"Versionless" -> "Release Tracks"

website/blog/2021-11-23-how-to-upgrade-dbt-versions.md

@@ -18,7 +18,7 @@ It's been a few years since dbt-core turned 1.0! Since then, we've committed to
 
 In 2024, we're taking this promise further by:
 - Stabilizing interfaces for everyone — adapter maintainers, metadata consumers, and (of course) people writing dbt code everywhere — as discussed in [our November 2023 roadmap update](https://github.com/dbt-labs/dbt-core/blob/main/docs/roadmap/2023-11-dbt-tng.md).
-- Introducing **Versionless** in dbt Cloud. No more manual upgrades and no more need for _a second sandbox project_ just to try out new features in development. For more details, refer to [Upgrade Core version in Cloud](/docs/dbt-versions/upgrade-dbt-version-in-cloud).
+- Introducing **Latest** release track in dbt Cloud. No more manual upgrades and no need for _a second sandbox project_ just to try out new features in development. For more details, refer to [Upgrade Core version in Cloud](/docs/dbt-versions/upgrade-dbt-version-in-cloud).
 
 We're leaving the rest of this post as is, so we can all remember how it used to be. Enjoy a stroll down memory lane.
 

website/blog/2024-04-22-extended-attributes.md

@@ -80,7 +80,7 @@ All you need to do is configure an environment as staging and enable the **Defer
 
 ## Upgrading on a curve
 
-Lastly, let’s consider a more specialized use case. Imagine we have a "tiger team" (consisting of a lone analytics engineer named Dave) tasked with upgrading from dbt version 1.6 to the new **Versionless** setting, to take advantage of added stability and feature access. We want to keep the rest of the data team being productive in dbt 1.6 for the time being, while enabling Dave to upgrade and do his work in the new versionless mode.
+Lastly, let’s consider a more specialized use case. Imagine we have a "tiger team" (consisting of a lone analytics engineer named Dave) tasked with upgrading from dbt version 1.6 to the new **Latest release track**, to take advantage of new features and performance improvements. We want to keep the rest of the data team being productive in dbt 1.6 for the time being, while enabling Dave to upgrade and do his work with Latest (and greatest) dbt.
 
 ### Development environment
 

website/blog/2024-05-22-latest-dbt-stability-improvement-innovation.md

@@ -1,5 +1,5 @@
 ---
-title: "How we're making sure you can confidently go \"Versionless\" in dbt Cloud"
+title: "How we're making sure you can confidently switch to the \"Latest\" release track in dbt Cloud"
 description: "Over the past 6 months, we've laid a stable foundation for continuously improving dbt."
 slug: latest-dbt-stability
 
@@ -16,19 +16,19 @@ As long as dbt Cloud has existed, it has required users to select a version of d
 
 However, this came at a cost. While bumping a project's dbt version *appeared* as simple as selecting from a dropdown, there was real effort required to test the compatibility of the new version against existing projects, package dependencies, and adapters. On the other hand, putting this off meant foregoing access to new features and bug fixes in dbt.
 
-But no more. Today, we're ready to announce the general availability of a new option in dbt Cloud: [**"Versionless."**](https://docs.getdbt.com/docs/dbt-versions/upgrade-dbt-version-in-cloud#versionless)
+But no more. Today, we're ready to announce the general availability of a new option in dbt Cloud: [**the "Latest" release track.**](/docs/dbt-versions/cloud-release-tracks)
 
 <!--truncate-->
 
 For customers, this means less maintenance overhead, faster access to bug fixes and features, and more time to focus on what matters most: building trusted data products. This will be our stable foundation for improvement and innovation in dbt Cloud. 
 
-But we wanted to go a step beyond just making this option available to you. In this blog post, we aim to shed a little light on the extensive work we've done to ensure that using "Versionless" is a stable, reliable experience for the thousands of customers who rely daily on dbt Cloud.
+But we wanted to go a step beyond just making this option available to you. In this blog post, we aim to shed a little light on the extensive work we've done to ensure that using the "Latest" release track is a stable and reliable experience for the thousands of customers who rely daily on dbt Cloud.
 
 ## How we safely deploy dbt upgrades to Cloud
 
 We've put in place a rigorous, best-in-class suite of tests and control mechanisms to ensure that all changes to dbt under the hood are fully vetted before they're deployed to customers of dbt Cloud.
 
-This pipeline has in fact been in place since January! It's how we've already been shipping continuous changes to the hundreds of customers who've selected "Versionless" while it's been in Beta and Preview. In that time, this process has enabled us to prevent multiple regressions before they were rolled out to any customers.
+This pipeline has in fact been in place since January! It's how we've already been shipping continuous changes to the hundreds of customers who've selected the "Latest" release track while it's been in Beta and Preview. In that time, this process has enabled us to prevent multiple regressions before they were rolled out to any customers.
 
