redpanda-data · micheleRP · May 11, 2026 · May 11, 2026 · May 11, 2026
@@ -1,4 +1,4 @@
-* xref:index.adoc[Overview]
+* xref:ROOT:index.adoc[Overview]
 * xref:get-started:index.adoc[Get started]
 ** xref:get-started:byoc-prereqs.adoc[Prerequisites]
 ** xref:get-started:byoc-quickstart.adoc[Quickstart]

@@ -91,7 +91,7 @@ AI Hub mode automatically provisions 6 backend pools to handle different request
 * Authentication: x-api-key header
 * Transform: OpenAI → Anthropic Messages API
 * Timeout: Standard (60 seconds)
-* Models: All `anthropic/*` models via OpenAI-compatible endpoint
+* Models: All `anthropic/*` models through OpenAI-compatible endpoint
 --
 
 . *Anthropic with Transform (Streaming)*: Converts OpenAI format to Anthropic's native format for streaming requests
@@ -180,7 +180,7 @@ While routing rules are immutable, you can customize routing behavior through us
 
 include::ROOT:partial$ai-hub-preference-toggles.adoc[]
 
-=== Set preferences via Console
+=== Set preferences through Console
 
 . Navigate to your AI Hub gateway.
 . Click *Settings* → *Preferences*.
@@ -196,7 +196,7 @@ include::ROOT:partial$ai-hub-preference-toggles.adoc[]
 
 Changes take effect immediately for new requests.
 
-=== Set preferences via API
+=== Set preferences through API
 
 [,bash]
 ----

@@ -140,7 +140,7 @@ Store these files securely. You'll reference them when configuring Custom mode r
 Define your post-ejection configuration:
 
 . *Routing rules*: Write CEL expressions that replicate AI Hub behavior, then add your custom rules
-. *Backend pools*: Identify modifications needed (timeouts, custom providers, etc.)
+. *Backend pools*: Identify modifications needed (timeouts, custom providers, and so on)
 . *Testing strategy*: Plan how you'll validate that existing functionality still works
 . *Rollout approach*: Decide whether to eject immediately or test in staging first
 
@@ -185,13 +185,13 @@ Provide builders with clear expectations:
 
 The ejection process is irreversible. Follow these steps carefully.
 
-=== Step 1: Initiate ejection
+=== Initiate ejection
 
 . Navigate to your gateway in the console.
 . Click *Settings*.
 . Click *Eject to Custom Mode* button.
 
-=== Step 2: Confirm understanding
+=== Confirm understanding
 
 The console presents warnings about ejection:
 
@@ -204,14 +204,14 @@ The console presents warnings about ejection:
 
 Check all boxes to proceed.
 
-=== Step 3: Execute ejection
+=== Execute ejection
 
 . Enter the gateway name to confirm: `[Your Gateway Name]`
 . Click *Eject to Custom Mode*.
 
 Ejection typically completes in seconds. The gateway remains available during the transition.
 
-You can also eject via API:
+You can also eject through API:
 
 [,bash]
 ----
@@ -230,7 +230,7 @@ Expected response:
 }
 ----
 
-=== Step 4: Verify ejection
+=== Verify ejection
 
 After ejection completes:
 

@@ -44,7 +44,7 @@ When you create a gateway, you choose between two modes that differ in configura
 
 |*Routing preferences*
 |6 configurable toggles
-|N/A (full control via rules)
+|N/A (full control through rules)
 
 |*Modify backends*
 |Cannot modify/delete
@@ -67,7 +67,7 @@ AI Hub mode provides instant, pre-configured access to OpenAI, Anthropic, and Go
 
 AI Hub mode eliminates complex LLM gateway configuration by providing pre-built routing rules and backend pools. Platform admins add provider credentials (OpenAI, Anthropic, Google Gemini) once, and all teams immediately benefit from intelligent routing across both providers.
 
-Teams adopting LLMs typically face significant friction: configuring backends and routing rules takes hours, different providers have incompatible APIs, and developers must learn each provider's quirks. AI Hub mode solves this by providing instant access—IT adds API keys once, all teams benefit immediately.
+Teams adopting LLMs typically face significant friction: configuring backends and routing rules takes hours, different providers have incompatible APIs, and developers must learn each provider's quirks. AI Hub mode solves this by providing instant access: IT adds API keys once, all teams benefit immediately.
 
