Palantir Foundry vs Databricks vs Snowflake: Which One Fits Your Enterprise?

Last updated: October 7, 2026

By Omar J, Director, PAL4C Labs

TL;DR Databricks and Snowflake store, engineer, and analyze enterprise data. Palantir Foundry turns that data into operational decisions people can approve and write back. They overlap less than they appear to from a distance, and both vendors now partner with Palantir. So the real question is which layer you’re missing, and whether you need a decision layer yet.

I lead Palantir Foundry and AIP delivery at PAL4C Labs. Palantir Foundry vs Databricks is the comparison buyers raise with me most, and Snowflake tends to join the conversation about five minutes later. Fair enough. All three platforms land in the same budget meetings, all three talk about AI, and from across the room they look like rivals chasing the same dollars.

Up close, they mostly aren’t. Databricks and Snowflake are built to store, engineer, and analyze data at scale, while Foundry is built to model the operations that data describes, such as shipments, suppliers, and work orders, and to let people and AI agents act on those things with permissions and an audit trail attached.

A word on where I sit before any of that. PAL4C Labs is a Palantir-only consultancy. We help enterprise operations and data teams design, build, and run Foundry and AIP programs, from data integration through the Ontology to AI agents, and we earn nothing when you pick Databricks or Snowflake on their own. So I have a horse in this race. I’ve kept the comparison to documented capabilities and fit, labeled my opinions as opinions, and included a section on when you should skip Foundry altogether. Hold me to it.

Is This Really a Three-Way Choice?

Rarely. Databricks is a lakehouse, and Snowflake is a cloud data platform built around a warehouse, so both are foundations. Foundry is a decision layer that can sit on either one. The real decision is usually about combinations.

Overlap exists at the edges. All three ingest data, run transformations, and ship AI features. What differs is the center of gravity, with Databricks pulled toward engineering and machine learning, Snowflake toward SQL analytics, and Foundry toward operational workflows that write decisions back into the business.

Empty houses are rare for Foundry. In our engagements, the client nearly always runs a lakehouse or a warehouse already, has a data team that’s fond of it, and wants decisions to move faster than the monthly dashboard cycle allows. Nobody wants to rip that out. Nor should they.

Even the vendors seem to agree. Databricks signed a product partnership with Palantir in March 2025, and Snowflake followed in October 2025, both built around reading the same data without copying it. Plumbing details further down.

What Does Each Platform Actually Do?

Foundry integrates enterprise data and models it as an Ontology of business objects and actions. Databricks is a lakehouse platform for data engineering, analytics, and AI. Snowflake is a consumption-priced cloud data platform with Cortex AI built in.

Each one gets a paragraph below, starting from the vendor’s own description rather than mine.

Palantir Foundry

Palantir’s Foundry platform summary calls it an enterprise data operating system. You integrate data, then build an Ontology layer made of objects, links, action types, and functions. Actions write changes back. So when a planner approves a reorder inside a Foundry app, the order object itself changes, with the planner’s permissions checked on the way through. AIP Logic adds no-code functions powered by large language models. And Foundry runs in the cloud or on-premise through Apollo, Palantir’s deployment layer.

Databricks

Databricks’ own documentation describes a unified, open analytics platform for data, analytics, and AI. Its lakehouse merges two things that used to live apart, the data warehouse and the data lake, and Unity Catalog governs what sits inside. It runs on AWS, Azure, and Google Cloud. If your engineers write Spark and Python all day, they probably already have a favorite cluster configuration here.

Snowflake

Snowflake grew up as a cloud data warehouse and kept expanding. Its Cortex AI layer runs AI next to your data, with Cortex AI Functions, Cortex Analyst for turning natural-language questions into SQL, and Cortex Agents, plus model access that includes Anthropic, Meta Llama, and Mistral. Billing is consumption-based, in credits. SQL-first teams settle in fast. That counts for more than people admit.

How Do Foundry, Databricks, and Snowflake Compare Side by Side?

Foundry centers on operational workflows with write-back, Databricks on data engineering and machine learning, and Snowflake on SQL analytics with consumption pricing. The table below lines them up, with our opinion rows clearly labeled.

Two rows deserve a warning sticker. Primary users and best fit are PAL4C’s view, drawn from the projects we’ve worked on, not from any vendor’s positioning. Everything else traces back to each vendor’s own documentation or pricing page.

Read the table top to bottom and one row does most of the work. Write-back. Foundry treats acting on data as a core job of the platform, while Databricks and Snowflake leave that layer to the pipelines and apps your team builds on top, which is a perfectly reasonable design choice for platforms whose main job is storing and processing data well. Neither approach is wrong. They serve different owners. And the owner question tends to settle the debate faster than any feature checklist ever will.

Objects and Actions vs Tables and Queries

Databricks and Snowflake think in tables. Foundry thinks in things.