 We're very confident in the robustness of this process**. We also know that we'll need to continue building trust with time.** We're sharing details about this work in the spirit of transparency and to build that trust.
 
@@ -82,9 +82,9 @@ All incidents are retrospected to make sure we not only identify and fix the roo
 
 :::
 
-The outcome of this process is that, when you select "Versionless" in dbt Cloud, the time between an improvement being made to dbt Core and you *safely* getting access to it in your projects is a matter of days — rather than months of waiting for the next dbt Core release, on top of any additional time it may have taken to actually carry out the upgrade.
+The outcome of this process is that, when you select the "Latest" release track in dbt Cloud, the time between an improvement being made to dbt Core and you *safely* getting access to it in your projects is a matter of days — rather than months of waiting for the next dbt Core release, on top of any additional time it may have taken to actually carry out the upgrade.
 
-We’re pleased to say that since the beta launch of “Versionless” in dbt Cloud in March, **we have not had any functional regressions reach customers**, while we’ve also been shipping multiple improvements to dbt functionality every day. This is a foundation that we aim to build on for the foreseeable future.
+We’re pleased to say that, at the time of writing (May 2, 2025), since the beta launch of the "Latest" release track in dbt Cloud in March, **we have not had any functional regressions reach customers**, while we’ve also been shipping multiple improvements to dbt functionality every day. This is a foundation that we aim to build on for the foreseeable future.
 
 ## Stability as a feature
 
@@ -98,7 +98,7 @@ The adapter interface — i.e. how dbt Core actually connects to a third-party d
 
 To solve that, we've released a new set of interfaces that are entirely independent of the `dbt-core` library: [`dbt-adapters==1.0.0`](https://github.com/dbt-labs/dbt-adapters). From now on, any changes to `dbt-adapters` will be backward and forward-compatible. This also decouples adapter maintenance from the regular release cadence of dbt Core — meaning maintainers get full control over when they ship implementations of new adapter-powered features.
 
-Note that adapters running in dbt Cloud **must** be [migrated to the new decoupled architecture](https://github.com/dbt-labs/dbt-adapters/discussions/87) as a baseline in order to support the new "Versionless" option.
+Note that adapters running in dbt Cloud **must** be [migrated to the new decoupled architecture](https://github.com/dbt-labs/dbt-adapters/discussions/87) as a baseline in order to support the new "Latest" release track.
 
 ### Managing behavior changes: stability as a feature
 
@@ -118,7 +118,7 @@ We’ve now [formalized our development best practices](https://github.com/dbt-l
 
 In conclusion, we’re putting a lot of new muscle behind our commitments to dbt Cloud customers, the dbt Community, and the broader ecosystem:
 
-- **Continuous updates**: "Versionless" dbt Cloud simplifies the update process, ensuring you always have the latest features and bug fixes without the maintenance overhead.
+- **Continuous updates**: The "Latest" release track in dbt Cloud simplifies the update process, ensuring you always have the latest features and bug fixes without the maintenance overhead.
 - **A rigorous new testing and deployment process**: Our new testing pipeline ensures that every update is carefully vetted against documented interfaces, Cloud-supported adapters, and popular packages before it reaches you. This process minimizes the risk of regressions — and has now been successful at entirely preventing them for hundreds of customers over multiple months.
 - **A commitment to stability**: We’ve reworked our approaches to adapter interfaces, behaviour change management, and metadata artifacts to give you more stability and control.
 

website/blog/2024-06-12-putting-your-dag-on-the-internet.md

@@ -114,6 +114,6 @@ Traditionally dbt is the T in ELT (dbt overview [here](https://docs.getdbt.com/t
 
 In order to get this functionality shipped quickly, EQT opened a pull request, Snowflake helped with some problems we had with CI and a member of dbt Labs helped write the tests and merge the code in!  
 
-dbt now features this functionality in dbt 1.8+ or the “Versionless” option of dbt Cloud (dbt overview [here](/docs/dbt-versions/upgrade-dbt-version-in-cloud#versionless)). 
+dbt now features this functionality in dbt 1.8+ and the "Latest" release track in dbt Cloud (dbt overview [here](/docs/dbt-versions/cloud-release-tracks)). 
 
 dbt Labs staff and community members would love to chat more about it in the [#db-snowflake](https://getdbt.slack.com/archives/CJN7XRF1B) slack channel.

website/dbt-versions.js

@@ -16,7 +16,7 @@
 exports.versions = [
   {
     version: "1.10",
-    customDisplay: "Cloud (Versionless)",
+    customDisplay: "Cloud (Latest)",
   },
   {
     version: "1.9",

website/docs/docs/build/incremental-microbatch.md

@@ -8,7 +8,7 @@
 
 :::info Microbatch
 
-The new `microbatch` strategy is available in beta for [dbt Cloud Versionless](/docs/dbt-versions/upgrade-dbt-version-in-cloud#versionless) and dbt Core v1.9. 
+The new `microbatch` strategy is available in beta for [dbt Cloud "Latest"](/docs/dbt-versions/cloud-release-tracks) and dbt Core v1.9. 
 
