Choosing between Foundry Workshop and a custom web application starts with the workflow, data foundation and people who will operate it. A team that already has governed operational data in Foundry faces a different decision from a business that needs one application on its existing cloud stack.
This guide compares three architectural options: Workshop, a custom frontend backed by Foundry and a custom application with an independently managed backend. It is a scoping framework, not a claim that one approach is always faster or cheaper. Examples and effort figures are hypothetical, not client results or Dataphian quotations.
Understand the three options
1. A Workshop application
Palantir describes Workshop as an application builder for operational users. It uses object data, Actions for writeback and Functions for business logic. That makes it relevant to an interface for investigating records and taking governed actions, rather than just displaying a static report.
For an existing Foundry organization, assess whether the needed object types, relationships and actions are already suitable for the workflow. If they are not, the project includes data and Ontology work as well as the interface. A low-code screen does not remove that dependency.
2. A custom frontend backed by Foundry
The choice is not limited to Workshop or abandoning Foundry. Palantir's Ontology SDK documentation describes building custom applications that query and write back to the Ontology, including React applications. A custom interface can therefore retain Foundry as its data and governance foundation.
This option deserves investigation when the data model fits but the required user journey needs a different interaction design. Confirm supported APIs, authentication, application permissions, deployment and commercial terms for the intended environment. A custom frontend does not automatically remove platform dependencies.
3. An application with an independent backend
A custom web application can use a backend, database, identity service and integrations operated outside Foundry. That gives the team architectural choices, but also responsibility for data access, authorization, audit history, deployment, monitoring and recovery.
This can be an appropriate scope when the business needs a bounded workflow on its existing systems and has no requirement to make Foundry the data foundation. Avoid comparing its first screen with a complete platform-backed workflow. Compare the full system needed for the same accepted outcome.
When Workshop is worth evaluating first
Consider a hypothetical operations team investigating delayed orders. Customer, order and shipment objects already exist in Foundry, their freshness meets the operational need, and the team has approved actions for assignment and escalation. A Workshop proof can test the operator journey against that existing foundation.
In this situation, ask whether the available components express the important interactions clearly, whether all intended operators can access the required resources and whether the team can own the module and its dependencies. Reuse has value only when the underlying model and permissions match the work.
Do not assume that an existing Foundry contract means every new workflow, user group or workload has no additional cost or approval requirement. Obtain confirmation from the platform and commercial owners before using that assumption in a budget.
When a custom application deserves a closer look
Consider another hypothetical business that needs a branded customer journey connecting a CRM, document store and a small operational database. It has no established Foundry data model for the workflow. An independently managed application or existing SaaS product may be a more proportionate option to investigate.
Reasons to test a custom approach can include specific interaction requirements, an existing application the workflow must extend, distribution constraints or a team with an established release process. These are evaluation criteria, not proof that Workshop cannot meet a requirement. Demonstrate the actual journey and verify platform capabilities before deciding.
If the audience includes external customers, assess identity, invitations, tenant boundaries, support and licensing for the intended user model. Do not infer permission to expose platform data merely because a prototype works for an internal administrator. Our customer portal guide provides a checklist for that separate audience.
Compare ownership across the whole workflow
Data model and freshness
Write down the source of truth for each operational fact and the freshness required for a decision. In a Foundry-backed option, identify the necessary object types, links, pipelines and actions. In an independent backend, identify the equivalent records, integrations and update rules.
A screenshot with correct sample data does not prove that updates arrive reliably. Include missing records, stale inputs and source outages in the evaluation. If reconciling sources is the main challenge, use the questions in our data engineering guide to scope that foundation first.
Permissions and governed actions
Workshop's permissions documentation distinguishes access to the module from access to its data, actions and functions. Test with real operator roles, not just a builder account. Opening an application is not proof that every intended action is available or appropriately restricted.
For a custom backend, implement and test the equivalent record and action authorization on the server. For a custom frontend backed by Foundry, assess both the application's access configuration and the user's effective permissions. In every option, document who may view, modify, approve and administer the workflow.
