Hopsworks 5.0, the unified and Sovereign Data and AI platform built around the Coding AI and Data Stack; a new paradigm where coding agents replace fragmented tooling to accelerate the development and maintenance of ML pipelines, data workflows, and AI applications and much more. It marks a new chapter in how AI systems get built and maintained. For the first time, teams have a full coding agent and terminal embedded inside the platform itself, pre-loaded with Claude Code and Codex.
Hopsworks 5.0 introduces Coding Data and AI Stack with embedded coding agents.
Development containers with Claude Code and Codex pre-installed for unified development.
New UI delivers 50% lower latency with Wizard interface for guided pipeline setup.
Platform Intelligence automates data source connections with LLM-generated descriptions.
Native Trino SQL query capabilities and Apache Superset dashboarding layer.
Column-level access control for granular data sharing across teams.
A terminal and development containers are built directly inside Hopsworks to allow agents to instrument the platform, giving developers a unified environment to build and operate the full ML stack; feature pipelines, training pipelines, inference pipelines, Streamlit applications, and analytics dashboards without leaving the platform. Claude Code and Codex come pre-installed, and multiple agent sessions can run in parallel, so developers can work across different parts of a project simultaneously. Developers describe what they want to build in plain language, and the agent writes, runs, and iterates on the code. Sessions are fully persistent across restarts.
A completely redesigned user interface delivers 50% lower latency across the most common platform tasks and simplifies user experience across the whole platform with the help of the Wizard interface. The Wizard allows users to select any case, such as time series forecasting, auto research, and others, automatically driving through steps and a guided setup that delivers a production-ready pipeline with a coding agent.
An intelligent agent is embedded across key platform workflows. When connecting external data sources from Databricks, Snowflake, BigQuery, MongoDB, SAP HANA, and others, Platform Intelligence uses an LLM to generate human-readable column names and descriptions, infer primary keys and event time columns, and configure ingestion schedules automatically. Hopsworks now supports native SQL query capabilities powered by Trino and a full dashboarding layer via Apache Superset. Teams can run exploratory SQL queries, build and share dashboards, and export results as PDFs or PNGs without leaving Hopsworks.
Hopsworks 5.0 introduces a significantly expanded set of data sources alongside two new ways to work with external data: mounting external tables without copying data, and ingesting data to Hopsworks using DLTHub on a configurable schedule. Column-level access control enables the sharing of selected columns from tables to teams in different projects, giving data owners precise control over what columns are shared, with whom. Models can now be imported directly from Hugging Face, including the model card documentation.
Jim Dowling, CEO of Hopsworks, stated: "Hopsworks 5.0 is our most ambitious release yet. It is a big step in moving Hopsworks from a MLOps platform to an AI Lakehouse, with support now for dashboards at scale, a new query engine, Trino, and native Apache Iceberg support. But, Hopsworks 5.0 is not a me-too Lakehouse. It is a new class of AI Lakehouse powered by coding agents. The low-code and no-code tooling that dominates other Lakehouses is obsolete. Instead, we have added first class support for building and operating pipelines, agents, apps, and dashboards from a single Terminal UI."
About Hopsworks
Hopsworks is the leading unified platform for building batch, real-time, and LLM-powered AI systems, enabling organisations to go from raw data to production-ready AI and Data products at scale. As a European-founded, sovereign AI platform, Hopsworks is purpose-built for organisations that require data sovereignty and regulatory compliance. At its core is a powerful AI Lakehouse, combining a best-in-class feature store, model registry, and vector database covering the full pipeline lifecycle from data ingestion to batch, real-time, and agentic inference.
