What a $200-a-month data stack looks like

The quote lands in every founder’s inbox eventually: Fivetran for ingestion, Snowflake for the warehouse, Looker on top. The bundles we’ve seen quoted to seed-stage companies run into the low thousands of dollars a month before anyone has answered a single question. For a company with under a few hundred gigabytes of data, which describes most companies before Series B, that quote is pricing a problem you don’t have. Here is the stack we deploy instead, itemized, with list prices checked in October 2026.

The bill of materials

Every line below is something we run in production. The prices are steady-state monthly figures, not free-tier gymnastics that collapse the month you get traction (with one exception, noted).

• Ingestion: $0 to about $50. dlt is an open-source Python ingestion library; jobs pulling your product database, Stripe, and the ad platforms run on the same small VM as everything else, so the marginal cost is zero. If you’d rather buy connectors than own scripts, Airbyte Cloud’s Standard plan starts at $20 a month with five credits included and $5 per extra credit, which covers modest seed-stage volumes.
• Storage: $2 to $10. Cloudflare R2 lists $0.015 per GB-month with no egress fees, so a few hundred gigabytes of Parquet costs a few dollars. This is the line everyone expects to be big; it is the smallest number on the page.
• Compute: $0. DuckDB is free and scans this much data in seconds on a single node. If you want a shared cloud endpoint your team and BI tool can both hit, MotherDuck’s Lite tier is free for up to three users with 10 GB of storage and 10 compute hours a month. That’s the free-tier exception: its Business tier is $250 a month plus usage, so it belongs on this list only once more people need it.
• Orchestration: $24 to $48. One small VM runs Dagster (open source, Apache 2.0): schedules, retries, and a UI that answers “did last night’s run succeed.” On DigitalOcean, a 4 GB droplet is $24 a month and an 8 GB one is $48. A serverless cron works too, but you’ll miss the run history the first time something breaks at 3am.
• BI: $0 to $100. Metabase’s open-source edition is free with unlimited users and rides on the same VM. If you’d rather not host it, Metabase Cloud Starter is $100 a month for five users. Evidence is the code-first alternative if your reports belong in git.
• Monitoring: $0. The orchestrator already knows when a run fails or data goes stale; a Slack webhook is the pager. No observability SaaS required at this scale.

Add it up and the ledger reads roughly $30 to $60 a month at the frugal end, and about $200 with managed connectors, a bigger VM, and hosted BI. The outcome is the same shape as the expensive version: your sources land in one queryable place, models run on a schedule, charts sit on top, and a failed run pings a channel instead of dying silently.

What the expensive stack actually buys

Being fair here matters, because the expensive stack is not a scam. It is priced for a later stage than yours, and it buys three real things:

• Connector breadth and maintenance. Fivetran maintains a large catalog of connectors, and when NetSuite changes its API at 2am, that’s their pager. With dlt, an upstream API change is your engineer’s Tuesday morning. Note that Fivetran is not all-or-nothing: its free plan covers 500,000 monthly active rows, and its own pricing example puts four paid connectors at about $511 a month. Buying just the painful connectors is a legitimate hybrid.
• Governance. Snowflake’s role hierarchies and row access policies (an Enterprise Edition feature) matter enormously with forty data consumers and a compliance program. With four consumers who share a standup, they’re configuration you pay for and never touch.
• Seats and self-serve. Looker’s semantic modeling layer earns its price when dozens of non-analysts need to explore data unaided. Metabase covers a ten-person company fine.

The crossover points can be estimated. When the engineering hours spent patching connectors cost more than the subscription would (at a loaded $100 an hour, half a day a week pays for a lot of Fivetran), buy the connectors. When you pass roughly a terabyte of actively queried data, need cross-team isolation, or an auditor starts asking who can see which rows, the warehouse tier begins justifying itself. Before those points, the premium buys standby capacity you don’t use.

The line item that isn’t on any invoice

Now the honest part. The $200 stack does not run itself. Sources drift, a connector update breaks a sync, a backfill needs to run without double-counting, and someone has to read the morning alert and own the fix. In our experience that’s two to four hours a week in steady state, more in a bad week, and that operator is the real cost line whichever stack you buy. You can pay it three ways: a product engineer’s attention (the most expensive hours you have), a dedicated data hire (the median data scientist salary was $120,230 in May 2025, before benefits, tools, and recruiting), or an outside operator.

One rung further down the honesty ladder: if you’re pre-product-market-fit and every question you have is answerable from one Postgres database, skip all of this. A read replica and a lightweight BI tool are the correct stack, and this post will keep until the day two dashboards disagree about revenue.

We build the stack above in two to three weeks and then run it on retainer: the monitoring, the break-fix, the backfills, the new models as your questions change. The $200 is the infrastructure. The operator is what you’re really choosing, so if you have an enterprise quote on your desk, bring it to an intro call and we’ll price the boring version next to it.

Sources

• Fivetran pricing, Fivetran
• Airbyte pricing, Airbyte
• R2 pricing, Cloudflare Docs
• MotherDuck pricing, MotherDuck
• Droplet pricing, DigitalOcean
• Metabase pricing, Metabase
• Understanding row access policies, Snowflake Documentation
• Data Scientists: Occupational Outlook Handbook, U.S. Bureau of Labor Statistics