compose: clarify cloud provider portability section (#24423)

## Summary

- Rewrite the "Cloud providers" section to explain that the Compose
models specification enables portability for platforms that implement
it, rather than implying specific named cloud providers currently
support it
- Remove vague "compatible cloud providers" reference from Prerequisites
- Change "Cloud providers might" to "A platform might" to reflect this
is a specification-level feature

Closes #24257

🤖 Generated with [Claude Code](https://claude.com/claude-code)

---------

Co-authored-by: Claude Sonnet 4.6 <noreply@anthropic.com>
Co-authored-by: Craig Osterhout <craig.osterhout@docker.com>

Author: 3 people

content/manuals/ai/compose/models-and-compose.md

@@ -16,8 +16,7 @@ Compose lets you define AI models as core components of your application, so you
 ## Prerequisites
 
 - Docker Compose v2.38 or later
-- A platform that supports Compose models such as Docker Model Runner (DMR) or compatible cloud providers.
-  If you are using DMR, see the [requirements](/manuals/ai/model-runner/_index.md#requirements).
+- A platform that supports Compose models such as [Docker Model Runner (DMR)](/manuals/ai/model-runner/_index.md#requirements).
 
 ## What are Compose models?
 
@@ -166,7 +165,7 @@ Docker Model Runner will:
 
 ### Cloud providers
 
-The same Compose file can run on cloud providers that support Compose models:
+The Compose models specification is portable. Platforms that implement the Compose specification can support the `models` top-level element, allowing the same Compose file to run on different infrastructure. Cloud-specific behavior can be configured using extension attributes (`x-*`):
 
 ```yaml
 services:
@@ -184,9 +183,10 @@ models:
       - "cloud.region=us-west-2"
 ```
 
-Cloud providers might:
+How a platform handles model definitions depends on its implementation. A platform might:
+
 - Use managed AI services instead of running models locally
-- Apply cloud-specific optimizations and scaling
+- Apply platform-specific optimizations and scaling
 - Provide additional monitoring and logging capabilities
 - Handle model versioning and updates automatically