 === Pre-configured components
 
@@ -86,7 +86,7 @@ When you create an AI Hub gateway, you automatically get:
 
 * Model prefix routing: `openai/*`, `anthropic/*`
 * Model name pattern routing: `gpt-*`, `claude-*`, `o1-*`
-* Special routing: embeddings, images, audio → OpenAI only
+* Special routing: Embeddings, images, audio → OpenAI only
 * Native SDK detection: `/v1/messages` → Anthropic passthrough
 * Streaming detection → Extended timeout backends
 

@@ -258,8 +258,8 @@ model = "openai/gpt-5.2"
 model = "anthropic/claude-sonnet-4.5"
 
 # ⚠️ Works but relies on pattern matching
-model = "gpt-5.2"  # Routes to OpenAI via pattern matching
-model = "claude-sonnet-4.5"  # Routes to Anthropic via pattern matching
+model = "gpt-5.2"  # Routes to OpenAI through pattern matching
+model = "claude-sonnet-4.5"  # Routes to Anthropic through pattern matching
 ----
 
 Explicit provider prefixes ensure deterministic routing and make your code more maintainable.

@@ -30,8 +30,8 @@ Claude Code connects to AI Gateway through two primary endpoints:
 
 The gateway handles:
 
-. Authentication via bearer tokens in the `Authorization` header
-. Gateway selection via the endpoint URL
+. Authentication through bearer tokens in the `Authorization` header
+. Gateway selection through the endpoint URL
 . Model routing using the `vendor/model_id` format
 . MCP server aggregation for multi-tool workflows
 . Request logging and cost tracking per gateway
@@ -389,7 +389,7 @@ Track Claude Code activity through gateway observability features.
 |===
 
 
-=== Query logs via API
+=== Query logs through API
 
 Programmatically access logs for integration with monitoring systems:
 

@@ -42,7 +42,7 @@ Cline connects to AI Gateway through two primary endpoints:
 
 The gateway handles:
 
-. Authentication via bearer tokens in the `Authorization` header
+. Authentication through bearer tokens in the `Authorization` header
 . Model routing using the `vendor/model_id` format
 . MCP server aggregation for multi-tool workflows
 . Request logging and cost tracking per gateway
@@ -326,9 +326,9 @@ Provide these instructions to users configuring Cline in VS Code.
 
 Users configure Cline's API provider and credentials through the Cline extension interface.
 
-IMPORTANT: API provider configuration (API keys, base URLs, custom headers) is managed via Cline's extension global state, not VS Code `settings.json`. These settings are stored in the extension's internal state and must be configured through the Cline UI.
+IMPORTANT: API provider configuration (API keys, base URLs, custom headers) is managed through Cline's extension global state, not VS Code `settings.json`. These settings are stored in the extension's internal state and must be configured through the Cline UI.
 
-==== Configure via Cline UI
+==== Configure through Cline UI
 
 . Open the Cline extension panel in VS Code
 . Click the settings icon or gear menu
@@ -352,13 +352,13 @@ Configure Cline to connect to the aggregated MCP endpoint through the Cline UI o
 . Search for "Cline > Mcp: Mode"
 . Enable the MCP mode toggle
 
-==== Configure MCP server via Cline UI
+==== Configure MCP server through Cline UI
 
 . Open the Cline extension panel in VS Code
 . Navigate to MCP server settings
 . Add the Redpanda AI Gateway MCP server with the connection details
 
-==== Configure via cline_mcp_settings.json
+==== Configure through cline_mcp_settings.json
 
 Alternatively, edit `cline_mcp_settings.json` (located in the Cline extension storage directory):
 
@@ -438,7 +438,7 @@ Cline autonomous operations may generate request sequences. Look for patterns to
 |===
 
 
-=== Query logs via API
+=== Query logs through API
 
 Programmatically access logs for integration with monitoring systems:
 

