Data Strategy Assessment
We read your estate as it runs, typically in two weeks. You keep a costed modernization map with its TCO delta.
Choose the platform that wins
on your own workloads.
Settle Fabric, Databricks or staying put on your own workloads, priced as usage grows, before a migration is funded.
Your workloads, run
Every candidate runs the queries that decide the choice.
Priced as usage grows
Each workload costed at your volumes, today and later.
Your current platform included
The platform you run today competes on the same terms.
Settled in the Blueprint phase, typically two to three weeks · Fixed price · The verdict is yours
Choose on measured workloads,
not on the quadrant.
Platforms are often chosen on a renewal discount, an analyst ranking or the best demo. The cost shows later, when volumes grow and nobody wrote down the cost per workload.
Behavior over feature lists
Observed query time, concurrency and latency go into the score; feature lists stay out.
Every workload priced
Each workload is costed on each candidate at your volumes, and again as they grow.
Models tested the same way
Frontier models and smaller models on compute you control are scored on your own tasks.
This is the right engagement when
- A renewal or capacity commitment is due, and the alternative was never priced.
- Two platforms run side by side, and nobody knows whether to keep both.
- A new workload class is arriving: streaming events, agents or residency-bound AI.
- The model route is open: frontier models, or smaller ones on compute you control.
The same workloads on every candidate,
priced as they grow.
Five steps take the same workloads through every candidate and end in a written verdict.
We agree the candidates, your current platform among them, and the few workloads that carry the decision. Your governance rules are requirements every candidate must meet.
You receive
Evaluation brief
The candidates, the deciding workloads and the bar each must clear.
What this is not.
This work ends in a signed verdict and a target architecture. Planning the move comes after.
Already moving, and choosing services for one source?
That is Technology Selection · Migration stageStaying put, but paying too much?
That is Data Estate OptimizationChoosing the model inside one agent build?
That is Agentic AI
The story this service stands on.
An Australian engineering firm with a pipeline-engineering practice
Engineering · Pipelines
- Lower cost of the internal assistant than ChatGPT Enterprise, on a pay-on-use model
- Roughly 80 to 100×
Safety Management Study Reports in 2 to 3 Days Instead of a Week
Build or buy is a platform decision with a price. Off-the-shelf assistants had not met the firm's expectations and its sensitive data had to stay in Australia, so we built its secure internal assistant on Azure OpenAI inside its own Azure tenant, on a pay-on-use model, at roughly 80 to 100 times lower cost than ChatGPT Enterprise.
Questions buyers ask.
How is this different from a vendor's proof of concept?
A vendor's proof of concept is run by the party that wants the answer to be yes, often on a workload that suits its platform. Here every candidate, the platform you run today included, runs the same representative workloads from your estate, and each workload is priced at your volumes on each candidate. You keep the scorecard and the cost model, with its assumptions exposed, so the result can be checked or challenged.
Do we have to choose one platform?
No. Streaming and batch are scored separately, and the verdict says which candidate won on which workload. Where that points to two platforms, the cost of running both is priced beside the cost of consolidating on one, so a split is chosen on its numbers, not by default.
How do you choose between a frontier model and one we host ourselves?
We choose on your tasks, not on a leaderboard. Each AI task runs on your own prompts and documents through frontier models and through smaller models on compute you control, and the scores that count are quality against your accuracy bar, latency and cost per task. Where residency, regulation or sensitive data decides, a model on compute you control is one of the candidates, and the deployment surface follows your estate.
What do we keep if we stop after the verdict?
You keep everything the work produced: the verdict with its numbers, the workload scorecard, the cost model per workload and the target architecture, yours whether or not you continue with us. The evaluation is narrow by design: the few workloads that decide the platform, not a port of your estate. It is the target-architecture work of the Blueprint phase, which typically runs two to three weeks after an Assess of typically two weeks. Fixed price, fixed scope, priced up front.
More in Strategy & Advisory.
Natural next step
Natural next step: Data & AI Strategy, Strategy & Advisory
When the verdict is signed, the target architecture and the cost model per workload cross to Data & AI Strategy, and the question changes from which platform to which bets run on it, in what order and at what price.
Data Governance
Owners, access tiers and lineage are decided before the next workload lands, in a governance model the platform enforces.
Data & AI Strategy
Every candidate use case gets a written verdict. The survivors become a costed roadmap and delivery backlog.
AI Readiness
One agent is measured before it is built: Butter's free verdict, then typically two weeks on its data, controls and evaluation baseline.
Bring the platform decision
you will have to defend.
Bring the choice you are circling, and the workloads that would settle it, to a 30-minute working session.