Palantir Foundry Alternative: GroveStreams Compared

Short version

Palantir Foundry is a broad enterprise data and operations platform. Its centrepiece is the Foundry Ontology — a semantic model of the customer's objects, properties, links, and actions, layered on top of governed data pipelines. Around the ontology, Foundry adds a large surface: Pipeline Builder, Code Workbook, Code Repository, Workshop apps, Slate, Vertex (cross-system integration), Quiver (no-code data products), the Ontology SDK, and now AIP — AIP Logic, AIP Threads, AIP Agent Studio — for building LLM-powered workflows and agents over ontology data. Customers usually engage Forward Deployed Engineers (FDEs) to land the platform inside a program.

GroveStreams is a temporal intelligence platform built around one primitive: the stream. Every property, every relationship, and every measurement is a stream with its own independent time axis, queried through real SQL (GS SQL), with AI forecasting and an AI assistant built in. The footprint is intentionally narrower than Foundry's — one product, one query language, one bill — and the centre of gravity is any data with a time axis and relationships that change over time: energy storage, utility metering, manufacturing equipment, fleet operations, building systems, financial services.

If you are buying an enterprise operations platform with a broad workflow and ML surface, and you have the budget and the appetite for a multi-quarter rollout with embedded engineers, Foundry is built for that and the case for it doesn't come from this page. If you have a focused temporal-data problem — relationships change over time, you want native history, real SQL, predictable cost, and a path to a working model this week — GroveStreams is built for that, and the math leans differently.

What each platform is

Palantir Foundry is the enterprise platform Palantir wraps around its data-integration core. The surface is large; a partial list of the named pieces a customer typically encounters:

• Foundry Ontology — the semantic layer: object types, properties, link types, and actions, mapped from underlying datasets.
• Pipeline Builder — low-code data-transformation pipelines.
• Code Workbook and Code Repository — notebook-style and IDE-style authoring for PySpark / Spark SQL transforms and ontology functions.
• Workshop (and the older Slate) — the app builder for operational workflows on top of the ontology.
• Quiver — no-code data products and analyses.
• Vertex — system-of-system integration for ontology-driven operations across heterogeneous environments.
• Ontology SDK — TypeScript and Python SDKs that let developers work against ontology objects from outside Foundry.
• AIP — the AI surface: AIP Logic for LLM-orchestrated workflows over the ontology, AIP Threads for chat-style interaction, and AIP Agent Studio for agent authoring.

Programs are usually delivered with Forward Deployed Engineers (FDEs) — Palantir engineers embedded with the customer's team to model the domain, build pipelines, and stand up the first Workshop apps. The model is “Palantir comes in and operates the platform alongside you,” not “you sign up and start using it.”

GroveStreams is a single product, cloud-native, built around one primitive — the stream — and one query language — GS SQL. Templates declare entity schemas; streams hold timestamped values; relationships are themselves streams whose values are references to other entities. Roll-ups, derived streams, AI forecasting, an AI assistant, dashboards, alerts, OData/JDBC for BI tools, HTTP ingestion, branded subdomains for white-label deployments — all in one product, queried through one language, billed under one plan.

Ten years in production, 6,600+ organizations, 1.7M streams, 307K derivations. SOC2-certified data center, role-based access control, encryption at rest and in flight, 2FA, fine-grained API-key security. Self-serve sign-up; no required partner engagement, no embedded engineers.

Ontology: current-state graph vs. temporal relationships

The Foundry Ontology and the GroveStreams data model both let you describe a domain as objects with properties and links between them. The shape of “link” is where the two part company.

Foundry's ontology models the current state of the domain very well. Object types have properties; link types describe how object types relate; actions write changes back through governed code. When the underlying data evolves — an asset changes owner, a customer moves accounts, a piece of equipment is reassigned — the standard pattern is to keep history through dataset versioning, audit objects, time-indexed branches, or modelled “effective from / effective to” properties. Reconstructing “what link was in effect on March 14” is something an experienced Foundry team can do; it's also something that team has to build into the ontology and the pipelines.

