Forecasts with confidence intervals, regressions, significance tests — the analysis SQL can't express — now run inside Cube. With Python Analysis, a Workbook tab can carry a Python script alongside its semantic query: the script reads the query results as data.csv, writes a chart to output.json, and runs against a sandbox with pandas, Prophet, and the rest of the scientific stack pre-installed. Because the input comes from your semantic model, a forecast uses the same governed metric definitions as every dashboard.
You don't have to write the code yourself — ask Analytics Chat to "forecast next quarter's revenue" and the agent writes and runs the analysis, reaching for Python only when SQL genuinely can't answer the question. The script and its results are saved as part of the workbook, so a Python-backed report can be published to a Dashboard, re-run on view, and filtered by dashboard filters like any other chart — including on embedded surfaces. Read more in the announcement.
Cube now speaks your team's language. The interface is fully translated into 12 locales — English, German, Spanish (Spain and Latin America), French, Italian, Japanese, Norwegian Bokmål, Portuguese (Brazil and Portugal), Swedish, and Vietnamese — across Workbooks, Dashboards, Analytics Chat, the Data Model IDE, administration, sign-in, and transactional emails. Dates, numbers, and day names follow the same locale, so a report reads the way its audience expects.
Each person picks their own language from the Language picker in Preferences, and it follows them everywhere, including the emails Cube sends them. For embedded analytics, set an account-wide default under Embed → Settings, override it per embed with ?locale=, or switch it at runtime from the host page.
Fixed the AI Summary widget failing to load or regenerate on embedded Dashboards.

Everything you can do in Cube is now one command away. The Cube CLI is a single native binary that puts Cube's full public API in your terminal: creating and monitoring deployments, editing data model files, working with branches and commits, linking GitHub repositories and triggering builds, managing workbooks, explorations, and notifications, and administering users, groups, policies, SSO, and agents.
Install it in a single line on macOS, Linux, or Windows and sign in with the OAuth device flow — no passwords typed into a prompt. Every command supports JSON output, deterministic exit codes, and thorough --help, and deploys hash your files so only what changed is uploaded. That also makes the CLI the most reliable way to hand Cube to an AI agent: it's all text in, text out, so an agent can chain a whole workflow without a human in the loop. The CLI is in preview — read the full story in Power of CLI.
You can now compare any measure against a prior period directly in Workbooks and Dashboards. Pick a comparison window and charts, totals, and the results grid all render the current and prior values side by side, with the companion SQL available whenever you want to check the math.
Azure is now available on self-serve plans, alongside AWS and GCP.
Added public REST endpoints for the GitHub integration, so you can link a repository and create a deployment from it end to end without opening the UI.

Connect your dbt project once and every model becomes a governed cube. The dbt integration converts dbt models into cubes — dimensions, measures, column descriptions, primary keys, and joins inferred from dbt relationship tests and foreign-key constraints — so you never redefine your transformations by hand, and dbt stays the single source of truth.
Sync however fits your workflow: pull manually from the IDE, call the REST endpoint from CI right after dbt run, or trigger on every push with a webhook. Every automated sync lands on a review branch that a human approves before it reaches production. Cube connects to your repository over HTTPS or SSH deploy keys and only ever parses your project — it never touches your data warehouse. Read more in the announcement.
The Cube MCP server can now edit the semantic data model, not just read it — external agents can open a dev branch, write model files, and commit changes for review.
Creator Mode embeds can now hide the Semantic SQL and Generated SQL views, so embedded end users can't see your data source's table and schema names.
Answering the same question every Monday morning shouldn't depend on someone remembering to ask it. With Scheduled Tasks, you save an agent prompt once, pick a cadence — hourly, daily, weekly, monthly, or a custom cron expression — and Cube runs it on schedule: querying your semantic layer, creating and updating reports and dashboards, and emailing results to workspace members.
Every run produces its own thread in Analytics Chat, so you can open any past run and ask follow-up questions in context, and each task's detail page shows its run history at a glance. Tasks run under the security context of the person who created them, so results respect row-level security and access policies. Create one from the new Scheduled section in the sidebar — or simply ask the agent in Analytics Chat to set it up for you.
Creators can now edit a published dashboard directly from its chat — ask the agent to add a chart, change filters, or adjust the layout, and it updates the draft; an Open in builder button then carries the conversation into the builder so you can review and publish.
Chat History in the admin console now labels where each conversation originated: Web, Slack, MCP, API, or a scheduled task.
searchDataModel and runQuery tools, so external agents can explore the data model and run governed queries directlyDeployments can now authenticate Chat and embedding API requests with their own Cube authentication (JWT/JWKS) instead of a separate embed token, via a new deployment setting.
default_ui_filters now accept relative date ranges (like "5 weeks ago" to "today") and use the same operator vocabulary as the workbook filter barYou can now rename a workbook from its context menu in the Workspace.
Renaming or deleting a field in the data model can silently break saved reports and dashboards that reference it — and you usually find out from the people whose charts stopped working. Content Validator, a new tab in Data Model IDE, scans your saved content and flags every report that references a measure, dimension, or view that no longer exists.
Validation is branch-aware: run it in dev mode or on a shared branch to see exactly which reports a model change would break before it merges. For each broken draft report you can jump straight to it, replace the missing member with a valid one, or delete the report; published dashboards link back to their drafts so you can fix and republish.
Added a fill in missing rows option so time-series charts render every date in a range, even ones with no data.
Fixed unresolved validation errors when pushing a data model to Snowflake semantic views.

