AVEVA PI Alternative: GroveStreams Compared

Short version

AVEVA PI System is the dominant operational historian in heavy industry. It is mature, deeply trusted, and woven into compliance frameworks and contract language that no new entrant can replicate quickly. Its data model is anchored by the PI Data Archive (a tag-oriented time-series store) and the PI Asset Framework (AF), a hierarchical asset model with attributes, AF Tables, and Event Frames. AVEVA CONNECT is the cloud platform that extends the PI footprint — data services in the cloud, with the existing PI System bridging in.

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. Cloud-native from day one. Self-serve. Centre of gravity: 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 protecting a brownfield deployment with deep OPC integrations, AF hierarchies, and regulatory paperwork tied to PI tag names, AVEVA PI is built for that and a like-for-like rip-and-replace is rarely a good idea. If you are picking a successor for the next ten years — or starting a new program where the data needs real SQL, native relationship history, forecasting, and an AI assistant — GroveStreams is built for that.

What each platform is

AVEVA PI System (originally developed by OSIsoft, acquired by AVEVA in 2021) is the on-premises historian most heavy-industry organizations standardize on. The product family is broad:

• PI Data Archive — the time-series store, organized around tags (one identifier per measurement).
• PI Asset Framework (AF) — the hierarchical model that lets teams group tags into assets with attributes, templates, and parent/child relationships.
• AF Tables — relational reference data joined to AF (rate schedules, equipment specs, unit conversions).
• Event Frames — time-bounded events such as batches, downtime, alarms, or excursions.
• PI Vision — the modern web visualization layer for dashboards and displays.
• PI Integrator / PI Web API / PI DataLink — integration surfaces for downstream systems (and the long-running Excel add-in that a generation of process engineers still uses every day).
• Connectors and Interfaces — an exceptionally deep catalog covering OPC DA, OPC UA, OPC HDA, and dozens of vendor-specific control-system protocols.

AVEVA CONNECT is the cloud platform that surrounds the PI footprint. It provides cloud data services for time-series, sharing, and downstream applications, and gives organizations a path to expose PI data outside the four walls of the plant without standing up additional on-prem infrastructure. The standard pattern today is “PI on-prem at the site, CONNECT in the cloud, bridging between them” rather than a clean lift-and-shift.

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; link streams hold timestamped relationships. 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.

The data model: AF + AF Tables vs. streams-all-the-way-down

The most important difference between the two platforms is where time lives.

PI's data model separates time-bearing data (tags in the Data Archive) from the surrounding context (AF hierarchies, AF Tables, and Event Frames). AF gives you a hierarchy of assets with attributes and templates, which is a clean way to organize equipment. AF Tables let you attach reference data — rate schedules, tariff tiers, equipment specs — and reference them from analyses. But AF Tables are designed as relational reference data; they are not natively time-bearing in the way tags are. When the reference data changes — a rate schedule revision, a unit conversion update, a parent-asset reassignment — the standard pattern is to keep history through Event Frames, additional tags that record the change, or external versioning. Reconstructing “what rate schedule was in effect on March 14” or “which feed line was this pump on last quarter” is something an experienced PI team can absolutely do; it's also something that team had to build.

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 rate was in effect on March 14” or “what tank was Pump-A feeding on March 14” resolves through the corresponding stream the same way it would resolve any other value at that time. Relationship history and reference-data history are not features you build; they are how the data is stored.

PI's strengths in this area are real and worth naming. AF is an extremely well-understood modelling surface. Event Frames give a clean primitive for batches and incidents. The PI ecosystem — templated analyses, Notifications, Integrator-driven exports — was tuned over twenty years against the specific patterns of process industry. If your organization has standardized analyses, displays, and regulatory reports against an AF hierarchy that already works, the value in that hierarchy is not just the schema; it's the institutional knowledge encoded around it.

Query language: AF SDK and PI SQL vs. real SQL

PI exposes data through several developer surfaces — PI Web API (REST), AF SDK (.NET), PI ODBC / PI SQL Commander (a SQL-shaped surface over PI data with PI-specific extensions), and PI DataLink for Excel. Each is fit for purpose, and each is its own thing to learn. Cross-asset, cross-time queries that a SQL user expects to write as a single statement are typically expressed as a combination of AF SDK code, calculated tags, and Excel.

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 OData URL. For a team where the analysts already know SQL and the BI tools already know PostgreSQL, this collapses a lot of integration work that historically lived in PI DataLink workbooks or AF SDK utilities.

AI: built-in forecasting and an in-product agent that acts under RBAC

Both platforms have AI stories. They are structured differently.

AVEVA's AI offering is a portfolio — AVEVA Predictive Analytics for asset-level early-warning models, AVEVA Insight for operational analytics, and broader Connected Worker AI capabilities. These are powerful in their target use cases (rotating-equipment health, anomaly detection on specific asset classes) and they are deployed through AVEVA programs alongside PI. They are not, however, a single “point at a tag, get a forecast” surface inside the historian itself.