GroveStreams takes a different shape. A relationship between two entities is itself a stream — a link stream. When Pump-A is reconnected from Tank-7 to Tank-12, a new data point is appended; the old connection stays in the history with its own timestamp. When the rate schedule revs, a new value is appended; the old one stays where it was, with its own timestamp. A query that asks “what link was in effect on March 14” or “what rate plan was attached to meter M-118 on March 14” resolves through the corresponding stream the same way it would resolve any other value at that time. Relationship history is not a feature you build; it is how the data is stored.

Foundry's strengths in this area are real and worth naming. The ontology is an exceptionally well-developed semantic layer — richer in pre-built action types, approval flows, and governed write-back UI than GroveStreams's template model. If the program needs that depth of action governance with a partner-led delivery motion, that is what Foundry is built for. Worth being precise about the GroveStreams side, though: its AI Assistant is not read-only. It runs GS SQL, and GS SQL includes DDL — CREATE, ALTER, and DROP on templates, streams, derivations, and views. An agent here can change the world too — in two senses. It can stand up an entirely new Temporal Intelligence org from a natural-language description, or restructure an existing one. And it can reach the outside world: stream events fire action packages — email and SMS, outbound HTTP calls, MQTT publishes — which an agent can compose with derived streams into a workflow. The difference is shape and polish, not whether the AI can write. Foundry's write surface is a turnkey catalog of individually-governed actions; GroveStreams's is the full DDL and DML grammar of GS SQL plus event action packages, assembled from primitives, all bounded by RBAC.

Query interface: Ontology SDK and Pipeline Builder vs. real SQL

Foundry exposes ontology data through several developer surfaces — the Ontology SDK (TypeScript and Python), Pipeline Builder for low-code transforms, Code Workbook and Code Repository for PySpark / Spark SQL, and various REST endpoints for app integrations. Each is fit for purpose. Cross-object, temporal queries that a SQL user expects to write as a single statement are typically expressed as a combination of SDK calls, ontology functions, and pipeline outputs materialized as new datasets.

GroveStreams ships GS SQL, a purpose-built SQL dialect. One virtual table — Stream — projects across every entity in the org. Anyone who knows SQL can write:

• Roll-up queries with built-in temporal accessors (sample(), last(), time filters).
• Cross-stream joins with && conditions that align mismatched time axes through roll-ups onto a common grid.
• Saved-query VIEWs that expose temporal results — including roll-ups, derived streams, and temporally-resolved relationship joins — to BI tools as standard tables.

GroveStreams also speaks the PostgreSQL wire protocol (Beta). Tableau, Power BI Desktop, Grafana, DBeaver, Excel, DataGrip, and psql connect directly with a user's email and password — no special driver, no API keys, no extra connector. For a team where the analysts already know SQL and the BI tools already know PostgreSQL, this is a much shorter line from data to answer than the Ontology SDK plus a materialized dataset.

AI: AIP vs. an in-product agent that runs GS SQL (DDL included), under RBAC

Both platforms have substantial AI stories. They are structured differently.

Foundry's AI surface is AIP: AIP Logic for LLM-orchestrated workflows that read and write the ontology under governed actions, AIP Threads for chat-style interaction grounded in ontology data, and AIP Agent Studio for authoring agents. The pairing of AIP with the ontology's action surface is genuinely powerful: an LLM-driven workflow can not only answer questions about objects, it can operate on them through the same governed actions that human users do. For programs where the AI is meant to drive operational decisions across many object types — supply-chain rebalancing, incident response, regulated workflows — AIP's scope is large.