Cube Evals brings deterministic accuracy checks to your Cube agent. Pair natural-language questions with known-correct answers, run the agent against that suite, and get an objective accuracy score plus a per-case breakdown of what failed. Grading compares result sets directly — it tolerates numeric variance and ignores column aliases — so there's no LLM judge, and every failure shows the agent's SQL next to the ground-truth SQL.
Eval cases live as YAML in your data model repo, so you can run them on a branch in front of a code review, inside AI Studio. Read the full story in Introducing Cube Evals.
A number on its own is a dead end. Add links to a dimension in your data model and they appear as actions in the menu that opens when you left-click any results-table cell — so a reader can jump straight from that number to the context behind it. There are two kinds:

A number tells you something moved; the reason usually lives in a ticket, a doc, or an error tracker. With MCP Connectors, the Cube agent can now reach the external tools your team already uses — and pull that surrounding context into the same conversation, while every answer stays grounded in your semantic layer. We're launching with Notion, Linear, Sentry, and Attio, with many more connectors on the way — and you can already point to any remote MCP endpoint with a custom connector today.
Ask "activation dropped last week — what changed?" and the agent can combine the metric with the features that shipped and the errors that spiked. It works both directions: results can be written back as a Notion page, filed as a Linear ticket, or posted wherever your team works. Set up connectors from the connector directory; administrators choose exactly which tools the agent can use — nothing is enabled by default — and every connection respects your existing access controls.
Each user connects their own accounts: connectors that need a personal login are authorized per user and can be reviewed or revoked from Connected accounts in your preferences. Read the full story in Introducing MCP Connectors.
The new Usage Analytics page gives account administrators a set of pre-built dashboards that show how your deployments are used: query volume and latency, cache efficiency, user adoption, and AI activity with token consumption. Each dashboard answers a different question about your account — Overview, Query Activity & Performance, Users & Adoption, and AI & Token Tracking.
Usage Analytics is itself a Cube application: your account's usage telemetry is modeled as a curated set of views — API Requests, AI Usage, and Users & Adoption — and the pre-built dashboards are regular Cube dashboards on top of them. It runs the full Cube experience in Creator Mode, so you can explore the usage data, create workbooks, and assemble your own dashboards — anything you build stays private unless you share it. Available on Premium and above plans.
meta.default_ui_filters — selecting the view in a new workbook pre-populates them as editable, removable filters
Agent Skills let teams capture repetitive analytical workflows as reusable, named procedures that the agent runs consistently every time — across Analytics Chat, Workbooks, and Dashboards. Instead of re-explaining the same multi-step analysis each time, you define it once and the whole team runs it the same way.
There are three ways to invoke a skill: click its button in chat, type / for a slash menu filtered by title and description, or just ask in plain language and the agent matches your request to the right skill and runs it. Once you've picked one, you can refine it inline — "for EMEA, last 6 weeks" — and the agent adjusts the run accordingly.
Skills are authored as plain Markdown files in your project's agents/skills/ directory: frontmatter plus plain-language steps, no special syntax. Running a skill is available to Explorer and Viewer roles; authoring one requires data-model edit permissions. Read the full story in Introducing Cube Agent Skills.
Published Dashboards can now include a built-in Dashboard Agent so viewers can ask questions about what they're looking at. A chat bubble appears in the bottom-right corner — click it to open a side panel and the dashboard reflows alongside it.
The agent is read-only and grounded in what the viewer sees: it knows the dashboard's widgets, the charts they render, and the viewer's active filters and time grains, so answers always reflect the current view. Ask it to summarize the dashboard, explain a trend, or compare widgets — much like Analytics Chat, but for consumers rather than editors. Conversations persist in the URL, so a refresh restores the thread right where it left off.
The Map chart now supports a Region mode that plots query results as a choropleth over polygon GeoJSON. Choose a built-in source — World Countries or US States — or point to any custom HTTPS GeoJSON URL, then join it to your data on a region property. Cube auto-detects the join fields and color-scales regions by your measure, with optional shading for unmatched regions.
loadQueryResults MCP tool to paginate through large query resultsllm: auto to automatically select a modelpublic: false