saasinator.ai
FUNCTION · DATA & ANALYTICS

Metered on the question.
Then the seat, then the row.

Warehouse credits, BI seats, ML platform licences — every analytics workflow bills on data your business already owns. You do not have to open by moving the warehouse: start with one agent that reads it and bills on nothing.

  • You own the code from commit one
  • No per-row or per-seat fees
  • Open formats — Iceberg, Delta, Parquet
Start an ignite buildSee the methodology
The estatereplaceable
  • Snowflake · Databricks · BigQueryOpen tables — Iceberg, Delta, Parquet
  • Fivetran · Stitch · dbt CloudIngest and ELT pipelines
  • Tableau · Power BI · LookerSemantic layer
  • Hightouch · Census · SegmentAnalytics agents and BI surfaces
  • Collibra · Alation · AtlanActivation pipelines
  • Databricks ML · Amazon SageMakerCatalogue and lineage ledger
Ingest · warehouse · semantic layer · BI · ML · governanceyours on commit one
Why this domain exists

You are not buying analytics.
You are renting access to your own data.

Not the engines — their commercial shape: metered on the questions you ask, metered again per named user, and priced a third time on every row that moves.

Data and analytics is the only function where the contract bills on your own data. Snowflake credits, Databricks DBUs and BigQuery slots price the questions you ask of data you own. Tableau, Power BI, Looker and ThoughtSpot bill per seat on top. Fivetran, Stitch, Hightouch, Census and Segment price the rows; Collibra, Alation and Atlan price the catalogue. Open formats throughout — Iceberg, Delta and Parquet on engines you operate.

Metered on your own data

Snowflake credits, Databricks DBUs and BigQuery slot consumption bill against the questions you ask of assets you already own. The team that uses the platform best generates the largest invoice.

Metered again per seat

Tableau, Power BI, Looker, SAP Analytics Cloud and ThoughtSpot price per named user, on top of the consumption the same dashboard already caused. Widening the audience widens the bill.

Priced again per row

Fivetran, Stitch, Hightouch, Census and Segment price ingest and activation by connector and by row moved, while Collibra, Alation and Atlan price the catalogue per asset. Growth in the data itself is growth in the invoice.

Double meterThe vendors' published pricing models. The meters are theirs, not ours.
Per question askedThe same dashboard is charged twice, then charged again per row.
A question asked of assets you already own
Credits, DBUs and slot consumption bill the query
Tableau, Power BI and Looker price per named user on top of it

Rented today

  • Metered on your own data
  • Metered again per seat
  • Priced again per row

Owned after

  • Your systems
  • Claude-first engine
  • Your data
What this desk rents today collapses into one stack it owns outright.
STAGE 01Ignite

Start with something the warehouse cannot do.

New agents reading the warehouse you already run. Nothing migrated, no table moves — every existing pipeline and dashboard keeps doing what it does today. Confidence starts here, in weeks.

Analytics agent

Natural-language exploration grounded in your own metric definitions, gated on an eval suite your analysts can read and re-run themselves. Built on your semantic layer, so an answer is traceable rather than opaque.

Runs on your own warehouse, beside the BI seats you pay for

Scorecard and activation agent

Customer segments, supplier scorecards and operational rankings computed on your own scoring criteria and pushed into the systems your teams already work in, rather than waiting for someone to open a dashboard.

Built on your own scoring criteria and your own warehouse

Lineage and PII ledger

Lineage, classification, access and PII tagging logged as one ledger on infrastructure you operate, so a data-governance review is something your team reads rather than requests.

Built across the data systems you already operate

Start an ignite build
STAGE 02Reforge

The estate you already own, rebuilt AI-native.

The pipelines your engineers wrote, the metrics your analysts defined, the classifications your governance team maintains. Same logic, open substrate, and the metering stops.

IN — what you run today
  • Snowflake · Databricks · BigQuery
  • Fivetran · Stitch · dbt Cloud
  • Tableau · Power BI · Looker
  • Hightouch · Census · Segment
  • Collibra · Alation · Atlan
  • Databricks ML · Amazon SageMaker
SAIF

the saasinator AI Factory — glass-walled delivery

  • Brief
  • Build
  • Evals
  • Deploy
  • Transfer
OUT — what you own afterwards
  • Open tables — Iceberg, Delta, Parquet
  • Ingest and ELT pipelines
  • Semantic layer
  • Analytics agents and BI surfaces
  • Activation pipelines
  • Catalogue and lineage ledger

Product names are the vendors' own. What comes out the other side is yours — source, models, data and pipeline, transferred on commit one.

Ingest and ELT rebuild

The loading, transformation and freshness logic your engineers already maintain, rebuilt on Airflow, Prefect or Dagster with dbt models living in your own repository and writing to your own tables.

Fivetran · Stitch · dbt Cloud

Semantic layer and dashboard rebuild

Metrics defined once against your own warehouse and served to every surface, with dashboards, exploration and embedded analytics rebuilt as screens your engineers extend rather than workbooks that come with a seat.