GroveStreams takes a more opinionated, in-product approach — and the AI Assistant is more agent than chatbot:

• An AI Assistant in every org that reads templates, streams, samples, and metadata, and writes and runs GS SQL — including DDL. Because GS SQL carries the full CREATE / ALTER / DROP grammar for templates, streams, derivations, and views, the assistant does more than answer questions: it can build or restructure an entire Temporal Intelligence org from a natural-language brief — declare the templates, wire the streams and relationships, add roll-ups and derivations, stand up dashboards. Vibe-design a working temporal model in one conversation, or hand it an existing org and ask it to extend one.
• Governed by RBAC, not a side door — on every path. An interactive agent runs as the signed-in user and is bound by that user's role-based access control. A scheduled agent runs as a “Run As” user configured on the schedule; an agent driven through an API key runs as the “Run As” user assigned to the key. Every automation path therefore has an explicit, named identity with its own permissions and audit trail — the agent can reach across the org only as far as that identity is allowed to. There is no separate, less-governed agent path.
• Scheduled agents. Recurring, autonomous agent runs — forecast-and-alert, reconcile, summarize, restructure on a cadence — execute under the schedule's Run-As identity, so an unattended run is governed exactly like the human or service account behind it.
• Multi-provider — OpenAI, Anthropic, Gemini, xAI, with org-controlled keys — so the model choice is yours, not the vendor's.
• MCP — GroveStreams speaks the Model Context Protocol, so external agents and agentic IDEs can discover templates and drive the platform through the same governed GS SQL surface.
• Actions on the outside world. GroveStreams can act, not only read and compute. Stream events fire start- and stop-action packages — email and SMS notifications, outbound HTTP calls, MQTT publishes. Composed with derived streams (Excel-style formulas that recompute in real time), these become a multi-step workflow: a derived stream watches a condition, an event triggers, and the action package calls out to whatever needs to move. An agent can assemble that whole chain. It is a composition of primitives rather than Foundry's turnkey, individually-governed action catalog — less polished for heavy operational write-back — but the path from a condition to a real-world side effect is real, and the agent can build it.
• Eight built-in forecasting model types — TFT, N-BEATS, ARIMA, Prophet, TCN, Transformer, Exponential Smoothing, RNN — with a Model Builder and Wizard. Each model trains on one or many input streams and emits a forecast stream. Pick the streams, pick a horizon, run a forecast. (Details: Forecasting docs.)
• Built-in correlation detection across streams.

So the honest framing is not “Foundry can act, GroveStreams can only answer.” Both AI surfaces can change the world. They differ in shape: AIP drives a catalog of pre-modelled, individually-governed ontology actions — the right fit for regulated cross-object operational workflows with per-action approval. GroveStreams hands the agent the full GS SQL grammar — query and schema — plus event action packages (notifications, HTTP, MQTT) it can wire to derived-stream conditions, all bounded by RBAC; the right fit when the goal is to vibe-design, populate, query, forecast, and act over a temporal-relationship model fast. Foundry's action surface is more turnkey and more deeply governed; GroveStreams's is assembled from primitives but reaches the outside world all the same. GroveStreams also gives a much shorter path from “I have a stream” to “here is the forecast” and from “I have a question” to “here is a SQL answer and a dashboard,” without standing up a parallel program. For an evaluator, the question is whether the centre of gravity is governed cross-object workflows (Foundry / AIP) or designing and operating a temporal-data model in real SQL, with forecasting built in (GroveStreams).

Deployment and onboarding: FDE-led vs. self-serve

Foundry programs are typically delivered as Palantir engagements. Forward Deployed Engineers embed with the customer team to model the ontology, build the pipelines, and stand up the first Workshop apps. There are good reasons for this model — Foundry is a broad platform, the ontology is a serious modelling exercise, and a partner-led motion accelerates real outcomes for programs that warrant it. It also means that time from purchase to a production-ready environment is measured in months and often quarters, and that the early skill base for the platform inside the customer often sits with the FDEs, not the customer's team.