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

• 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.)
• 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. Vibe-design a working temporal model in one conversation, or extend an existing one.
• Governed by RBAC, not a side door — on every path. An interactive agent runs as the signed-in user, 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 has an explicit, named identity with its own permissions and audit trail.
• Scheduled agents — recurring, autonomous agent runs (forecast-and-alert, reconcile, summarize) execute under the schedule's Run-As identity.
• Actions on the outside world. Stream events fire start/stop action packages — email and SMS notifications, outbound HTTP calls, MQTT publishes. Composed with derived streams (Excel-style real-time formulas), they become a multi-step workflow: a derived stream watches a condition, an event triggers, the action package calls out — and an agent can assemble the whole chain. It is a composition of primitives rather than a turnkey action catalog, but the path from a condition to a real-world side effect is real.
• MCP — GroveStreams speaks the Model Context Protocol, so external agents and agentic IDEs can drive the platform through the same governed GS SQL surface.
• Multi-provider — OpenAI, Anthropic, Gemini, xAI, with org-controlled keys.
• Built-in correlation detection across streams.

AVEVA's AI portfolio is broader along the asset-management and condition-monitoring axis — predictive models tuned to rotating equipment and specific asset classes are real strengths. GroveStreams's AI is an in-product agent: it gives a much faster path from “I have a stream” to “here is the forecast,” and from “here is what I want” to a built-or-restructured org that queries, forecasts, and — through event action packages — acts, all under RBAC, without standing up a parallel ML pipeline or a separate program.

Cloud path: on-prem + CONNECT vs. cloud-native

PI was designed on-prem, and even with AVEVA CONNECT extending it, the operational reality is hybrid: the Data Archive and AF still live at the site, CONNECT sits in the cloud, and a bridge in between keeps the data moving. There are good reasons for that — air-gapped sites, deterministic OPC interfaces, control-system network constraints, regulatory boundaries. It also means that a clean “turn the historian into a cloud platform” migration is not really a button.

GroveStreams is cloud-native. 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 on-prem appliance to maintain, no version upgrades to schedule with a control-systems window, no separate cloud service to bridge into.

Pricing and onboarding

AVEVA PI's pricing is not public. Enterprise PI deployments are commonly six- and seven-figure annual commitments, structured around the components in use (Data Archive, AF, Vision, Integrator, Notifications, Connectors), the size of the site footprint, and the cloud services consumed through CONNECT. Initial deployment, AF hierarchy design, and integration work are typically performed alongside a partner. Time from purchase to a production-ready environment is measured in months at minimum and often quarters.

GroveStreams pricing is public, predictable, and self-serve. See pricing for current tiers. There is a free tier; you can sign up and have a model running in an afternoon. The platform is designed to be operated by the customer, not by a partner on the customer's behalf. 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.

At-a-glance comparison

When to pick AVEVA PI

• You are running a heavy-process site (oil & gas, power generation, chemicals, refining, large-scale manufacturing) with decades of accumulated PI tags, AF hierarchies, and analyses already in production.
• OPC-DA / OPC-UA / OPC-HDA coverage and vendor-specific control-system connectors are core to the ingestion problem.
• Regulatory frameworks reference PI tag conventions and AF analyses directly.
• You have, or plan to engage, an AVEVA partner to lead the deployment and the cloud bridging.
• Your operations teams live in PI Vision and PI DataLink and switching them is a multi-year change-management programme in itself.

When to pick GroveStreams

• You want every value and every relationship to have temporal history natively — not reconstructed from Event Frames, audit tables, or change-tracking tags.
• You want real SQL (and PostgreSQL-compatible BI tooling) as the primary query interface for analysts, not AF SDK code wrapped around a SQL veneer.
• You want AI forecasting built into the platform — eight model types, in-product wizard — without standing up a separate predictive-analytics program.
• You want a cloud-native platform with self-serve sign-up and predictable pricing, not a hybrid on-prem + cloud topology with partner-led onboarding.
• Your domain is industrial-adjacent but not heavy-process — energy storage, utility metering, fleet operations, building systems, financial services, agriculture, logistics, healthcare — where PI was overbuilt for the use case and underbuilt for the relational and reference-data needs.
• You want an AI agent that can act, not just chat — vibe-design templates and whole orgs from natural language, run GS SQL including DDL, and fire real-world actions (notifications, HTTP, MQTT) off event-driven conditions — all bounded by the same RBAC as a human user, reachable over MCP.

Honest note

AVEVA PI is the historian most heavy-industry organizations standardize on for good reasons. The install base, the OPC depth, the regulatory acceptance, and the AF modelling tradition are real assets, and a like-for-like replacement at a working site is rarely the right move. The case for GroveStreams is not “rip out PI everywhere.” The case is: when the next program comes up — a new line, a new fleet, a new analytics initiative, a CONNECT renewal that is being re-scoped — the temporal-by-default model and the real-SQL surface make the path from data to answer materially shorter, and the relationship-history primitive eliminates a class of work that a PI-plus-AF deployment has to assemble.

Still weighing options? For the closest industrial DataOps peer, see our Cognite Data Fusion comparison. For the ontology + temporal angle, see our Palantir Foundry 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.