The fastest OLAP databases compared: DuckDB, MotherDuck, ClickHouse, Snowflake, BigQuery

TL;DR

• DuckDB leads single-node benchmarks. It reached #1 open-source on ClickBench with v1.4 LTS, thanks to a highly optimized vectorized engine and in-process embedding.
• MotherDuck is the managed cloud path. MotherDuck Mega finishes ClickBench in 5.9s, faster than ClickHouse Cloud (18.7s) and self-hosted ClickHouse (32.3s).
• ClickHouse wins at petabyte-scale event streams. Pick it for immutable telemetry, logs, and sustained Kafka ingestion if you have DevOps capacity for ZooKeeper and shard tuning.
• Snowflake and BigQuery are governance-first, not speed-first. Choose them for enterprise governance and secure sharing, not interactive query latency.
• MotherDuck fits AI and SaaS teams. Ducklings spin up in 100ms, isolate each tenant, and bill per second.

Why DuckDB is fastest on a single node

DuckDB wins single-node benchmarks on the strength of a vectorized execution engine. Rather than processing data one row at a time, it operates on batches of roughly 2,048 values at once, which keeps the CPU's cache and SIMD instructions saturated and strips out per-row interpretation overhead. Paired with a columnar storage layout, a query reads only the columns it touches and scans them in tight, cache-friendly loops. ClickHouse is also a vectorized columnar engine, so the two are closer in execution model than benchmark headlines suggest. DuckDB's single-node edge comes mostly from running embedded, with no cluster to coordinate, plus the engine optimizations shipped in v1.4 LTS.

The architecture removes overhead that distributed systems carry by default. DuckDB runs in-process, embedded directly inside your application, so a query never crosses a network boundary or coordinates with a separate cluster. There is no shard rebalancing, no serialization between nodes, and no round trip to a query engine. The CPU reads columnar data straight from memory and runs.

The benchmark numbers back the design. On October 9, 2025, DuckDB reached #1 on ClickBench on the strength of optimizations in v1.4 LTS, and under ClickBench's updated rules DuckDB stands as the top open-source system in hot runs, trailing only the closed-source research prototype Umbra. Adoption tracks the same curve. The Python package sees nearly 25 million monthly downloads on PyPI, and Stack Overflow's 2025 Developer Survey shows DuckDB usage jumping from 1.4% in 2024 to 3.3% in 2025.

Local DuckDB is fast, and MotherDuck is how you run that exact engine in production at any scale. MotherDuck provisions DuckDB on managed cloud instances called Ducklings, which scale vertically from Pulse up through Standard, Jumbo, Mega, and Giga as your data grows. For read-heavy traffic, you attach read-scaling replicas to spread concurrent dashboard queries across more compute. Each tenant gets an isolated Duckling through Hypertenancy, so one customer's heavy query cannot slow another's. None of this changes your SQL or your connection string, because MotherDuck speaks DuckDB's dialect and runs DuckDB under the hood.

ClickBench numbers: DuckDB vs ClickHouse vs the field

The numbers that anchor this whole comparison come from ClickBench, the analytics benchmark ClickHouse itself maintains. On the standard run, MotherDuck Mega finishes in 5.9 seconds, ClickHouse Cloud on a 64GiB 2-node setup takes 18.7 seconds, and ClickHouse self-hosted lands at 32.3 seconds. MotherDuck Mega beats both ClickHouse configurations outright, and it runs DuckDB under the hood.

That result is not a fluke. DuckDB v1.4 LTS reached #1 open-source on ClickBench hot runs, trailing only Umbra, a closed-source research prototype. For an embedded, single-node database to beat purpose-built distributed systems on a scan-heavy workload is the kind of result that should make you rethink your stack.

Here's the honest part. ClickBench runs 43 queries against a single flat table of roughly 100 million rows of web analytics data. Every query is an aggregation, filter, or string operation on that one table. There are no JOINs. So the benchmark tells you a great deal about telemetry, log analysis, and dashboard queries that hit one wide table, and it tells you almost nothing about star-schema BI where five or six tables join on every query.

ClickBench also runs queries sequentially, so it never measures concurrency or how a system holds up under many users at once. Jordan Tigani, MotherDuck's CEO, makes the sharper point that what you actually care about is time from idea to answer, not time from query to result.

Read the 5.9s number for what it is. DuckDB's engine is genuinely fast on scan-heavy single-table work, and MotherDuck delivers that speed as a managed service. Don't read it as a verdict on join-heavy analytics it was never built to measure.

