Palantir holds the decisions. Databricks runs the compute. Which work belongs where is now a question you can measure instead of argue.
Palantir isn’t too expensive. It’s too busy. Inside most enterprises it does two jobs: running the decisions your operators depend on, and crunching the heavy data that feeds them. You bought it for the first job. Blueprint built a tool that finds the second, pipeline by pipeline, and shows what that work would cost on Databricks.
Two jobs, one invoice
Inside most enterprises, Palantir does two jobs and bills them as one.
The first job is the one your operators love. It’s the layer where the business is modeled, where people see what’s happening and record their decisions, with governance and lineage over every step. Palantir calls it the Ontology. Your teams just call it where the work gets done. That layer is Palantir’s real advantage, and nothing in this argument asks you to give it up.
The second job is heavy processing: the pipelines that pull, clean, join and crunch large tables so the first layer has something to show. That work landed in Palantir for a sensible reason. It’s where the team already worked. But convenience isn’t architecture. Heavy compute at scale is exactly what Databricks was built to run.
Over time, the second job quietly becomes the bigger part of the bill. Scaling it means buying more Palantir capacity. Touching it feels risky, because the workflows leadership watches every day sit right on top. So the heavy work stays where it is. Not because anyone chose to overpay, but because there was no safe way to separate the two jobs.
Better together is a placement decision
Databricks and Palantir formalized their partnership in 2025 for a simple reason: each platform is stronger when it isn’t stretched to do the other’s job. Palantir keeps the decisions. Databricks runs the compute. The results flow back, and the people working in Palantir keep working exactly as they do today.
The idea is easy to agree with. The execution is where it gets hard, because better together isn’t one decision. It’s hundreds of small ones. Every pipeline has to be judged on its own: how much processing it burns, how easily it moves, and how tightly it’s tied to the applications your teams depend on. Misjudge in one direction and you leave money on the table. Misjudge in the other and you break a workflow your COO watches every morning.
Hundreds of judgments is too many for any team to make by hand. So Blueprint automated the judgment.
An answer for every pipeline
The Blueprint assessment answers the question for every pipeline, and shows its work. Nobody else in the Databricks ecosystem has one.
It starts with a read-only export of a Palantir pipeline. No access to your production environment. No change to anything your teams use. From that single export, the assessment does three things.
- It scores. Every step in the pipeline is scored out of 100 across five published dimensions. Above 60, it moves. Below 45, it stays. In between, it’s flagged for human judgment. Anything tied directly to the decision layer stays in Palantir, whatever it scores.
- It builds. The work worth moving is translated automatically to run on Databricks, then built and run, with the output checked against the structure the original pipeline defines.
- It proves. The result is written back where Palantir can read it, and the run is priced from Databricks’ own billing records. Not a rate card. Not a projection. A receipt.
Then you decide, with evidence in hand, pipeline by pipeline.
Transparency is the product
Here’s what executives tend to notice first. Most assessments hand you a conclusion and ask for your trust. This one hands you the reasoning.
The scoring weights are published, so your architects can argue with a score instead of a verdict. Every run carries a record of what it read, what it built, and what it cost. And the comparison is made on your pipeline, against the figures you supply, not against a benchmark someone ran on somebody else’s data.
That matters more than it sounds. Cost reduction pitches die in the CFO’s office for one reason: the savings are an estimate, and finance discounts estimates to zero. A measured run survives the meeting. In Blueprint’s work moving pipeline and storage workloads to Databricks, costs on those workloads have come down 30 to 60 percent, depending on volume and complexity. The assessment exists so you don’t have to take that range on faith. It measures what your pipelines cost to run on Databricks, sets that beside what you pay today, and tells you plainly when a pipeline belongs in Palantir.
The first pipeline is a test. The tenth is a strategy.
Each pipeline you place correctly lowers the bill, frees Palantir capacity for the operational work it does best, and sharpens a map of your estate that gets more accurate every time you use it. In weeks, not quarters, you have a placement score for every pipeline, a working Databricks build, and a costed roadmap your CFO can check line by line.
That’s the return that compounds. Not a one-time cut, but a data estate where every workload earns its place.
The executive question
There’s a test you can run this week, and it costs nothing. Ask your data leader three questions. Which of our Palantir pipelines cost the most to run? Which of them need to be in Palantir? What would the rest cost on Databricks?
If the answers come back as estimates, or not at all, Palantir is carrying work nobody decided to give it.
Let’s find that work together. Give me 30 minutes and I’ll walk you through what the assessment would read in your environment, what it would leave alone, and what your first pipeline would tell you. No migration, no disruption, no commitment beyond the conversation.
With more than 25 years of leadership experience at the intersection of technology, strategy, and customer experience, Brad brings a rare blend of executive vision, operational discipline, and creative innovation. His work spans founding, scaling, and transforming companies that bridge human insight and artificial intelligence, with a focus on meaningful, measurable impact.
