Palantir Foundry Alternative: Start With One Workflow
A focused open-source context layer can be a Palantir Foundry alternative for one operational workflow, such as a delivery blocker linked to customer commitments and an approved action. It is a fit when the required sources, users and writes are bounded. Full-platform requirements need a broader evaluation.
Last tested: September 2026. The local synthetic workflow and SDK were tested; third-party platforms were reviewed from their documentation.
For a team with one recurring customer-delivery problem, I recommend testing that workflow before committing to a platform replacement. Keep the existing systems of record and make the missing connection explicit.
| Your requirement | Starting point |
|---|---|
| One customer workflow across a few existing systems | Evaluate a focused context layer against that workflow |
| A shared platform for many data pipelines, applications and governance needs | Evaluate Foundry and other full platforms with a representative workload |
| Temporal facts and evidence retrieval inside an application | Evaluate Graphiti or Graphiti Local as a component |
| Learn or develop inside Palantir’s ecosystem | Evaluate its developer tier and Ontology SDK |
What a Palantir Foundry alternative needs to replace
“Palantir alternative” is too broad for a useful estimate. Foundry’s scope includes data integration, an ontology, application development and governance. Its Ontology SDK treats Foundry as the backend. Downloading an SDK does not replace that backend.
Write down the actual user story first: who needs which information, what they may see, what changes, who approves a write, and where the result belongs. Those boundaries determine whether a small implementation is enough.
For example: a supplier misses an interface specification. The delivery ticket says “Blocked”, the CRM still carries the agreed rollout date, and a wiki decision says the customer must confirm a revised date. The delivery lead needs those three records together before acting.
Open-source ontology components and the workflow around them
Graphiti provides temporal context graphs, provenance and typed entities. Graphiti Local adds a read interface and a separate proposal workflow. My agent-approval-gate repository provides reusable approval contracts.
These components address different parts of the system. A graph does not automatically inherit your Jira permissions or implement a reliable write-back. Someone must connect source identity, access rules, evidence versions and actions.
The operational context-layer case study describes that wider client pattern. The downloadable starter below is a separate implementation with fictional records. It is not the client’s code or a copy of its environment.
Try the delivery-blocker workflow
Download the MIT-licensed source and Python SDK. Extract the archive, open the context-starter directory, and use Python 3.12 or later:
python3 -m venv .venv
source .venv/bin/activate
python -m pip install .
determa-context serve
Open http://127.0.0.1:8787. Initial dependency installation needs internet access; the running synthetic demo makes no external requests.
- Read the blocker and its linked customer records.
- Edit a comment and prepare it for review.
- Inspect the exact wording, approve it, then post to the local Jira simulator.
- Check the receipt and action trail.
- Prepare a different comment, close its review, and simulate a source update. Reopen the pending proposal and attempt approval: the changed source invalidates it.
Switch the demo person to inspect a different set of available records. This illustrates access filtering; the selector is not a login system.
The SDK exposes the same workflow:
from determa_context import Context
from determa_context.connectors import MAYA, seed
ctx = Context("example.sqlite3")
seed(ctx) # fictional records only
signals = ctx.signals(MAYA)
proposal = ctx.propose(
MAYA, "jira-aurora", "Please confirm the revised test date."
)
# Show the proposal to a person before calling review and dispatch.
The starter stores workflow state in SQLite. It exports dispatched decisions in Graphiti Local’s JSONL format for manual ingestion into a separate demo graph. Graph retrieval is not part of this demonstration.
Compare scope before comparing price
| Capability | This starter | Work required for a client deployment |
|---|---|---|
| Customer context | Explicit links between three synthetic records | Real connectors, mapping and synchronization |
| Access | Two demonstration identities and source groups | SSO, trusted identity resolution and delegated permissions |
| Approval | Exact payload, source revisions and expiry | Organization-specific approval roles and channels |
| Write-back | Local Jira simulator with a persistent receipt | Live API integration, ambiguity handling and reconciliation |
| Repeated dispatch | One simulator effect per proposal | Connector-specific retry guarantees |
| Audit | Local hash-linked events | Retention, access, monitoring and storage protection |
| Graph | Manual export for Graphiti Local | Deployment, ingestion policy and evaluated retrieval |
Palantir advertises a free developer tier. Production economics need a separate comparison. Count license or subscription fees, implementation, hosting, model usage, connector maintenance, incident response and handover. Apply the same users, workflow and time horizon to both options.
Use Foundry when your evaluation calls for a supported platform spanning many workflows and its capabilities meet the acceptance criteria. Use the starter to investigate a smaller, clearly bounded requirement. Keep the current process if the measurable benefit does not justify another system.
Turn the example into a scoped engagement
The first engagement should answer one question: can this workflow run correctly on your sources, under your permissions, at an acceptable operating cost?
Determa’s context-layer implementation offer begins with a free scoping call. Bring the workflow owner, the systems involved and one representative failure. If useful, a separately commissioned, paid assessment produces a source/access map, acceptance criteria, a build-or-buy recommendation and an implementation estimate by phase.
Changelog
- September 2026: Initial comparison and runnable synthetic starter. The page explains the backend dependency of Palantir’s SDK and distinguishes the tested local workflow from client deployment work.
Frequently asked questions
Is there an open-source alternative to Palantir Foundry?
There are open-source components and projects covering parts of the problem. A focused context layer can serve a defined workflow; it does not provide the entire Foundry platform. Compare the operational scope and support requirements before choosing.
Does the Palantir Ontology SDK work without Foundry?
Palantir describes the Ontology SDK as a way to build applications with Foundry as the backend. Public SDK code does not provide an independent, self-hosted Foundry server.
Is a self-hosted context layer cheaper than Palantir?
That depends on the workflow and operating model. Compare implementation, connector upkeep, infrastructure, models, security, support and exit costs over the same period. A free SDK or developer tier does not establish production cost.
What does the Determa Context Starter demonstrate?
A synthetic delivery blocker linked to customer evidence, a reviewed comment, and a persistent receipt in a local Jira simulator. It includes a Python SDK, source revision checks and demonstration access groups. It makes no live Jira calls.
Can the starter run on premises or inside the EU?
The synthetic starter runs on a local machine without model calls. A client deployment needs an agreed identity, connector, hosting and model configuration; data residency must be verified for that complete configuration.
Can a team build on the starter without hiring Determa?
Yes. The source download is MIT licensed and includes installation instructions, a Python SDK and tests. Production connectors, identity integration and operational support are separate implementation work.
Your agents answer from whatever the retriever finds, and too often that is last quarter's truth. I build the context layer they answer and act from: a temporal knowledge graph that keeps every fact with its source and the time it held, reads with each person's own permissions, and writes nothing without a person's approval. On your own tenant, billed by the hour, step by step.