In our quest for an effective user dashboard, we tried everything: ClickHouse, Snowflake, Databricks, BigQuery and even Postgres. Many of these big solutions were too expensive and too complex for our use case.
Josh Lichti
Co-Founder & CEO
Stop paying for every byte you scan
Surprise bills, shared-slot contention, partitioning just to control costs: BigQuery's pricing model punishes you for querying your own data. MotherDuck is flat per-second pricing — no scanning tax, no surprises.
Why MotherDuckWhy MotherDuckWhy MotherDuckWhy MotherDuckWhy MotherDuckWhy MotherDuckWhy MotherDuckWhy MotherDuck
Why MotherDuck
The data warehouse for builders
BigQuery charges for every byte scanned. LIMIT doesn't reduce your bill. Partitioning and clustering aren't features, they're cost management you have to do yourself. Teams routinely get surprised by five-figure bills from a single unoptimized query. MotherDuck flips the model: flat per-second compute pricing, no scanning tax, and per-user isolated compute.
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|---|---|---|
| Architecture | Serverless with slot-based execution. | Hypertenancy: each user gets an isolated Duckling that spins up in 100ms and scales independently. |
| Cost model | Per TB scanned ($6.25/TB On-Demand) or slot reservations. 10MB minimum per query. LIMIT doesn't reduce your bill. | Billed by the second, $0.60–$36/hr. No scanning tax. A COUNT(*) costs fractions of a cent. |
| Cost predictability | Bytes-scanned costs are hard to predict. A single bad query can cost thousands. Slot commits start at 50 slots/month. | Predictable per-second billing. No surprise bills. Cost scales with compute time, not data size. |
| dbt performance | MERGE scans your entire target table by default. Requires manual fine-tuning with incremental_predicates to avoid full scans. | Efficient incremental processing out of the box. Your team stops optimizing for the bill. |
| AI integration | MCP requires OAuth plus IAM roles, service accounts, and project-level permissions. Vertex AI and BigQuery ML — mature but GCP-only. | Bring the MCP client you already use. Each agent gets its own isolated Duckling, so a runaway agent doesn't contend for shared slots. |
| Dual execution | No equivalent. | DuckDB-Wasm runs in the browser for ultra-fast in-browser analytics. Join local and cloud data in one query. |
| Local development | No local option. Test against the service or not at all. | DuckDB runs on your laptop — same SQL, same engine. Change one connection string to deploy to cloud. |
| Ad-hoc cloud storage queries | External tables require schema definition and dataset setup. No equivalent to one-line read_csv/read_parquet on arbitrary S3/GCS paths. | SELECT * FROM read_parquet('s3://...') — explore any file in any bucket without setup. Same for CSV, JSON, Iceberg, Delta. |
| Cloud dependency | GCP-native. Data egress to other clouds: $90–150/TB. | Runs on AWS, reads from S3 and GCS. |
| Native business intelligence | Looker is a separate paid product. Looker Studio is free but limited. | Dives: agent-native data apps. Create any experience in React + SQL, then deploy internally or embed. |
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Faster, at a fraction of the cost
MotherDuck outperforms BigQuery on benchmarks — with a pricing model that doesn't penalize you for querying your own data.
Faster on ClickBench
- MotherDuck Mega completes 43 queries in 5.9s — 4.5x faster than BigQuery serverless (26.8s)
- MotherDuck Standard (28.4s) matches BigQuery speed at a fraction of the cost
- Consistent hot and cold performance — no slot warmup variability
A fraction of the cost — with no surprises
- Per-second billing vs per-byte scanning: your cost scales with compute time, not data size
- No surprise bills — a bad query costs seconds of compute, not thousands of dollars in scanned bytes
- No engineering overhead managing partitioning, clustering, and incremental_predicates just to keep costs down
Don't take our word for it
With MotherDuck working to solve amazing problems through data, our behaviors have changed because we know we don't have to pay enormous costs every time we run a query, so we've got almost limitless performance.
Ravi Chandra
CTO
Our data pipelines used to take eight hours. Now they're taking eight minutes, and I see a world where they take eight seconds.
