saasinator.ai
PLATFORM · DATABRICKS

Spark, Delta and MLflow are open.
The meter on top is not.

DBUs on every job, Mosaic AI on every model call, an enterprise catalogue tier on both. You do not have to open by moving the lakehouse — start with one agent that reads it and bills on nothing.

  • You own the code from commit one
  • Open formats — Iceberg, Delta, Parquet
  • Runs on engines you operate
Start an ignite buildSee the methodology
Explore Databricks Mosaic AIDatabricks' own AI builder layer — Mosaic AI, the Agent Framework and Genie
The estatereplaceable
  • Databricks Workflows · JobsOpen tables — Iceberg, Delta
  • Delta Live TablesOrchestrated pipelines
  • Databricks SQL ServerlessStreaming pipelines
  • Databricks ML RuntimeTraining and serving stack
  • Mosaic AICatalogue and lineage
  • Unity Catalog EnterpriseAudit-trail ledger
Spark · Delta Lake · MLflow · Unity Catalog · Mosaic AIyours on commit one
Why this domain exists

You are not buying a lakehouse.
You are renting a meter on open source.

Not the engines — most are open projects you could run tomorrow. Their commercial shape: metered per job, metered again per model call, the governing catalogue sold as a tier.

Databricks built the lakehouse — Apache Spark, Delta Lake, MLflow, Unity Catalog — and packaged it as a consumption-priced platform. DBUs meter every Spark job, Mosaic AI meters the model calls, and Unity Catalog Enterprise sits on top of both. The underlying technology is largely open source; the meter is not. Open formats throughout: Iceberg, Delta and Parquet on Spark-on-Kubernetes, Trino, Ray and MLflow.

Metered per job

DBUs bill against every Spark job, every warehouse query and every pipeline run. The team that uses the platform best generates the largest invoice.

Metered again for AI

Mosaic AI Training, Foundation Model Fine-tuning and Model Serving price per token and per instance on top of the DBUs the same workload already consumed.

Governed as a tier

Unity Catalog is open source; the enterprise tier that governs your production estate is not. The catalogue you need to leave safely is the one you are paying to stay.

Double meterDatabricks' published pricing model. Both meters are theirs, not ours.
Per job, then per callThe AI tier meters the workload the DBUs already metered.
A Spark job, a warehouse query, a pipeline run
DBUs bill against every one of them
Mosaic AI training, fine-tuning and serving price per token and per instance on top
STAGE 01Ignite

Start with something the lakehouse cannot do.

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

Agent platform

Agentic workflows for any team on the framework you choose — LangGraph, CrewAI or your own — grounded in your data and gated on an eval suite your engineers can re-run.

Runs beside Mosaic AI Agent Framework and Genie

Retrieval and vector search

Retrieval pipelines over your own documents on pgvector, Qdrant or Weaviate, running on infrastructure you operate and can inspect.

Runs beside Databricks Vector Search

Lakehouse audit-trail ledger

Every job, every model call and every catalogue change logged on infrastructure you operate, so an audit is something your governance team reads rather than requests.

Runs beside Unity Catalog

Cost attribution surface

Job 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 Databricks System Tables and Budget Policies

Start an ignite build
STAGE 02Reforge

The estate you already own, rebuilt AI-native.

The pipelines your engineers wrote, the streaming logic they maintain, the experiments and features your data scientists track. Same logic, open substrate, and the metering stops.

IN — what you run today
  • Databricks Workflows · Jobs
  • Delta Live Tables
  • Databricks SQL Serverless
  • Databricks ML Runtime
  • Mosaic AI
  • Unity Catalog Enterprise
SAIF

the saasinator AI Factory — glass-walled delivery

  • Brief
  • Build
  • Evals
  • Deploy
  • Transfer
OUT — what you own afterwards
  • Open tables — Iceberg, Delta
  • Orchestrated pipelines
  • Streaming pipelines
  • Training and serving stack
  • Catalogue and lineage
  • Audit-trail ledger

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

Pipeline and orchestration rebuild

The orchestration your engineers already maintain, rebuilt on Airflow or Dagster running Spark on Kubernetes against your own tables.

Databricks Workflows · Jobs

Streaming rebuild

Structured streaming and change capture rebuilt on Kafka, Flink and Spark Streaming running on infrastructure you operate and can inspect.

Databricks Structured Streaming

Experiment and feature store rebuild

Experiment tracking, model registry and feature serving rebuilt on MLflow and Feast against your own warehouse, so the lineage of a model is yours to read.

MLflow · Databricks Feature Store

Notebook and compute-fleet rebuild

Collaborative notebooks and the compute behind them rebuilt on JupyterHub, VS Code dev containers and Ray or Dask on clusters your platform team operates.

Databricks ML Runtime

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
  • Orchestration on Airflow or Dagster running Spark on Kubernetes
  • Delta or Iceberg tables in your own object storage, same merge semantics
  • Streaming and change capture on Kafka, Flink and Spark Streaming
Data science
  • Collaborative notebooks on JupyterHub, VS Code dev containers, Hex or Deepnote
  • Experiment tracking on MLflow your platform team operates
  • Compute fleet on Ray, Dask or Spark-on-Kubernetes
Machine learning
  • Training pipelines on Ray, Kubeflow and MLflow
  • Model serving on KServe, BentoML or vLLM on your infrastructure
  • Feature store on Feast against your own warehouse
Generative AI
  • Fine-tuning and inference on the model layer you choose
  • Retrieval pipelines on pgvector, Qdrant or Weaviate
  • Agentic workflows on the framework you choose, gated on your eval suite
Governance
  • Catalogue on Unity Catalog OSS, Apache Atlas or OpenMetadata you operate
  • Column-level lineage, classification and policy 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 lakehouse stays — as software you own.

Liberation is earned, not sold. By the time we replace SQL or serving surfaces, you have watched us build. Then running a job on your own data stops being billable.

The licence ledger

Illustrative

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

Basis of charge
Per DBU consumed, per workload class. Usage is the meter, so the invoice tracks how well the platform is used rather than what it returns.
AI tier
Training, fine-tuning and serving price per token and per instance on top of the DBUs the same workload already consumed.
Governance tier
The open catalogue is free; the enterprise tier that governs the production estate is the one that renews.
On exit
Jobs, notebooks, lineage and policy sit inside the vendor's workspace — 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 Databricks agreement — no contract text, no clause references, no figures.

SQL Serverless replacement

The SQL and dashboard surface rebuilt on Trino over open tables, retired into only once the replacement is carrying the same queries.

Databricks SQL Serverless · AI/BI dashboards

Delta Live Tables replacement

Declarative pipeline logic rebuilt as orchestrated jobs your engineers own and can extend, running on clusters you operate.

Delta Live Tables

Mosaic AI replacement

Training, fine-tuning and serving rebuilt on MLflow, Ray and KServe, with prompts and evals your own team owns and can re-run.

Mosaic AI Training · Foundation Model Fine-tuning · Mosaic AI Model Serving

Unity Catalog Enterprise replacement

Catalogue, lineage and policy enforcement rebuilt on Unity Catalog OSS, Apache Atlas or OpenMetadata on infrastructure you operate.

Unity Catalog Enterprise

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 Databricks lakehouse first

Nothing is retired on faith. The Databricks lakehouse 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
Databricks DBU 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 DBU usage.

Ten working days. Which capability to build first, what the meter actually costs you on the jobs 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