DuckDB vs ClickHouse: a direct answer

The honest verdict is that ClickHouse wins on raw scan speed at petabyte scale, and DuckDB plus MotherDuck wins almost everywhere else for interactive analytics. Both are genuinely fast columnar engines. The difference comes down to SQL compatibility, cold-start behavior, billing, and how much operational work each one pushes onto you.

ClickHouse speaks its own SQL dialect, with case-sensitive functions and no full ANSI support. Together AI ran the standard TPC-DS suite against ClickHouse and found only 10 to 20 of the 99 queries ran without modification. DuckDB uses PostgreSQL-based, ANSI-compliant SQL, so your existing queries run out of the box. If you already have a BI tool or a library of standard SQL, ClickHouse means rewriting; DuckDB means copy and paste.

Cold start is the second sharp divide. ClickHouse Cloud takes more than a minute to spin up, so production deployments run 24/7 to dodge that delay. You pay for idle compute. MotherDuck Ducklings spin up in 100ms and scale to zero between queries, so you pay for compute you actually use.

The billing models follow from that. ClickHouse Cloud charges per-minute compute in 8GB increments plus compressed storage, which makes a monthly bill hard to predict. MotherDuck bills per second, one flat rate per instance size from $0.60 to $36 an hour, with no storage surcharge on analytical queries.

Tenant isolation is where the architectures part most clearly. ClickHouse Cloud shares infrastructure across tenants, so a heavy query from one customer can slow everyone else, the classic noisy-neighbor problem. MotherDuck's Hypertenancy gives each user or customer a dedicated Duckling with full compute isolation, so one tenant's workload cannot touch another's. On Postgres compatibility, ClickHouse requires the pg_clickhouse extension, while MotherDuck supports the Postgres wire protocol through a managed Postgres endpoint, so existing clients, BI tools, and applications connect without changes.

Head-to-head comparison table

The table below shows where each engine lands on the dimensions that decide a real deployment, not raw scan speed alone.

DuckDB wins on a laptop with zero setup. MotherDuck carries that engine to production while keeping the same SQL, and it isolates each tenant on its own compute. ClickHouse trades operational burden for petabyte-scale ingestion. Snowflake and BigQuery price and govern for the enterprise, not for sub-second interactive queries.

How MotherDuck's Hypertenancy changes the cost equation

Hypertenancy provisions a dedicated DuckDB instance, called a Duckling, for each user or customer, so one tenant's heavy query can never slow down another's. Traditional warehouses share a single cluster across every tenant, which forces you to overprovision compute for the whole pool to keep the busiest users happy. With Hypertenancy, each Duckling spins up in 100ms, runs in full isolation, and scales to zero when idle. You stop paying for compute the moment a tenant stops querying.

That isolation rewrites the per-user cost math. Josh Nakka, Co-Founder at PriceMedic, put it directly: "We were able to identify specific organizations, and even better, specific users, who need more compute. We can scale individually for each one, so we get a much faster UX without paying to scale an entire cluster." You match compute to the user who needs it instead of sizing one cluster for your worst case.

The billing minimum is where the difference becomes concrete. Snowflake charges a 60-second minimum every time a warehouse resumes, so a dashboard that fires ten independent sub-second queries against a freshly resumed warehouse can accumulate ten minutes of billed compute. MotherDuck bills by the second with a 1-second minimum. The same ten queries cost you roughly ten seconds of compute, not ten minutes.

The Pulse instance pushes this further for high-concurrency customer-facing analytics. Pulse meters on actual compute consumed in CU-seconds, at $0.60/hr, so idle time between queries is never billed. When your end users sit idle between clicks, which is most of the time in an embedded dashboard, you pay only for the milliseconds a query actually runs. The larger Ducklings, Standard through Giga, bill per wall-clock second and scale to zero when idle, which still beats Snowflake's per-activation minimum for everyday warehouse work.

For read-heavy workloads, you attach read-scaling replicas to individual instances and scale concurrent reads without provisioning a bigger cluster. The combination of per-user isolation, 100ms cold starts, and per-second or per-query billing is what makes interactive multi-tenant analytics economically viable. You serve thousands of concurrent users without paying for a fleet that sits mostly idle, which is exactly the bill that pushes teams off shared-cluster warehouses in the first place.

When to use each: four winning scenarios

DuckDB and MotherDuck fit four workloads cleanly. Each one below maps to a specific technical pressure, so find the description that matches your stack and you have your answer.

Connecting local DuckDB to MotherDuck: SQL snippet

Moving from local DuckDB to MotherDuck takes one change. You attach a MotherDuck database to your existing DuckDB session, and the same SQL keeps working against cloud data.