Jim O'Neill
Co-Founder & CTO
Why teams switch from BigQuery
No per-byte scanning tax, no partitioning just to control costs, every user gets their own isolated compute. MotherDuck is flat compute pricing billed by the second — query freely without watching the meter.
Hypertenancy Architecture
BigQuery shares compute across all users — heavy scans from one analyst can queue others, and there's no user-level isolation. Hypertenancy gives every user their own isolated Duckling that spins up in 100ms, so your biggest query never blocks a teammate. Full isolation at the user level.
Aggressively Serverless
BigQuery On Demand charges $6.25 per TB scanned — LIMIT doesn't reduce your bill, and a stray COUNT(*) on a large table hits the same charge as a full scan. MotherDuck bills by the second on flat compute pricing. You know what a query costs before you run it.
Query in Natural Language
BigQuery's MCP requires IAM roles, service accounts, and project permissions before an agent can run a query. MotherDuck MCP is OAuth and you're in — and every agent session gets its own isolated Duckling, so a runaway agent can't contend for shared slots. Bring your own model, no extra charge.
Data Apps Included
BigQuery connects to Looker Studio for free but with limited features; full Looker is a separate enterprise purchase. MotherDuck Dives are included — build any visualization or data experience with an AI agent, deploy internally or embed in customer-facing applications.
Postgres Compatible
BigQuery uses its own SQL dialect with no Postgres wire protocol support — you need dedicated connectors or the BigQuery API for app integration. MotherDuck supports Postgres wire protocol natively, so existing Postgres-compatible clients, frameworks, and tools connect directly.
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Choose a comparison
01
MotherDuck vs Snowflake
Snowflake is a popular cloud data warehouse, but high costs and management complexity hold data teams back. Here's how MotherDuck and Snowflake compare.
02
MotherDuck vs Databricks
Databricks is built for ML pipelines and large-scale Spark workloads. If your team's primary job is SQL analytics, MotherDuck is purpose-built for it — no JVM, no cluster tuning, no DBU math.
03
MotherDuck vs Postgresql
Postgres is the fastest growing transactional database in the world, but it wasn't built for analytical queries: aggregations, joins across millions of rows, dashboard-powering scans. MotherDuck brings Postgres compatibility and sub-second queries to your data stack.
04
MotherDuck vs Redshift
VACUUM, WLM queues, cluster resizing: Redshift is a full time job. MotherDuck is ultra fast and fully serverless — just sub-second analytics, billed by the second.
05
MotherDuck vs Clickhouse
ClickHouse is fast, but speed alone doesn't ship products. MergeTree tuning, shard balancing, non-standard SQL, and operational overhead add up. MotherDuck gives you sub-second analytics with zero infrastructure to manage.
FAQS
It depends on your query patterns, but teams running frequent queries on growing datasets often see 40–60% savings. That's from eliminating per-byte scan charges, the engineering hours spent managing partitioning and clustering for cost control, and the surprise bills that come from unoptimized queries hitting production.
Most of it. DuckDB SQL is PostgreSQL-based and supports window functions, CTEs, lateral joins, and UNNEST. A small set of BigQuery-specific functions need updating: SAFE_DIVIDE(x, y) → x / NULLIF(y, 0), CURRENT_TIMESTAMP() → CURRENT_TIMESTAMP, and some array syntax differences.
Yes — this is one of MotherDuck's strongest differentiators. Hypertenancy gives every end user or customer their own isolated Duckling that spins up in 100ms and scales to zero when idle. No noisy neighbors, no shared-slot contention.
MotherDuck handles production scale — companies like Together AI run serious workloads on it. If your dataset is truly large, DuckLake's partitioned storage means queries only scan the partitions they need, so you get fast performance even at scale.
Yes. MotherDuck's Lite plan includes an allotment of 10GB of storage and 10 compute-hours per month. To start, just sign in — no credit card required.
Yes. The MotherDuck MCP Server connects any AI agent — Claude, ChatGPT, Cursor — directly to your data. Non-technical team members can ask questions in plain English. Each user and agent gets their own isolated Duckling, and results can be published as Dives — interactive, shareable data apps — without a separate BI tool.
Leave scanning charges behind
Fly faster on MotherDuck, for internal insights or in your application.