 If you use a custom microbatch macro, set a [distinct behavior flag](/reference/global-configs/behavior-changes#custom-microbatch-strategy) in your `dbt_project.yml` to enable batched execution. If you don't have a custom microbatch macro, you don't need to set this flag as dbt will handle microbatching automatically for any model using the [microbatch strategy](#how-microbatch-compares-to-other-incremental-strategies).
 

website/docs/docs/build/metricflow-time-spine.md

@@ -7,7 +7,7 @@
 ---
 <VersionBlock firstVersion="1.9">
 
-<!-- this whole section is for 1.9 and higher + Versionless -->
+<!-- this whole section is for 1.9 and higher + Release Tracks -->
 
 It's common in analytics engineering to have a date dimension or "time spine" table as a base table for different types of time-based joins and aggregations. The structure of this table is typically a base column of daily or hourly dates, with additional columns for other time grains, like fiscal quarters, defined based on the base column. You can join other tables to the time spine on the base column to calculate metrics like revenue at a point in time, or to aggregate to a specific time grain.
 
@@ -108,7 +108,7 @@
   - It needs to reference a column defined under the `columns` key, in this case, `date_hour` and `date_day`, respectively.
   - It sets the granularity at the column-level using the `granularity` key, in this case, `hour` and `day`, respectively. 
 - MetricFlow will use the `standard_granularity_column` as the join key when joining the time spine table to another source table.
-- [The `custom_granularities` field](#custom-calendar), (available in Versionless and dbt v1.9 and higher) lets you specify non-standard time periods like `fiscal_year` or `retail_month` that your organization may use.
+- [The `custom_granularities` field](#custom-calendar), (available in dbt Cloud Latest and dbt Core v1.9 and higher) lets you specify non-standard time periods like `fiscal_year` or `retail_month` that your organization may use.
 
 For an example project, refer to our [Jaffle shop](https://github.com/dbt-labs/jaffle-sl-template/blob/main/models/marts/_models.yml) example.
 
@@ -310,9 +310,7 @@
 
 <VersionBlock lastVersion="1.8">
 
-The ability to configure custom calendars, such as a fiscal calendar, is available in [dbt Cloud Versionless](/docs/dbt-versions/versionless-cloud) or dbt Core [v1.9 and higher](/docs/dbt-versions/core). 
-
-To access this feature, [upgrade to Versionless](/docs/dbt-versions/upgrade-dbt-version-in-cloud#versionless) or your dbt Core version to v1.9 or higher.
+The ability to configure custom calendars, such as a fiscal calendar, is available now in [the "Latest" release track in dbt Cloud](/docs/dbt-versions/cloud-release-tracks), and it will be available in [dbt Core v1.9+](/docs/dbt-versions/core-upgrade/upgrading-to-v1.9). 
 
 </VersionBlock>
 

website/docs/docs/build/metrics-overview.md

@@ -95,7 +95,8 @@
 <VersionBlock lastVersion="1.8">
 Default time granularity for metrics is useful if your time dimension has a very fine grain, like second or hour, but you typically query metrics rolled up at a coarser grain. 
 
-To set the default time granularity for metrics, you need to be on dbt Cloud Versionless or dbt v1.9 and higher. 
+Default time granularity for metrics is available now in [the "Latest" release track in dbt Cloud](/docs/dbt-versions/cloud-release-tracks), and it will be available in [dbt Core v1.9+](/docs/dbt-versions/core-upgrade/upgrading-to-v1.9). 
+
 
 </VersionBlock>
 

website/docs/docs/build/snapshots.md

@@ -39,7 +39,7 @@
 <VersionBlock lastVersion="1.8" >
 
 - To configure snapshots in versions 1.8 and earlier, refer to [Configure snapshots in versions 1.8 and earlier](#configure-snapshots-in-versions-18-and-earlier). These versions use an older syntax where snapshots are defined within a snapshot block in a `.sql` file, typically located in your `snapshots` directory. 
-- Note that defining multiple resources in a single file can significantly slow down parsing and compilation. For faster and more efficient management, consider the updated snapshot YAML syntax, [available in Versionless](/docs/dbt-versions/versionless-cloud) or [dbt Core v1.9 and later](/docs/dbt-versions/core).
+- Note that defining multiple resources in a single file can significantly slow down parsing and compilation. For faster and more efficient management, consider the updated snapshot YAML syntax, [available now in the "Latest" release track in dbt Cloud](/docs/dbt-versions/cloud-release-tracks) and landing soon in [dbt Core v1.9](/docs/dbt-versions/core).
 