@@ -29,8 +29,8 @@ Key characteristics:
 
 * Uses native provider formats (Anthropic format for Anthropic, OpenAI format for OpenAI)
 * Supports multiple LLM providers simultaneously with per-provider configuration
-* Custom API endpoints via `apiBase` configuration
-* Custom headers via `requestOptions.headers`
+* Custom API endpoints through `apiBase` configuration
+* Custom headers through `requestOptions.headers`
 * Built-in MCP support for tool discovery and execution
 * Autocomplete, chat, and inline edit modes
 
@@ -44,7 +44,7 @@ Continue.dev connects to AI Gateway differently than unified-format clients:
 
 The gateway handles:
 
-. Authentication via bearer tokens in the `Authorization` header
+. Authentication through bearer tokens in the `Authorization` header
 . Provider-specific request formats without transformation
 . Model routing using provider-native model identifiers
 . MCP server aggregation for multi-tool workflows
@@ -580,7 +580,7 @@ Continue.dev generates different request patterns:
 |===
 
 
-=== Query logs via API
+=== Query logs through API
 
 Programmatically access logs for integration with monitoring systems:
 

@@ -32,7 +32,7 @@ Key characteristics:
 * Limited support for custom headers (makes multi-tenant deployments challenging)
 * Supports MCP protocol with a 40-tool limit
 * Built-in code completion and chat modes
-* Configuration via settings file (`~/.cursor/config.json`)
+* Configuration through settings file (`~/.cursor/config.json`)
 
 == Architecture overview
 
@@ -43,10 +43,10 @@ Cursor IDE connects to AI Gateway through standardized endpoints:
 
 The gateway handles:
 
-. Authentication via bearer tokens in the `Authorization` header
-. Gateway selection via the endpoint URL
+. Authentication through bearer tokens in the `Authorization` header
+. Gateway selection through the endpoint URL
 . Model routing using vendor prefixes (for example, `anthropic/claude-sonnet-4.5`)
-. Format transforms from OpenAI format to provider-native formats (for Anthropic, Google, etc.)
+. Format transforms from OpenAI format to provider-native formats (for Anthropic, Google, and so on)
 . MCP server aggregation for multi-tool workflows
 . Request logging and cost tracking per gateway
 
@@ -627,7 +627,7 @@ Cursor generates different request patterns:
 |Metric |Purpose
 
 |Request volume by provider
-|Identify which providers are most used via model prefix routing
+|Identify which providers are most used through model prefix routing
 
 |Token usage by model
 |Track consumption patterns (completion vs chat)
@@ -646,7 +646,7 @@ Cursor generates different request patterns:
 |===
 
 
-=== Query logs via API
+=== Query logs through API
 
 Programmatically access logs for integration with monitoring systems:
 

@@ -32,7 +32,7 @@ Key characteristics:
 * Limited support for custom headers (similar to Cursor IDE)
 * Supports BYOK for Business/Enterprise subscriptions
 * Built-in code completion, chat, and inline editing modes
-* Configuration via IDE settings or organization policies
+* Configuration through IDE settings or organization policies
 * High request volume from code completion features
 
 == Architecture overview
@@ -44,8 +44,8 @@ GitHub Copilot connects to AI Gateway through standardized endpoints:
 
 The gateway handles:
 
-. Authentication via bearer tokens in the `Authorization` header
-. Gateway selection via URL path routing or query parameters
+. Authentication through bearer tokens in the `Authorization` header
+. Gateway selection through URL path routing or query parameters
 . Model routing and aliasing for friendly names
 . Format transforms from OpenAI format to provider-native formats
 . Request logging and cost tracking per gateway
@@ -621,7 +621,7 @@ GitHub Copilot generates distinct request patterns:
 |Metric |Purpose
 
 |Request volume by model
-|Identify most-used models via aliases
+|Identify most-used models through aliases
 
 |Token usage by model
 |Track consumption patterns (completion vs chat)
@@ -643,7 +643,7 @@ GitHub Copilot generates distinct request patterns:
 |===
 
 
-=== Query logs via API
+=== Query logs through API
 
 Programmatically access logs for integration with monitoring systems:
 

