We called it the blue bar of tyranny. It was taking a minute to load a list of 100 employers just to filter on.
Greg Inman
CTO
Sub-second analytics, no cluster management
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.
Why MotherDuckWhy MotherDuckWhy MotherDuckWhy MotherDuckWhy MotherDuckWhy MotherDuckWhy MotherDuckWhy MotherDuck
Why MotherDuck
The data warehouse for builders
Redshift teams spend hours every week on VACUUM scheduling, ANALYZE jobs, WLM queue tuning, cluster resizing, and concurrency scaling policies — before a single business question gets answered. MotherDuck eliminates all of that. Serverless, sub-second analytics with zero operational overhead. Connect and query. That's it.
 | ||
|---|---|---|
| Architecture | Shared-nothing clusters with leader + compute nodes. Provisioned or serverless. | Hypertenancy: each user gets an isolated Duckling that spins up in 100ms and scales independently. |
| Cost model | Provisioned: pay-per-node (~$0.38–$13.04/hr). Serverless: $0.375/RPU-hour. Always-on clusters add up fast. | Billed by the second, $0.60–$36/hr. No clusters to manage. Customers see 40%+ cost savings. |
| Maintenance | VACUUM, ANALYZE, WLM tuning, cluster resizing — all on you. Ongoing engineering overhead. | Entirely managed — just choose your instance size. |
| AI integration | ML via SageMaker integration. No native MCP or agent support. | Bring your own agent via MCP. Add token, start querying. No intermediary layer. |
| Query performance | ClickBench: 13.2s (ra3.16xlarge, 4-node). Cold runs: 176.5s. | ClickBench: 5.9s (Mega). Cold runs: 9.8s. 2.2x faster hot, 18x faster cold. [See results →](https://benchmark.clickhouse.com/#system=+erk) |
| 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. Develop against the cluster or not at all. | DuckDB runs on your laptop — same SQL, same engine. Change one connection string to deploy to cloud. |
| S3 data access | Redshift Spectrum: additional complexity and $5/TB scan charges. | Query S3 natively, no extra charge. |
Read the story
Read the story
Read the story
Read the story
Read the story
Faster, at a fraction of the cost
MotherDuck outperforms Redshift on benchmarks — without the cluster management or maintenance overhead.
Faster on ClickBench
- MotherDuck Mega finishes 43 queries in 5.9s — 2.2x faster than Redshift's best (ra3.16xlarge 4-node, 13.2s)
- Cold query performance: MotherDuck Mega 9.8s vs Redshift 176.5s — 18x faster
- Data loading: MotherDuck loads 75GB in ~1 minute vs Redshift's 30–42 minutes
A fraction of the cost
- MotherDuck Mega ($12/hr) vs Redshift ra3.16xlarge 4-node (~$40/hr) — 70% cheaper, 2.2x faster
- MotherDuck Standard runs a full ClickBench session for ~$0.06 — Redshift Serverless costs ~$0.26+ for the same
- Zero engineering overhead: no VACUUM scheduling, WLM tuning, or cluster sizing reviews (~$18K/yr saved)
Don't take our word for it
It takes a lot of work to optimize Redshift. And basically, we had to scale for that. And it wasn't cost efficient.
Hidde Stokvis
COO
There's an error we'd frequently hit: 'this query is beyond the scale factor.' You'd have to upgrade the whole cluster. It's an expensive error with no other fix.
Josh Nakka
Co-Founder
Why teams switch from Redshift
No VACUUM schedules, no WLM queue tuning, no cluster resizing. MotherDuck is fully serverless and billed by the second — so your data team answers business questions instead of managing infrastructure.
Hypertenancy Architecture
Redshift clusters are shared — COPY jobs, VACUUM operations, and heavy analytical queries compete for the same resources. Hypertenancy gives every user their own isolated Duckling with a 100ms cold start, so a bulk load never blocks your analysts. No WLM queues, no resource groups.
Aggressively Serverless
Redshift clusters run 24/7 whether you're querying or not. Concurrency scaling and Serverless exist but add billing complexity. MotherDuck scales to zero between queries and bills by the second — no cluster sizing, no reserved instance math, no idle compute costs.
Query in Natural Language
Redshift has no native MCP or natural language query interface — you're writing SQL or wiring up a BI tool. MotherDuck connects to any AI agent via the MCP Server, so you can query, explore, and manage your warehouse in natural language. Bring your own agent, no extra charge.
Data Apps Included
Redshift integrates with Amazon QuickSight for dashboards, but it's a separate service with its own pricing and limitations. MotherDuck Dives are included — build any visualization or data experience with an AI agent, deploy internally or embed in customer-facing applications.
Postgres Compatible
Redshift diverged from PostgreSQL years ago — JDBC/ODBC drivers exist, but edge cases with Postgres clients break regularly. MotherDuck fully supports the Postgres wire protocol, so any Postgres-compatible client, ORM, or third-party tool connects without modification.
More ComparisonsMore ComparisonsMore ComparisonsMore ComparisonsMore ComparisonsMore ComparisonsMore ComparisonsMore Comparisons
More Comparisons
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 Bigquery
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.
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 workload, but teams typically see 40–70% savings — and that's before accounting for the engineering hours you'll get back from eliminating VACUUM, ANALYZE, WLM tuning, and cluster sizing.
Very closely. Both Redshift and DuckDB SQL are PostgreSQL-based, making this one of the smoothest migrations. Main changes: remove distribution/sort key syntax (DuckDB handles this automatically), swap a few proprietary functions like LISTAGG → STRING_AGG and GETDATE() → NOW().
Yes, in most cases. MotherDuck reads Parquet, CSV, Iceberg, Delta, and DuckLake files directly from S3 — no additional configuration and no per-TB scan charges.
Redshift Serverless reduces cluster management, but VACUUM and ANALYZE are still required, and per-unit costs are higher. On ClickBench, Redshift Serverless hits 19.7s — slower than MotherDuck Jumbo (14.2s) and Mega (5.9s).
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 clusters and complexity behind
Fly faster on MotherDuck, for internal insights or in your application.
