that avoid expensive joins. PostgreSQL needs index design, vacuum tuning and connection management. Most of this cost is one-time learning and design work, but it recurs when new workloads arrive. Check whether your staff already have these skills or whether the scope must include training.

Support and who provides it

Community forums and vendor documentation cost nothing in licenses but leave response times and incident ownership with your team. A commercial support contract, a managed service or a retained partner moves that responsibility, and its fee recurs. Ask for the support model in writing: who is on call, what severity levels exist, and which versions are covered. Confirm these terms with the vendor or provider, since they are not stated here.

Upgrade effort per release cycle

Upgrades carry recurring labor. ClickHouse publishes stable releases roughly monthly and LTS releases twice a year, with each LTS supported for a year, so a stable-track deployment upgrades far more often than an LTS one. PostgreSQL ships one major version a year and supports each for five years. Ask who tests upgrades, how long rollback windows run, and whether DeployOpen's upgrade retainer or your team owns this work.

Editions and managed services

Both databases are open source under permissive licenses, so the choice is between self-operating and paying a managed or hosted service. Managed options replace staff time with a recurring fee that scales with compute and storage. Compare the fee against the staffing and on-call cost it removes. Ask whether the managed service matches your data residency and air-gap requirements, and what happens to data if you exit.

Moving analytics off PostgreSQL step by step

This sequence moves heavy reporting queries to ClickHouse in controlled stages while PostgreSQL stays the system of record, so each cutover can be checked and reversed before traffic depends on the new store.

Inventory slow queries and access patterns
The data platform team and the DBA pull query logs from PostgreSQL and list the reports, dashboards and jobs that consume the most time or lock the most resources. For each one, record the tables touched, the columns read, the filters and grouping keys, how often it runs and whether it scans many rows or fetches single records. This matters because ClickHouse rewards wide scans and aggregations over many rows, and it is a poor fit for point lookups and frequent small updates. The step is done when the team has a ranked list that separates analytical candidates from transactional queries that should stay in PostgreSQL. Ask your DBA to confirm the log settings and retention needed to capture this history before the inventory starts.
Choose which tables to copy
Using the inventory, the team selects the smallest set of tables that serves the top analytical workloads, usually append-heavy fact tables and their reference dimensions. Each candidate gets a documented owner, a freshness requirement agreed with the report consumers, and a note on whether the source rows are ever updated or deleted in place, since that affects how changes are replicated. Tables that hold the system of record for transactions often stay in PostgreSQL with only derived aggregates copied. The step closes with a signed-off table list and a data classification check, so no sensitive column is copied to a new store without an owner's approval.
Define the ClickHouse schema for columnar access
The data engineers translate each selected table into a ClickHouse table design. This means choosing a sort key that matches the most common filter and grouping patterns, selecting column types that compress well, and deciding whether any data needs partitioning by time. PostgreSQL B-tree indexes do not carry over, and the team should not assume they will. Denormalization is often considered here, but it adds consistency work that someone must own. The step is complete when a reviewed schema exists for each table, with the sort key and types justified against the inventory, and a test load into a non-production ClickHouse instance has run successfully.
Rebuild queries and dashboards in ClickHouse SQL
Analysts and engineers port each prioritized query to ClickHouse SQL. Simple SELECT, WHERE, GROUP BY and join logic often moves with small edits, but functions, date handling, window behavior, and PostgreSQL-specific syntax need manual review. Stored procedures, triggers and PostgreSQL extensions do not transfer and must be rewritten or retired. Each BI dashboard is then repointed to the new data source, which is where the connector, permissions and caching configuration must be rebuilt. The step is done when every ported query has a version in source control and each dashboard runs against ClickHouse in a staging environment.
Validate results against PostgreSQL
The team runs each ported query against both stores over the same time window and compares row counts, aggregate totals, null handling and rounding. Differences are traced to their cause, whether that is replication lag, type conversion, timezone handling or a real logic error. Validation should include the boundary cases that reports rely on, such as late-arriving rows and month-end cutoffs. Business owners sign off on the results for the dashboards they use. The step closes with a documented reconciliation report for each query, and a rule for what difference is acceptable. Confirm with the business owners what tolerance they accept before this work begins.
Cut over reporting traffic in stages
Reporting traffic moves to ClickHouse one dashboard group at a time, starting with the lowest-risk consumers. The team keeps the PostgreSQL version of each report available so it can be switched back quickly if a discrepancy appears. Monitoring tracks query latency, load errors, data freshness and access logs on both systems. Operators should confirm how the team will handle a ClickHouse outage, since reports will fail or fall back depending on the design. The step is done when all in-scope dashboards read from ClickHouse, the fallback path has been tested, and the on-call runbook names an owner for the reporting store.
Keep PostgreSQL as the system of record
PostgreSQL remains the authoritative source for transactions and for any data the business must be able to restore or audit. Changes flow into ClickHouse through a replication or batch process that the team owns, and every copied table has a documented refresh method, a check that counts and reconciles rows, and an alert when the copy falls behind. Backups, retention and access controls are defined for both stores, and the ClickHouse copy is treated as derived data that can be rebuilt. The step is complete when the runbook describes how to rebuild ClickHouse from PostgreSQL and a rebuild has been rehearsed.
Plan for the cost of running two stores
Once the cutover is complete, the organization carries two database engines, two sets of upgrades and two skill sets. Staff need working knowledge of ClickHouse table design, its release cadence and its LTS support window, which the ClickHouse FAQ describes as one year for each LTS line, alongside existing PostgreSQL expertise. Keeping the copies consistent is ongoing work that recurs with every schema change in the source. The team should document who is paid for this effort, whether internal staff or an external partner under a fixed-scope or retainer agreement, and ask any implementation partner to price schema-change handling and version upgrades separately from the initial build.

