You don't need twelve tools. A warehouse, ingestion, dbt & one BI tool cover ~90% of what a mid-market company needs. Here's the opinionated version, with rough monthly costs and the categories to deliberately skip.
The "modern data stack" diagrams floating around have a dozen logos on them, and vendors are happy to sell you every layer. For a mid-market company (~50–1,000 employees, data team of zero to five), most of those layers are premature. The stack that works is four pieces, and the discipline is in what you don't buy yet.
This is the opinionated version we give clients in strategy engagements. We don't resell any of these tools or take referral fees, so the recommendations are just recommendations.
The four pieces you actually need
1. A cloud warehouse. Snowflake, BigQuery, or Databricks. Rough rule: BigQuery if you're already deep in GCP (the integration and pricing gravity is real), Databricks if ML workloads dominate your roadmap, Snowflake for most everything else. At mid-market scale the capability differences matter less than vendors suggest; pick for your team & cloud rather than the benchmarks. Postgres is a legitimate answer below ~50M rows and a handful of sources, and don't let a vendor talk you off it early.
2. Managed ingestion. Fivetran or Airbyte for standard sources (Salesforce, Stripe, HubSpot, ad platforms). Writing your own connector for a SaaS API that Fivetran already supports is a poor use of engineering time; save custom pipelines for the internal systems nobody sells a connector for. Watch consumption pricing as row volumes grow (this is the line item that surprises people at renewal).
3. dbt for transformation. This is the layer that turns raw tables into defined, tested, documented models, and the layer companies skip (which is why their numbers disagree across reports). dbt Core is free; the cost is the modeling discipline. If you adopt one practice from this post, adopt this one. (It's also most of what our transformation practice builds.)
4. One BI tool. Looker, Tableau, Power BI, or Metabase; the ranking matters far less than having exactly one, pointed at dbt-modeled tables rather than raw data. Metabase is underrated for teams without a BI specialist; Power BI is the path of least resistance in Microsoft shops. Two BI tools means two versions of every number.
Orchestration (Airflow, Dagster) earns its place once you have real dependencies between jobs; dbt Cloud or even scheduled queries are fine before that.
What it costs
Rough steady-state monthly numbers for a typical mid-market setup:
- Warehouse compute: $500–$5K/mo (varies widely with workload; watch for dashboards set to refresh hourly)
- Ingestion: $300–$3K/mo depending on connector count and volume
- dbt: $0 (Core) to a few hundred/mo (Cloud)
- BI: $0 (self-hosted Metabase) to $3K+/mo (Looker et al., per-seat)
Call it $1K–$10K/mo all-in for the platform. The larger cost is the people and the modeling work, which is where the build-vs-hire-vs-contract math comes in (getting that math right for your situation is most of what a strategy diagnostic is for).
What to skip (for now)
Each of these is a real category with real value at a scale most mid-market companies haven't reached. Buying them early adds integration surface and spend without moving a metric.
- A data catalog. Under ~10 data people, a maintained dbt docs site and a naming convention do the job. Catalogs earn their keep when cross-team discovery is a daily problem.
- Reverse ETL. Skip until there's a specific, named workflow ("scores back into Salesforce for the SDR team") with an owner. Bought speculatively, it becomes an unused pipe with a subscription.
- MDM (master data management). A real discipline, priced and staffed for the enterprise. Mid-market entity-resolution problems are usually solvable in the modeling layer.
- Streaming infrastructure. Kafka because "we might need real-time" is the classic over-buy. Most "real-time" requirements are actually "hourly is fine." Build streaming when a named use case (fraud, ops alerting, in-product features) needs sub-minute data.
- A second BI tool. Someone will ask for one within the year. Say no and consolidate definitions instead.
- An ML platform. Skip feature stores & model registries until at least one model is in production. Ship the first one with plain tooling.
The pattern behind the list: buy the tool when the problem has a name, an owner & a frequency, not because the reference diagram has a gap.
Sequencing, if you're starting from spreadsheets
Warehouse and ingestion in the first couple of weeks (a week or two of real work now), dbt models for your 10–20 core entities and metrics next (the actual work), BI on top of those models last. Resist building dashboards on raw data "temporarily": those temporary dashboards are the single most common mess we're hired to unwind in modernization work.
If you're weighing this build for your own company (what to buy, what your current setup is worth keeping, whether to hire or contract the work), that's the shape of our 2–6 week strategy diagnostic. Or just tell us where you are and we'll give you the two-paragraph version of what we'd do, free, within a business day.