That sounds like a slogan. It isn’t. A shipment in a lakehouse is a row, or a few rows spread over three tables that one good engineer knows how to join. A shipment in Foundry’s Ontology is an object, linked to a carrier object and a warehouse object, carrying properties like ETA and delay status, and wrapped in action types such as reassigning the carrier, each with its own permission rules and its own record of who did what and when. Same data. Different contract with the people who use it.

That’s why the Ontology gets so much airtime in Palantir conversations, and our walkthrough of how the Ontology works goes further than I can here.

Can you build the same thing on a lakehouse or a warehouse? Yes, with effort. Roughly, your team would own these pieces.

• An object model the business recognizes, kept in step as the source tables change underneath it.
• Permissions. Per action, not only per table.
• Somewhere a planner can approve or reject a recommendation without opening a notebook.
• Who changed what, and why? You’ll want an audit trail that still answers that a year later.
• A path that pushes each approved change back to the system of record.

None of that is exotic. Strong data teams build it on Databricks and Snowflake every day. The real question is whether you want to own that glue for the next five years or buy it as a platform and spend your engineering hours somewhere else. For a 40-person data engineering group with a product mindset, owning it can be the right call. For an operations team that needs results this fiscal year, it usually isn’t.

Ontology design is also where most of our own projects begin, which is why Ontology design and build is its own service line at PAL4C. Get the objects wrong and every agent built on top inherits the mistake. Quietly.

How Do AIP, Databricks AI, and Snowflake Cortex Compare?

All three bring large language models to enterprise data. Snowflake Cortex runs AI functions and agents inside Snowflake, Databricks ships AI products like Genie on the lakehouse, and AIP grounds models in Foundry’s Ontology so they can propose governed actions.

Answering versus acting is the useful split. Cortex Analyst turns a natural-language question into SQL, which is a quick way to get an answer out of warehouse data. AIP functions and agents read Ontology objects and then propose changes through the same action types a person would use, so the output is a pending decision rather than a chart.

Nobody’s AI story is thin here. Databricks said in its February 2026 announcement that its AI products had passed a $1.4B revenue run-rate, and the same release names Lakebase and Genie among its products. Serious money. Cortex, for its part, gives Snowflake customers models from Anthropic, Meta, and Mistral without moving data out of Snowflake.

So where does AIP earn its keep? Inside decisions. Our AIP primer covers the components, and the AIP agent builds we deliver show what that looks like in production.

Real numbers help. For a global logistics operator, we unified 18 disconnected warehouse and inventory systems into one Foundry Ontology and added AIP forecasting agents on top. Procurement delays dropped 34%. Annual savings reached $12M. Deployment took 5 months. The forecasting model wasn’t the clever part, honestly. What paid off was that the agents’ recommendations landed on the same supplier and inventory objects the procurement team already worked with every day, so acting on one felt like a normal part of the job rather than a separate project somebody had to champion. The logistics case study has the fuller story.

How Does Pricing Work on Each Platform?

All three charge by consumption but meter it differently. Databricks bills DBUs per second, Snowflake bills credits for compute plus storage, and Foundry meters compute-seconds and GB-months, with no public price list.

Comparing headline rates is close to useless. A DBU, a credit, and a compute-second measure different work on different engines, so the only fair test is to price one real workload on each platform, with each vendor in the room, and compare totals.

Palantir’s usage types documentation defines the compute-second as its unit of work and measures storage and Ontology volume in GB-months. Databricks pricing describes the DBU as a normalized unit of processing. Snowflake’s pricing options lay out credits, capacity, and editions.

Short version? All usage-based.

Then there’s the longer version, the one that hits your budget. Cost follows workload shape, and the cheapest platform on paper turns expensive the moment you ask it to do a job it wasn’t designed for, whether that’s running heavy Spark-style transformations in a tool built for operational apps or bolting a homegrown approval workflow onto a warehouse.

We’ve seen Foundry bills feel heaviest when teams treat it as a general-purpose warehouse and copy everything in. Foundry’s Multimodal Data Plane exists so you don’t have to, reading Databricks and Snowflake data through virtual tables instead of duplicating it. Keep bulk storage and heavy transformation wherever it’s cheapest for you. Pay Foundry for the decisions.

Can Foundry Work with Databricks and Snowflake?

Yes, and both vendors have formal partnerships with Palantir. Foundry’s Multimodal Data Plane reads Databricks and Snowflake data through virtual tables without duplication, Snowflake supports zero-copy sharing through Iceberg, and Databricks can query Foundry tables in preview.

Four routes matter. Direction is the detail people skip, and it decides what you can actually design.

The Palantir and Databricks partnership combines AIP with the Databricks Data Intelligence Platform through Unity Catalog and Delta Sharing. Going the other way, Databricks catalog federation for Foundry lets Unity Catalog run read-only queries against Foundry Iceberg tables. It’s still labeled public preview. Check its status before you put it in a production design. The Snowflake and Palantir partnership runs both ways, with zero-copy interoperability between Snowflake Iceberg Tables and Foundry and AIP.