Where ClickHouse projects usually run long, and running both side by side

Implementing ClickHouse is rarely a single installation step. The work spans data ingestion design, schema modeling for columnar storage, query rewriting for dashboards and reports that were written against PostgreSQL, access control, backup and restore, monitoring, and an upgrade routine tied to ClickHouse's release tracks. ClickHouse publishes stable releases roughly monthly and LTS releases twice a year, with each LTS line supported for a year, so the team needs a written decision on which track it runs and how quickly it moves between minor versions. Confirm the current stable and LTS lines against the ClickHouse releases page before the scope is written, since the lines change every few months.

Projects usually run long in four places. The first is data pipeline reliability: replicating changes from PostgreSQL into ClickHouse must handle late data, retries, schema drift and backfills, and each of these is easy to underestimate. The second is dashboard rebuilds, because queries and semantic definitions written for row-oriented PostgreSQL often need rewriting to suit columnar storage. The third is skills gaps, since ingestion, table engine choices and operational tuning are unfamiliar to teams that have only run PostgreSQL. The fourth is validation of results, where totals in ClickHouse must be reconciled against the system of record before anyone trusts a report. Ask for each of these to appear as a named deliverable with acceptance criteria in the written scope, not as an allowance inside a general implementation line.

Running both side by side works best when PostgreSQL stays the system of record for transactional data and ClickHouse serves analytics as a derived copy. Write down which tables are replicated, how often, who owns the pipeline, and what happens to ClickHouse when the pipeline stops. Plan backup and restore for each store separately, because restoring ClickHouse from PostgreSQL may be the recovery path rather than a separate backup. Set access control in both systems so that analytics users cannot reach transactional data they should not see. Ask your DeployOpen team to confirm these decisions in writing, and to state which costs are one-time (design, build, validation) and which recur (retainer support, upgrades, testing) in the quote.

How much does ClickHouse cost to run next to PostgreSQL, and what does the work need from us?

What actually drives the cost of running ClickHouse alongside PostgreSQL?

The cost is driven mostly by the infrastructure that holds analytical data, the people who operate it, and the work to keep data moving between the two systems. ClickHouse is open source under Apache-2.0, so there is no license fee for the database itself. The recurring costs are compute and storage in your own cloud account or data center, monitoring, backups, and the engineering time to run ingestion from PostgreSQL. Costs rise with data volume, query concurrency, retention periods and the number of pipelines. Ask any provider to split the quote into one-time build work and recurring operating work, so you can see which part grows with your data.

How long does it take to move analytics off PostgreSQL?

No duration can be stated here without a scoped plan, and the time depends on the factors that shape the work. The main ones are how many reports and queries currently run on PostgreSQL, how much history must be backfilled, how many source systems feed the pipelines, and whether dashboards need to be repointed. Teams that keep PostgreSQL for transactions and add ClickHouse for analysis usually have a smaller migration than teams that want to retire PostgreSQL reporting entirely. Ask for a written scope with deliverables and milestones before agreeing to any timeline, since DeployOpen engagements are typically fixed-scope and agreed in writing before work starts.

What does the work need from our internal team?