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-- Local DuckDB session, attach MotherDuck cloud
ATTACH 'md:my_database';

-- Query cloud data exactly like a local table
SELECT customer_id, sum(amount) AS revenue
FROM my_database.main.orders
GROUP BY customer_id;

The pattern worth seeing is Dual Execution, where a single query joins a local file on your laptop with a production table in the cloud. DuckDB pushes the cloud-side work to MotherDuck and runs the rest locally, so you test against real production data without copying it down.

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-- Join a local CSV against a cloud production table
SELECT l.event_id, c.plan_tier
FROM read_csv('local_events.csv') AS l
JOIN my_database.main.customers AS c
  ON l.customer_id = c.customer_id;

In Python the only edit is the connection string. You swap duckdb.connect() for duckdb.connect('md:my_database'), and every query you already wrote runs unchanged against cloud compute.

Pricing at a glance

MotherDuck splits into three plans, and most developers can test the whole thing before paying anything. The Lite tier is free, needs no credit card, and gives you 3 internal users, 2 service accounts, 10GB of storage, and 10 hours of Pulse compute a month. Business runs $250 per org per month plus usage, adds unlimited service accounts and all five instance sizes, and includes a 99.9% SLA and 90-day snapshot retention. Enterprise moves to custom fixed-cost capacity pricing with AWS PrivateLink and a HIPAA BAA.

Compute is billed by the second across five instance sizes, so you can match the machine to the job. Pulse runs $0.60/hr and meters on compute consumed in CU-seconds rather than wall-clock time, which means you pay only while a query actually runs. Standard is $2.40/hr, Jumbo $4.80/hr, Mega $12.00/hr, and Giga $36.00/hr, each billed per wall-clock second and scaling to zero when idle. Storage sits at $0.04 per GB per month on every tier.

For a customer-facing dashboard with users mostly idle between clicks, Pulse keeps the bill tied to real query time. For dbt runs or heavy joins, step up to Standard or Jumbo and pay per second while the work happens. You get a free 7-day Business trial to size your workload, and MotherDuck runs in four AWS regions: us-east-1, us-west-2, eu-central-1, and eu-west-1.

Conclusion

The decision comes down to your workload. Pick ClickHouse for petabyte-scale event streams where you have the DevOps capacity to run it, and pick Snowflake or BigQuery when enterprise governance outranks speed. For interactive analytics, embedded dashboards, SaaS workloads, and AI agents, DuckDB gives you the fastest single-node engine, and MotherDuck takes that speed to production without forcing you to learn a different SQL dialect or babysit a cluster.

The next step depends on where you start. If you've never touched DuckDB, install it and run a query against a local Parquet file. If you're already running DuckDB locally, MotherDuck is one connection string away from production. Add your token to the connection, swap duckdb for md:, and your existing queries run against scalable cloud compute with no rewrite.

FAQs

Is DuckDB faster than ClickHouse?
On ClickBench, DuckDB reached #1 open-source in hot runs with v1.4 LTS, and MotherDuck Mega finishes the benchmark in 5.9s versus 32.3s for self-hosted ClickHouse. The benchmark measures single-table scans without joins, so it favors DuckDB for telemetry and dashboards. ClickHouse still wins on petabyte-scale event streams where distributed nodes scale nearly linearly.

Can DuckDB handle production workloads?
Yes, and the adoption numbers back it. DuckDB sees nearly 25 million monthly PyPI downloads, and over 20 Fortune-100 companies run it. For cloud production, MotherDuck runs the same DuckDB engine under a serverless service, so you scale to Giga-sized instances without changing your SQL.

What is MotherDuck? MotherDuck is a serverless cloud analytics service that runs DuckDB under the hood and speaks DuckDB's SQL dialect. It adds Hypertenancy, dual execution across laptop and cloud, and per-second billing from $0.60 to $36/hr. You connect by changing one connection string from your local DuckDB.

DuckDB vs Snowflake for dashboards? MotherDuck bills with a 1-second minimum, while Snowflake charges a 60-second minimum per warehouse activation. A dashboard firing ten short queries against a freshly resumed Snowflake warehouse can rack up ten minutes of billed compute. MotherDuck also runs around 6x faster than similarly priced Snowflake instances on scan-heavy workloads.

Does MotherDuck support multi-tenancy? Yes, through Hypertenancy, which provisions a dedicated DuckDB instance per user or customer. Each tenant's queries run on isolated compute, so a heavy workload from one customer cannot degrade another. Ducklings spin up in 100ms and scale to zero when idle, which keeps per-tenant isolation affordable for customer-facing analytics.