Is anyone actually doing this? A Databricks speaker at Data + AI Summit said joint Databricks and Palantir customers grew from about 30 to over 150. One vendor’s number, said on a stage. Still, it points the same way as everything else here.

Boring on purpose. That’s the architecture we recommend most. Heavy engineering stays where it already runs. Decisions get modeled in Foundry. Approved changes flow back. Most of the effort lands in Foundry data integration engineering, wiring up virtual tables, sharing, and sync so nobody ends up maintaining three copies of the same customer table and arguing about which one is right.

What the Latest Numbers Say, Briefly

Growth is real on all three sides. Palantir’s Q2 2026 results showed revenue of $1.935B, up 93% year over year, with US commercial revenue up 149% to $764M. Snowflake’s fiscal 2026 results reported product revenue of $4.47B, up 29%, and 733 customers with more than $1M in trailing 12-month product revenue. Databricks, still private, announced a $5.4B revenue run-rate in February 2026, growing over 65% year over year.

Careful lining these up. A run-rate isn’t a reported fiscal year, and one quarter isn’t a full year. Apples, oranges, and a pear. More to the point, none of these figures tells you which platform fits your stack. Demand signal? Yes. Fit test? No.

Which Platform Should You Choose?

Choose Databricks when data engineering and machine learning are your bottleneck, Snowflake when SQL analytics and BI are, and Foundry when the bottleneck is turning data into repeated operational decisions. Running two of the three together is normal.

Start from the constraint, not the feature list. Ask what’s actually slowing you down this year, then pick the layer that removes it.

• Pipelines, feature engineering, and model training fill your backlog. Databricks is the natural home.
• Are your analysts in SQL and BI tools all day, and is the main ask faster, governed reporting? Snowflake fits that brief well.
• Planners remake the same call every day using five systems and a spreadsheet. That’s Foundry territory, usually on top of whatever foundation you already run.
• Already happy on Databricks or Snowflake, and the gap is action? Add Foundry. Don’t migrate.
• No data team, three source systems, a reporting need. None of these yet.

If your gap sits right between reporting and action, our analytics and decision intelligence work lives on exactly that boundary.

When Databricks or Snowflake Alone Is the Better Call

Commercially, I’d rather skip this part. I’m writing it anyway, because a Foundry program bought for the wrong reasons burns budget and goodwill on every side, ours included.

Analytics is the whole job. If the output of your data program is dashboards and reports that people read in a weekly meeting, a lakehouse or a warehouse with a BI tool on top covers it, and Foundry’s Ontology and action layer would mostly sit idle.

You want to own the models. Teams that build, tune, and serve their own machine learning models, and want full control over that work, will find Databricks a more natural fit.

Your decisions are slow and few. A capital plan reviewed twice a year doesn’t need an operational layer. A good analyst will do.

You need to start small this quarter. Pay-as-you-go DBUs and on-demand Snowflake credits let a five-person team get going without a large upfront commitment, while Foundry is bought under a negotiated contract with Palantir, and that difference matters when the budget owner wants proof before signing anything multi-year.

Other Platforms Worth a Look

G2’s Foundry alternatives list includes Alteryx, Snowflake, SAS Viya, Databricks, Tableau, Dataiku, and Google BigQuery. They’re not one category. Tableau is a BI tool, Dataiku is a data science platform, and BigQuery is Google Cloud’s data warehouse, so the right pick depends on whether your gap is visualization, modeling, or storage. Know the gap first. Shop second.

Before You Sign Anything

The short version holds. Databricks and Snowflake store and shape enterprise data, each with serious AI tooling now. Foundry turns that data into operational decisions that write back, with AIP agents working on the same objects your people do. And because Palantir partners with both, the honest question isn’t which one wins. It’s which layer you’re missing.

Two habits will save you money. Price one real workload on each platform, not a benchmark. And name the decision you want to change before you name a vendor.

If you’re weighing Foundry against a stack you already run, our Foundry implementation guide lays out the phases and timelines. Want a second opinion on your own architecture? Ask for an architecture review from our implementation team, and we’ll map one recurring decision against your current lakehouse or warehouse and tell you whether Foundry belongs in the picture at all. Or book a stack review straight away. If the answer is no, you’ll hear it from us first.

What Buyers Ask Before Choosing

Is Foundry Just a Pricier Data Warehouse?

Can Foundry Sit on Top of Our Databricks Lakehouse?

Does Foundry Read Snowflake Data Without Copying It?

Will One of These Eventually Replace the Other Two?

How Do the Pricing Models Actually Stack Up?

If None of These Feel Right, What Else Belongs on the Shortlist?