 
 </VersionBlock>
@@ -172,7 +172,7 @@
 
 <Expandable alt_header="Use dbt_valid_to_current for easier date range queries">
 
-By default, `dbt_valid_to` is `NULL` for current records. However, if you set the [`dbt_valid_to_current` configuration](/reference/resource-configs/dbt_valid_to_current) (available in Versionless and 1.9 and higher), `dbt_valid_to` will be set to your specified value (such as `9999-12-31`) for current records.
+By default, `dbt_valid_to` is `NULL` for current records. However, if you set the [`dbt_valid_to_current` configuration](/reference/resource-configs/dbt_valid_to_current) (available in dbt Core v1.9+), `dbt_valid_to` will be set to your specified value (such as `9999-12-31`) for current records.
 
 This allows for straightforward date range filtering.
 
@@ -210,10 +210,10 @@
 ### How snapshots work
 
 When you run the [`dbt snapshot` command](/reference/commands/snapshot):
-* **On the first run:** dbt will create the initial snapshot table — this will be the result set of your `select` statement, with additional columns including `dbt_valid_from` and `dbt_valid_to`. All records will have a `dbt_valid_to = null` or the value specified in [`dbt_valid_to_current`](/reference/resource-configs/dbt_valid_to_current) (available in Versionless and 1.9 and higher) if configured.
+* **On the first run:** dbt will create the initial snapshot table — this will be the result set of your `select` statement, with additional columns including `dbt_valid_from` and `dbt_valid_to`. All records will have a `dbt_valid_to = null` or the value specified in [`dbt_valid_to_current`](/reference/resource-configs/dbt_valid_to_current) (available in dbt Core 1.9+) if configured.
 * **On subsequent runs:** dbt will check which records have changed or if any new records have been created:
   - The `dbt_valid_to` column will be updated for any existing records that have changed.
-  - The updated record and any new records will be inserted into the snapshot table. These records will now have `dbt_valid_to = null` or the value configured in `dbt_valid_to_current` (available in Versionless and 1.9 and higher).
+  - The updated record and any new records will be inserted into the snapshot table. These records will now have `dbt_valid_to = null` or the value configured in `dbt_valid_to_current` (available in dbt Core v1.9+).
 
 #### Note 
 - These column names can be customized to your team or organizational conventions using the [snapshot_meta_column_names](#snapshot-meta-fields) config.
@@ -402,9 +402,7 @@
 
 Snapshot <Term id="table">tables</Term> will be created as a clone of your source dataset, plus some additional meta-fields*.
 
-Starting in 1.9 or with [dbt Cloud Versionless](/docs/dbt-versions/upgrade-dbt-version-in-cloud#versionless):
-- These column names can be customized to your team or organizational conventions using the [`snapshot_meta_column_names`](/reference/resource-configs/snapshot_meta_column_names) config. 
-- Use the [`dbt_valid_to_current` config](/reference/resource-configs/dbt_valid_to_current) to set a custom indicator for the value of `dbt_valid_to` in current snapshot records (like a future date such as `9999-12-31`). By default, this value is `NULL`. When set, dbt will use this specified value instead of `NULL` for `dbt_valid_to` for current records in the snapshot table.
+In dbt Core v1.9+ (or available sooner in [the "Latest" release track in dbt Cloud](/docs/dbt-versions/cloud-release-tracks)), these column names can be customized to your team or organizational conventions via the [`snapshot_meta_column_names`](/reference/resource-configs/snapshot_meta_column_names) config.
 
 | Field          | Meaning | Usage |
 | -------------- | ------- | ----- |
@@ -495,7 +493,7 @@
 
 - In dbt versions 1.8 and earlier, snapshots are `select` statements, defined within a snapshot block in a `.sql` file (typically in your `snapshots` directory). You'll also need to configure your snapshot to tell dbt how to detect record changes. 
 - The earlier dbt versions use an older syntax that allows for defining multiple resources in a single file. This syntax can significantly slow down parsing and compilation.
-- For faster and more efficient management, consider[ upgrading to Versionless](/docs/dbt-versions/versionless-cloud) or the [latest version of dbt Core](/docs/dbt-versions/core), which introduces an updated snapshot configuration syntax that optimizes performance.
+- For faster and more efficient management, consider [choosing the "Latest" release track in dbt Cloud](/docs/dbt-versions/cloud-release-tracks) for early access to this functionality, which will be landing in dbt Core v1.9.
 
 The following example shows how to configure a snapshot: