Data warehouse as a service: what it is, how to choose, and what it costs

How data warehouse as a service works

A DWaaS provider gives you a data warehouse without the operational overhead of running one yourself. Under the hood, the provider manages compute and storage resources in one or more cloud regions. You interact with the warehouse through a SQL interface, a web console, or an API.

Most DWaaS platforms separate storage from compute. Your data lives in cheap object storage (like S3 or GCS), and when you run a query, the provider spins up compute resources to process it. This separation is what makes elastic scaling possible.

The "as a service" part means you are not managing any of this. You do not patch operating systems or tune buffer pools. The tradeoff is less control, and you pay a premium for that convenience. How much of a premium depends on which provider you choose.

DWaaS vs. traditional on-premises data warehouses

With an on-premises warehouse, you own and operate everything: the hardware, the software licenses, the network, the storage, the people who keep it all running. With DWaaS, the provider owns and operates the infrastructure, and you consume it as a service.

On-premises warehouses require capital expenditure upfront. You need to estimate your capacity needs years in advance, buy hardware accordingly, and hope you guessed right. If you undersize, queries slow down and analysts complain. If you oversize, you waste money on idle hardware.

DWaaS flips this model. You start small and grow as needed, with no hardware to buy and no data center lease to sign.

The total cost of ownership tends to be lower for small and mid-sized workloads, though at very large scale the math can shift back toward self-managed options.

There are cases where on-premises still makes sense: strict data residency requirements, extremely predictable workloads, or large sunk costs in existing infrastructure. But for most teams, the operational burden of running your own warehouse is hard to justify in 2026. For a deep dive into enterprise data warehouse architecture and when on-premises still makes sense, see our dedicated guide.

Comparing DWaaS providers

Not all DWaaS offerings are the same. The right choice depends on your data volume, team size, cloud provider, and how much operational complexity you are willing to take on.

What to look for when choosing a provider

Separation of storage and compute is table stakes at this point. Every major provider does this. What varies is how granular the separation is and how quickly compute can scale.

SQL compatibility matters more than people think. Some providers implement non-standard SQL extensions that lock you in. DuckDB (MotherDuck's engine) follows PostgreSQL SQL syntax closely, which makes migration straightforward in either direction.

Concurrency handling is where providers differ. How does the system behave when 50 analysts run queries at the same time? MotherDuck handles this through its per-user tenancy model where each user gets their own isolated DuckDB instance, so queries never compete for resources. Snowflake solves this with virtual warehouse scaling but at a cost.

Ecosystem integration matters because a warehouse that does not connect to your existing tools (dbt, Fivetran, Airbyte, Looker, Tableau, Python notebooks) creates friction you will regret later.

Security and governance (role-based access control, column-level masking, audit logging) are non-negotiable for most organizations. Smaller teams may not need all of this on day one, but you will want it eventually.

Operational overhead is the factor most teams underestimate. The hours your engineers spend managing warehouse configurations and troubleshooting performance are real cost, even if they never show up on a bill. MotherDuck sidesteps this problem because there is no configuration to manage.

Pricing models and hidden costs

DWaaS pricing falls into a few broad categories, and the sticker price rarely tells the full story.

Consumption-based pricing charges you for what you use, measured in compute-seconds, bytes scanned, or credits depending on the provider. Snowflake and BigQuery (on-demand) use variations of this model. You pay nothing when idle, which sounds great until a runaway query or usage spike causes bill shock.

Provisioned pricing means you pay for a fixed amount of compute regardless of whether you use it. Redshift's original model works this way. More predictable but less efficient, since you pay for idle capacity.

Serverless pricing is a flavor of consumption-based pricing where the provider also handles capacity management. BigQuery and MotherDuck use serverless models. You don't choose instance sizes or cluster counts. MotherDuck's pricing is particularly straightforward: you choose a Duckling size for your workload, and that's it.

The hidden costs are where teams get burned. Egress charges for moving data out of the cloud add up. Storage costs for old data accumulate quietly. And the human cost of managing a complex warehouse platform is real, even if it never appears on the cloud bill. Because MotherDuck is serverless, you skip the warehouse tuning, capacity planning, and credit monitoring that drive those hidden costs on other platforms. There are practical ways to cut cloud data warehouse costs significantly. A thorough TCO analysis that accounts for these factors will save you from surprises.

When DWaaS makes sense

DWaaS is the right choice for most teams building a modern data warehouse in 2026. If you don't already have a large on-premises investment, there is little reason to build one.

Startups and small teams benefit the most. You can go from zero to running analytics queries in an afternoon without hiring infrastructure engineers. MotherDuck and BigQuery both offer generous free tiers that let you get real work done without spending anything.

Mid-sized companies benefit from the elasticity. Your data volumes and query patterns will change as the business grows, and a DWaaS platform can scale with you without a forklift upgrade.

Large enterprises are already using DWaaS, though they often run it alongside existing on-premises systems during a migration period.

Getting started with DWaaS

The fastest way to get started is to sign up for MotherDuck (it takes about 30 seconds) and load some data. You can query local CSV or Parquet files immediately using DuckDB's local execution, then push your data to the cloud when you're ready. No credit card required.

If you want to compare providers, most offer free tiers. Try a couple with real data and see which one fits your workflow. A hands-on test is worth more than any vendor comparison document.

Start with a single use case. Pick one dashboard or one analytics question that your team needs answered. Load the relevant data, build the queries, and see how it feels.

If you're a startup or small team, the cloud data warehouse startup guide walks through the process. For teams that want to enable analysts to query data directly, DWaaS is a natural fit for self-service analytics.

Think about your data pipeline early. A warehouse is only useful if data flows into it reliably. Tools like dbt, Fivetran, and Airbyte integrate with MotherDuck and all the other major DWaaS providers.

Set up cost monitoring from the start. Every DWaaS provider has some form of usage dashboard or budget alerting. Turn it on before you need it, not after you get your first surprising bill.