saasinator.ai
PLATFORM · SNOWFLAKE

Snowflake meters your own data
back to you.

Credits per query, storage per ingested gigabyte, Cortex per model call. 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
  • Open formats — Iceberg, Parquet, Delta
  • Runs on engines you operate
Start an ignite buildSee the methodology
Explore Snowflake Cortex AISnowflake's own in-warehouse AI layer — Cortex Complete, Cortex Analyst and Document AI
The estatereplaceable
  • Snowflake warehouse computeOpen tables — Iceberg, Parquet
  • Snowpipe · Streams · TasksIngest and ELT pipelines
  • SnowparkSemantic layer
  • Cortex AIAnalytics agents
  • Data Sharing · Clean RoomsSharing and clean rooms
  • Horizon Catalog · Object TaggingAudit-trail ledger
Warehouse · Snowpipe · Cortex AI · Snowpark · Horizonyours on commit one
Why this domain exists

You are not buying a warehouse.
You are renting permission to ask questions.

Not the engine — its commercial shape: metered per query, metered again per model call, and holding your tables in a format the vendor governs.

Snowflake is the data warehouse that made consumption pricing the default. Compute and storage are metered as credits, per query and per ingested gigabyte. Cortex AI sits on top and meters the model calls. Snowpark adds compute for the workloads SQL cannot carry. The category meant to compound your advantage compounds the invoice alongside it. Open formats throughout: Iceberg, Parquet and Delta on engines you operate.

Metered per query

Compute bills as credits and storage bills per ingested gigabyte. Curiosity is the meter, so the team that uses the platform best generates the largest invoice.

Metered again for AI

Cortex Complete, Cortex Analyst and Document AI price per call on top of the credits the query already consumed. The AI tier is a second meter on the same data.

Held in the vendor's estate

Tables, shares, tags and lineage sit inside Snowflake's own catalogue. Portable in principle, expensive in practice — and that asymmetry does not sit on your side of the table.

Double meterSnowflake's published pricing model. Both meters are theirs, not ours.
Per query, then per callThe AI tier is a second meter on the same data.
A question asked of your own data
Compute bills as credits, storage per ingested gigabyte
Cortex Complete, Cortex Analyst and Document AI price per call on top
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 keeps doing what it does today. Confidence starts here, in weeks.

Text-to-SQL analytics agents

Natural-language analytics grounded in your own semantic layer and gated on an eval suite your analysts can read and re-run themselves.

Runs beside Snowflake Cortex Analyst

Audit-trail sharing ledger

Every query, every grant and every share logged on infrastructure you operate, so an access review is something your governance team reads rather than requests.

Runs beside Snowflake Data Sharing and Horizon Catalog

Cost attribution surface

Query cost attributed to team, project and environment as it happens, so the platform team can answer for the meter without waiting for the invoice.

Reads your Snowflake credit consumption

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 policies your governance team maintains. Same logic, open substrate, and the metering stops.

IN — what you run today
  • Snowflake warehouse compute
  • Snowpipe · Streams · Tasks
  • Snowpark
  • Cortex AI
  • Data Sharing · Clean Rooms
  • Horizon Catalog · Object Tagging
SAIF

the saasinator AI Factory — glass-walled delivery

  • Brief
  • Build
  • Evals
  • Deploy
  • Transfer
OUT — what you own afterwards
  • Open tables — Iceberg, Parquet
  • Ingest and ELT pipelines
  • Semantic layer
  • Analytics agents
  • Sharing and clean rooms
  • Audit-trail ledger

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

Ingest and ELT rebuild

The loading, incremental and scheduled logic your engineers already maintain, rebuilt on Airflow, dbt or Dagster reading from your sources and writing to your own tables.

Snowpipe · Streams · Tasks

Streaming and CDC rebuild

Change capture and streaming ingest rebuilt on Kafka, Debezium and Flink running on infrastructure you operate and can inspect.

Snowpipe Streaming

Semantic layer and BI rebuild

Metrics defined once on Cube or the dbt semantic layer and served to every BI surface, so a definition is a contract your analysts own rather than an app that meters.

Snowflake Native Apps · Streamlit in Snowflake

Governance and lineage rebuild

Catalogue, lineage, classification and access policies rebuilt as data your governance team edits directly, on tooling they operate.

Snowflake Horizon Catalog · Object Tagging

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, dbt or Dagster reading from your sources
  • Storage and table format on Iceberg and Parquet in your own object storage
  • Streaming and CDC on Kafka, Debezium and Flink you operate
Analytics
  • Warehouse compute on Trino, ClickHouse or DuckDB, on engines you run
  • Semantic layer on Cube or dbt — metrics defined once, served everywhere
  • BI surfaces built against your own warehouse
AI & ML
  • LLM-against-warehouse workflows on the model layer you choose
  • Text-to-SQL agents grounded in your semantic layer and your eval suite
  • Feature store, training, registry and serving on MLflow and Kubeflow
Data sharing
  • Cross-tenant sharing on an Iceberg REST catalog or Delta Sharing
  • Clean rooms on infrastructure you operate
  • Audit-trail sharing ledger — every query, every grant, every share logged
Governance
  • Catalogue and lineage on tooling your governance team owns
  • Row-level security, column masking and classification as data your team edits
  • Cost attribution by team, project and environment
Functional areas this domain touches
Data & AnalyticsTechnology
Plan a rebuild
STAGE 03Liberate

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

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

The licence ledger

Illustrative

The commercial shape of a Snowflake estate, as a buyer reads it

Basis of charge
Per credit consumed and per gigabyte ingested. Usage is the meter, so the invoice tracks curiosity rather than value.
AI tier
Model calls price separately on top of the credits the same query already consumed.
Commitment condition
Discounting is conditional on a consumption commitment agreed up front, before the workloads that will consume it exist.
On exit
Tables, shares, tags and lineage sit inside the vendor's catalogue — 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 workload plan.

Illustrative. This is our reading of a commercial pattern, not a quotation from any Snowflake agreement — no contract text, no clause references, no figures.

Warehouse compute replacement

The OLAP surface rebuilt on Trino, ClickHouse or DuckDB over open tables, retired into only once the replacement is carrying the same queries.

Snowflake warehouse compute · Snowpark

Cortex AI replacement

LLM-against-warehouse workflows rebuilt on the model layer you choose, with prompts and evals your own team owns and can re-run.

Cortex Complete · Cortex Analyst · Document AI

Data sharing and clean rooms replacement

Cross-tenant sharing rebuilt on an Iceberg REST catalog or Delta Sharing, with row and column policies your governance team authors.

Snowflake Data Sharing · Snowflake Clean Rooms

ML platform replacement

Feature store, training, registry and serving rebuilt on MLflow and Kubeflow running on infrastructure you operate.

Snowpark ML · Cortex ML Functions

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 Snowflake warehouse first

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

See the methodology
Proof

Not a claim. A delivered platform.

100%
IP transferred on commit one
0
Snowflake credit consumption
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 workload and your credit usage.

Ten working days. Which capability to build first, what the meter actually costs you on the workloads you run, and what the replacement costs to build. You keep the analysis.

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