Your team needs to supply the people who know the data and the systems that will be touched. In practice that means someone who owns the PostgreSQL schemas and the business meaning of key tables, someone who manages your cloud or data center environment and access controls, and someone who can approve changes to dashboards and downstream consumers. Security and compliance staff should review data classification, encryption and retention decisions early. Plan for these people to be available for reviews and testing, because the work stalls when decisions wait on them.

Can our own engineers run ClickHouse next to PostgreSQL without outside help?

Yes, many platform teams can, provided they have operational experience with columnar databases and the capacity to own them after go-live. The areas that usually need deliberate skills are table design for ClickHouse's sorting and partitioning model, ingestion patterns, backup and restore testing, and access control. Your team should also plan for release management, since ClickHouse publishes stable releases roughly monthly and LTS releases twice a year, with each LTS line supported for a year. If your engineers have not run a columnar store before, the honest choice is usually a short, scoped assessment followed by a handover, not an open-ended dependency.

Should PostgreSQL stay the system of record when ClickHouse is added?

In most cases PostgreSQL should stay the system of record for transactional data, with ClickHouse holding a copy shaped for analysis. This keeps the write path that your applications already depend on and avoids forcing a new database into operational workflows. The cost of this choice is the ongoing pipeline that keeps ClickHouse current, along with reconciliation checks to confirm row counts and totals match. Moving the system of record is a larger project with its own application changes, so it should be a separate decision with its own scope. Confirm with your application owners which tables are authoritative before any design work starts.

How do we handle upgrades on each ClickHouse and PostgreSQL release cycle?

The two databases follow different cycles, so the upgrade plan must cover both. PostgreSQL ships a new major version about once a year and supports each major version for five years, with PostgreSQL 18 supported until November 14, 2030. ClickHouse publishes stable releases roughly monthly and LTS releases twice a year, supported for a year. Choose one ClickHouse line for production, usually an LTS line, and schedule upgrades on a fixed cadence with test environments that match production. Upgrade work recurs, so ask whether a quote covers one-time upgrade runbooks only or ongoing upgrades under a retainer.

What should we ask for in a written quote for this work?

A useful quote separates the one-time work from the recurring work and states what is excluded. Ask for the scope in writing, including the source systems, pipelines, dashboards and environments covered. Ask for deliverables such as architecture documentation, runbooks, backup and restore tests, and security configuration evidence. Ask who performs each task, what your team must provide, what happens if scope changes, and how upgrades and ongoing support are billed. Ask for the assumptions behind any timeline. DeployOpen does not publish a price list, so the quote follows from the written scope rather than a standard rate card.

Does the quote include security testing and compliance evidence for the new database?

It depends on what you ask for, so confirm it in the scope. DeployOpen can run security testing on the systems it builds, including infrastructure and applications, and it can prepare systems, controls and evidence for compliance assessments. It does not issue audit reports, attestations or certifications; independent auditors and certification bodies do that work. Ask whether testing covers the ClickHouse cluster, the ingestion path, access controls and backups, and whether evidence will be organized against the control frameworks your auditor uses.

How DeployOpen helps choose, implement or migrate between these databases

DeployOpen plans, implements, integrates, hardens, backs up and documents self-hosted open-source platforms inside the client's own infrastructure, so a ClickHouse or PostgreSQL decision is tested against your actual environment before anything is built. Engagements are agreed as fixed-scope work with written deliverables and timelines.

How we scope

Map which workloads stay on PostgreSQL transactions and which move to ClickHouse analytics, with query shapes and data volumes recorded.
Write the scope in advance, naming deployment location, ingestion path, retention rules and the reporting tools that connect to each database.
Confirm with your team the ClickHouse release line you would run, including stable versus LTS choice and the upgrade cadence you can sustain.

How we build

Implement the chosen architecture in your cloud accounts, data centers or air-gapped network, with replication and access controls configured per the agreed scope.
Set up the sync or replication path between PostgreSQL and ClickHouse and test that analytical results match source data for the agreed queries.
Run security testing on the database endpoints, connection credentials and any API layer that exposes analytical data to users.

How we hand over

Document the architecture, configuration, backup and restore procedures, and the steps needed for a version upgrade on each database.
Deliver evidence for compliance readiness, such as access configurations and logging settings, for review by your independent auditor.
Walk your platform and data teams through operation so that day-to-day running does not depend on DeployOpen staff.

How we support

Offer ongoing support, upgrades and continuous testing as a retainer once the initial scope is complete and handed over.
Plan version upgrades for both databases against their published release and support cycles, agreed in writing before each change.
Ask any provider, DeployOpen included, to confirm support windows and upgrade responsibilities in the quote before work starts.

ClickHouse vs PostgreSQL