@@ -28,7 +28,7 @@ Before configuring GitHub Copilot, ensure you have:
 ** API key with access to the gateway
 * Your IDE:
 ** VS Code with GitHub Copilot extension installed
-** Or JetBrains IDE (IntelliJ IDEA, PyCharm, etc.) with GitHub Copilot plugin
+** Or JetBrains IDE (IntelliJ IDEA, PyCharm, and so on) with GitHub Copilot plugin
 
 == About GitHub Copilot and AI Gateway
 
@@ -112,7 +112,7 @@ Replace `https://gw.ai.panda.com/v1` with your gateway endpoint.
 
 IMPORTANT: This experimental feature requires configuring API keys and custom headers through the Copilot Chat UI, not in `settings.json`.
 
-==== Configure API key and headers via Copilot Chat UI
+==== Configure API key and headers through Copilot Chat UI
 
 . Open Copilot Chat in VS Code (`Cmd+I` or `Ctrl+I`)
 . Click the model selector dropdown
@@ -163,7 +163,7 @@ Add the base URL configuration in VS Code settings:
 
 Replace `https://gw.ai.panda.com/v1` with your gateway endpoint.
 
-==== Configure API key and headers via Copilot Chat UI
+==== Configure API key and headers through Copilot Chat UI
 
 IMPORTANT: Do not configure API keys or custom headers in `settings.json`. Use the Copilot Chat UI instead.
 
@@ -195,7 +195,7 @@ JetBrains IDE integration requires GitHub Copilot Enterprise with Bring Your Own
 
 === Configure BYOK with AI Gateway
 
-. Open your JetBrains IDE (IntelliJ IDEA, PyCharm, etc.)
+. Open your JetBrains IDE (IntelliJ IDEA, PyCharm, and so on)
 . Navigate to *Settings/Preferences*:
 ** macOS: `Cmd+,`
 ** Windows/Linux: `Ctrl+Alt+S`
@@ -320,7 +320,7 @@ For large organizations deploying GitHub Copilot Enterprise with AI Gateway acro
 
 === Centralized configuration management
 
-Distribute IDE configuration files via:
+Distribute IDE configuration files through:
 
 * **Git repository**: Store `settings.json` or IDE configuration in a shared repository
 * **Configuration management tools**: Puppet, Chef, Ansible
@@ -372,13 +372,13 @@ Single key for all developers:
 === Automated provisioning workflow
 
 . Developer joins organization
-. Identity system (Okta, Azure AD, etc.) triggers provisioning:
+. Identity system (Okta, Azure AD, and so on) triggers provisioning:
 .. Create Redpanda API key
 .. Assign to appropriate gateway
 .. Generate IDE configuration file with embedded keys
 .. Distribute to developer workstation
 . Developer installs IDE and GitHub Copilot
-. Configuration auto-applies (via MDM or configuration management)
+. Configuration auto-applies (through MDM or configuration management)
 . Developer starts using Copilot immediately
 
 === Observability and governance

@@ -189,7 +189,7 @@ response = client.messages.create(
 ----
 
 
-After (Gateway via OpenAI-compatible wrapper)
+After (Gateway through OpenAI-compatible wrapper)
 
 Because AI Gateway provides an OpenAI-compatible endpoint, we recommend migrating Anthropic SDK usage to OpenAI SDK for consistency:
 
@@ -264,7 +264,7 @@ else:
 ----
 
 
-After (Unified via Gateway)
+After (Unified through Gateway)
 
 [source,python]
 ----
@@ -403,8 +403,7 @@ Option D: Feature flag service (recommended)
 
 [source,python]
 ----
-# LaunchDarkly, Split.io, etc.
-use_gateway = feature_flags.is_enabled("ai-gateway", user_context)
+# LaunchDarkly, Split.io, and so onuse_gateway = feature_flags.is_enabled("ai-gateway", user_context)
 ----
 
 
@@ -869,7 +868,7 @@ A/B testing
 
 * Test new models without code changes
 * Compare quality/cost/latency
-* Gradual rollout via routing policies
+* Gradual rollout through routing policies
 
 == Next steps