GroveStreams is cloud-native and self-serve. Data ingestion is HTTP REST. Storage, queries, dashboards, the AI Assistant, and the API are all delivered as SaaS. For sites that need to keep data inside specific boundaries, a private-cluster deployment is available. There is no embedded engineering team, no FDE motion, no separate cloud service to bridge into. A skilled DBA, data architect, or domain expert can build a working temporal model without a systems-integration engagement — and the AI Assistant can help vibe-design templates and dashboards from natural-language descriptions if that fits the team better.

Pricing: custom enterprise contract vs. public per-org tiers

Palantir Foundry pricing is not public. Customer contracts are bespoke and reflect the scope of the program: which platform components are in use, how many users and how many object types, how much data is moving through pipelines, and whether AIP and Forward Deployed Engineers are part of the engagement. Enterprise Foundry programs are commonly seven- and eight-figure annual commitments. That is appropriate for what Foundry is and the scope it covers; it is also a different category of investment from picking a focused platform for a focused problem.

GroveStreams pricing is public, predictable, and self-serve. See pricing for current tiers. There is a free tier; a working environment can be running in an afternoon. The platform is designed to be operated by the customer, not by a partner on the customer's behalf. For a temporal-data program where the cost of the platform should be a reasonable line on a reasonable budget — not the largest one — the math is genuinely different.

At-a-glance comparison

When to pick Palantir Foundry

• The program needs a broad enterprise platform — ontology, governed pipelines, operational apps, LLM-driven workflows — not a focused temporal-data platform.
• The semantic surface needs deep action governance: LLM workflows or human users writing back to the ontology through governed actions with audit and approval.
• Workshop / Slate-style operational apps with rich UI are central to what you're delivering, not a side benefit of having the data.
• Your organization has the budget and the appetite for a partner-led, Forward Deployed engagement and a multi-quarter rollout.
• The ontology spans many object types and the work is genuinely about governing how a large enterprise reasons about its objects — not specifically about temporal relationships and forecasts.

When to pick GroveStreams

• You want every value and every relationship to have temporal history natively — not reconstructed from dataset branches, audit objects, or effective-date properties.
• You want real SQL (and PostgreSQL-compatible BI tooling) as the primary query interface for analysts, not Ontology SDK code or pipeline outputs materialized as datasets.
• You want AI forecasting built into the platform — eight model types, in-product wizard — without standing up a separate ML or AIP program.
• You want an AI agent that can act, not just chat — one that runs GS SQL including DDL to vibe-design or restructure an entire org, bounded by the same RBAC as a human user, reachable over MCP — without a separate agent-authoring program.
• You want public, predictable pricing and a free tier you can evaluate without a procurement cycle, an FDE, or an MSA.
• Time-to-value matters: you want a working model this week, not this quarter.
• Your domain is temporal-data-with-relationships — energy storage, utility metering, manufacturing equipment, fleet operations, building systems, financial services, agriculture, logistics, healthcare — where the centre of gravity is “the values and the links both change over time and we need to answer questions across that history.”

Honest note

Foundry and GroveStreams are not the same product, and this page is not arguing that they are. Foundry is a much broader platform — the ontology, the action surface, AIP, Workshop, Pipeline Builder, Vertex — and for programs that genuinely need all of it, the case for Foundry comes from somewhere other than a comparison page. The case for GroveStreams here is narrower and more specific: if the centre of gravity of your problem is temporal data with relationships that change over time, then a focused platform built around that primitive — with real SQL, native relationship history, built-in forecasting, public pricing, and a path to a working model this week — will get you to an answer faster, at a cost that's a reasonable line on a reasonable budget.

Still weighing options? For the closest industrial DataOps peer, see our Cognite Data Fusion comparison; for the historian-replacement decision, see our AVEVA PI comparison. For the broader category, browse the Compare hub — we publish additional comparisons (Seeq, InfluxDB, TimescaleDB, Snowflake, PostgreSQL) and a category-wide roundup over the coming weeks.