Tableau · Power BI · Looker · ThoughtSpot

Catalogue and governance rebuild

Lineage, classification, access policy, PII tagging and the model registry rebuilt as one ledger your governance team edits directly, on tooling they operate.

Collibra · Alation · Atlan

Coverage

Five workload classes. The same pattern in each.

Wherever the meter runs hottest is where the licence bites hardest. These are the areas we rebuild, and what sits inside each.

Data engineering
  • Ingest and ELT on Airflow, Prefect or Dagster reading from your sources
  • dbt transformations and freshness SLAs in your own repository
  • Open table formats — Iceberg, Delta and Parquet in your own object storage
Analytics & BI
  • Semantic layer defined once against your own warehouse
  • Dashboards and exploration rebuilt as surfaces against that warehouse
  • Embedded analytics inside the products your teams already use
Machine learning
  • Feature store, training and model registry on MLflow, Kubeflow and Ray
  • Model serving on infrastructure your platform team operates
  • Experiment lineage your data scientists can read and re-run
Activation
  • Customer segments and supplier scorecards computed on your own criteria
  • Activation into the operational systems your teams already work in
  • Reverse-ETL pipelines owned end to end, not priced per row
Governance
  • Catalogue and lineage on tooling your governance team owns
  • Classification, access policy and PII tagging as data your team edits
  • Model registry held in the same ledger as the data it was trained on
Platforms this domain replaces
SnowflakeDatabricksMicrosoftSAP
Plan a rebuild
STAGE 03Liberate

The meter comes out. The analytics stay — as software you own.

Liberation is earned, not sold. By the time we replace warehouse compute or BI, you have watched us build. Then asking a question of your own data stops being billable.

The licence ledger

Illustrative

The commercial shape of a data and analytics estate, as a buyer reads it

Basis of charge
Per credit, per DBU and per slot consumed. Usage is the meter, so the invoice tracks curiosity rather than value.
Seat tier
The BI layer prices per named user on top of the consumption the same dashboard already caused.
Movement and coverage tiers
Ingest and activation price per connector and per row moved; the catalogue prices per asset described.
On exit
Metric definitions, lineage, classifications and dashboards sit inside the vendors' own products — not in an asset you hold.
Cost of staying————

Shape only — the direction of travel, not a quantity. Your own curve comes from your own consumption commitment and your own seat count.

Illustrative. This is our reading of a commercial pattern common to consumption-priced data platforms and seat-priced BI, not a quotation from any vendor agreement — no contract text, no clause references, no figures.

Warehouse and lakehouse replacement

Ingest, lake, warehouse and semantic layer rebuilt on open formats and engines your platform team operates, retired into only once the replacement is carrying the same queries.

Snowflake credits · Databricks DBUs · BigQuery slots

BI seat replacement

Dashboards, exploration and embedded analytics rebuilt as surfaces against your own warehouse, so widening the audience stops widening the invoice.

Tableau · Power BI · Looker · ThoughtSpot · SAP Analytics Cloud

ML platform replacement

Feature store, training, registry and serving rebuilt on MLflow, Kubeflow and Ray running on infrastructure you operate, with the experiment lineage yours to read.

Databricks ML · Amazon SageMaker enterprise add-ons

Reverse-ETL and activation replacement

Segments, scorecards and operational dashboards activated into your systems on pipelines you own end to end, rather than metered by the row.

Hightouch · Census · Segment

Plan a liberation
How it is built

Glass-walled from brief to transfer. Nothing behind a black box.

SAIF is our delivery method and it runs in the open. You watch the build as it happens, read the evals that gate every release, and keep every artefact — including the ones that record what did not work.

  1. 01

    Brief

    One workflow, scoped against your own data and your own renewal position.

  2. 02

    Build

    Agentic delivery against your systems, visible while it runs.

  3. 03

    Evals

    Every release gated on tests you can read and re-run yourself.

  4. 04

    Deploy

    Into infrastructure you control, alongside the system it stands beside.

  5. 05

    Transfer

    Your team runs it. We do not leave until they can.

You own it from commit one

Source, models, prompts, evals and pipeline. Not a licence to use what we built — the asset itself.

Two weeks to a working build

A working build against your own systems in two weeks. Fixed scope, fixed bill.

It runs beside the licensed warehouse first

Nothing is retired on faith. The licensed warehouse goes when the replacement is carrying the work.

See the methodology
Proof

Not a claim. Terms we sign up to.

100%
IP transferred on commit one
0
per-row and per-seat data fees
2 weeks
to a working build · fixed scope, fixed bill
Our commitment

Every engagement starts with a scoped working build against your own systems. If it doesn't convince you, you pay nothing — and you keep the code either way.

The ask

Bring one data contract and your seat count.

Ten working days. Which capability to build first, what the meter and seats cost on the workloads you run, and what owning the replacement takes. You keep the analysis.

Start an ignite buildSee how we build
Fixed feeTen working daysNo commitment beyond the diagnostic