Release, support and change ownership
Identify how an interface change is reviewed and released, how it depends on model or API changes and who responds when something fails. Decide which environment contains test data and how production changes are recovered or reversed.
For Workshop, assign ownership of the module and supporting Foundry resources. For a custom frontend, also assign repository, dependency and deployment maintenance. For an independent application, include database, backups, hosting, identity and integration operations. A short initial build does not establish a low lifetime support burden.
Portability and contractual ownership
Separate ownership of source code, application configuration, data and the right to use a platform. Confirm those rights in the relevant agreements. A custom application can still depend on vendor-specific services, just as a Foundry-backed interface depends on its platform resources.
Ask what another team would need to operate the system, which exports and documentation exist and how a future change of platform would be handled. Do not promise a frictionless migration from either approach without inspecting the data model, business logic and integration dependencies.
Run a proof on one operational decision
- Choose the decision — one operator, one question and one permitted action.
- Check the foundation — source data, freshness, object/record model and identity.
- Test viable options — the same journey and representative records in each candidate.
- Exercise failures — denied access, stale data, invalid actions and interrupted writes.
- Compare ownership — delivery effort, access/licensing, operations and exit dependencies.
Reject an option when a required condition fails; use evidence from the workflow rather than a generic platform preference.
Write acceptance criteria before the proof. For the delayed-order example, an operator must find the relevant order, understand the source freshness, assign an allowed owner and see the recorded outcome. A user without permission must be unable to perform that action. Include representative volume and response expectations agreed with the operator team.
Time-box the proof and record assumptions that were not tested. A sample built with one account and a tiny dataset does not establish performance, external-user access or commercial viability. If a dependency is unavailable, report it as unresolved rather than silently choosing a favorable substitute.
Costs: compare the same boundary
Use a worksheet with discovery, data/model work, interface work, permissions, testing, release, recurring platform charges and support. Workshop, an OSDK frontend and an independent app may share some costs while allocating others differently. Existing platform investment can affect marginal cost, but it should not hide new work or commercial obligations.
For illustration only, assume an agreed engineering rate of USD 75/hour:
- Workflow and platform-fit investigation: 40–60 hours gives USD 3,000–4,500. Confirm data access, user roles and the riskiest interaction before estimating delivery.
- Example implementation calculation: 40 hours of discovery + 60 data/model work + 100 interface/action work + 40 permissions and testing + 20 release/handoff = 260 hours, or USD 19,500.
- Dependency sensitivity: if data/model work is 180 rather than 60 hours, the same worksheet becomes 380 hours, or USD 28,500.
These are invented inputs, not estimates for any particular option, market prices or Dataphian quotations. The point is to expose the work being compared. Platform licensing, hosting, identity services, taxes, internal staff and recurring support are excluded and must be added using actual agreements and requirements. No Palantir license price, entitlement or partnership status is implied.
A bounded first release
- One operator group, one operational decision and one controlled action.
- A confirmed data source, agreed freshness, a minimal object/record model and sample cases.
- A written role matrix, tested allowed/denied behavior and an attributable action history.
- Clear failure messages, a recovery owner and a procedure for pausing writes.
- A release path, documentation, support ownership and a review of access and commercial dependencies.
Exclude a platform-wide migration, wholesale Ontology redesign, a second user audience and autonomous AI actions unless required for that first decision. AIP can be assessed as a separate workflow capability; choosing Workshop does not itself require introducing an AI agent.
Start with a workflow and the environment you already have
Dataphian offers Palantir Foundry and AIP engineering alongside full-stack product engineering. Bring the operator journey, existing data model, user-access requirements and the constraint you most need to test. Dataphian's scope is engineering; platform access and licensing are handled separately.
If the current workflow lives in a workbook, our spreadsheet-to-internal-tool guide can help identify the rules and records before comparing application platforms. For the Foundry data-model concepts, see the Ontology overview.