docs: gRPC-only OTLP, Windows 11, and availability targets

docs/DC/3D/Guides/Installation/Windows/index.md

@@ -4,6 +4,9 @@ description: Install DeepCube™ on Windows, through Steam or the offline MS
 
 # Installing on Windows
 
+!!! note "Requirements"
+    Windows 11, 64-bit. See [Supported Configurations](../../../Supported-Configurations/index.md) for full details.
+
 ## Steam
 
 [Open in Steam :material-steam:](steam://store/3624760){ .md-button .md-button--primary }

docs/DC/3D/Guides/Preferences/index.md

@@ -102,7 +102,7 @@ client: [contact support](../../../../contact.md) and say which GPU you have.
 
 !!! info "Minimum requirements"
     On Windows, the client needs at least a DirectX 12 capable GPU with 3 GB of memory,
-    on 64-bit Windows 10, and 8 GB is recommended. Below the minimum, lowering these
+    on 64-bit Windows 11, and 8 GB is recommended. Below the minimum, lowering these
     settings may not be enough. The full list, including experimental macOS support, is
     on [Supported Configurations](../../Supported-Configurations/index.md).
 

docs/DC/3D/Supported-Configurations/index.md

@@ -14,7 +14,7 @@ DeepCube runs on Windows, with experimental support for macOS. Linux support is
 
 | Component | Minimum | Recommended |
 |-----------|---------|-------------|
-| **Operating System** | Windows 10 64-bit | Windows 11 64-bit (latest) |
+| **Operating System** | Windows 11 64-bit | Windows 11 64-bit (latest) |
 | **Processor** | 4 cores, 3.0 GHz | 6 cores, 3.5 GHz |
 | **Graphics** | DirectX 12, 3 GB VRAM | DirectX 12, 8 GB VRAM |
 | **Memory** | 8 GB RAM | 32 GB RAM |

docs/DC/Studio/Guides/Installation/index.md

@@ -27,7 +27,7 @@ A subscription is required to use DeepCube Studio.
 5. Select a grid to connect to
 
 !!! note "Requirements"
-    Windows 10 or later (64-bit). See [Supported Configurations](../../Supported-Configurations/index.md) for full details.
+    Windows 11 (64-bit). See [Supported Configurations](../../Supported-Configurations/index.md) for full details.
 
 ## macOS
 

docs/DC/Studio/Supported-Configurations/index.md

@@ -16,7 +16,7 @@ DeepCube Studio is a lightweight native desktop application. It does not require
 
 | Component | Requirement |
 |-----------|-------------|
-| **Operating System** | Windows 10 64-bit or later |
+| **Operating System** | Windows 11 64-bit |
 | **Processor** | 2 cores, 2.0 GHz or faster |
 | **Memory** | 4 GB RAM |
 | **Storage** | 500 MB available |

docs/DC/Web/Troubleshooting/index.md

@@ -114,7 +114,7 @@ CORS (Cross-Origin Resource Sharing) errors occur when your instrumented applica
 |---------|-------|------------|
 | `Access-Control-Allow-Origin` error in browser console | Browser-based exporter sending directly to DeepCube endpoint | Browser-based (front-end) applications should route telemetry through a backend collector or proxy rather than sending directly to the DeepCube endpoint. |
 | CORS error only in production | Different origin in production vs development | Verify that your production domain is configured correctly in your collector setup. |
-| Preflight (OPTIONS) request fails | Firewall or proxy blocking OPTIONS requests | Ensure your network infrastructure allows HTTP OPTIONS requests to the DeepCube ingestion endpoint. |
+| Preflight (OPTIONS) request fails | Browser-based exporter sending directly to DeepCube endpoint | DeepCube does not accept telemetry from the browser directly. Route it through a backend OpenTelemetry Collector. |
 
 !!! warning "Browser instrumentation"
     Sending telemetry directly from browser JavaScript to the DeepCube endpoint will trigger CORS errors. Use a backend OpenTelemetry Collector as a relay to avoid this issue.

docs/Getting-Started/Plans/index.md

@@ -23,13 +23,15 @@ each plan does.
 | **Data Retention** | 7 days | 30 days | 90 days | 365 days |
 | **AI (Tessa)** | 7 queries/day | 35 queries/day | Large daily budget + Code Fix | Large daily budget + Code Fix + Infra |
 | **Support** | Discord | Chat/Email | Chat/Email/Phone | Chat/Email/Phone |
-| **Uptime SLA** | - | 99% | 99.9% | 99.99% |
+| **Availability target** | None | 99% | 99.9% | 99.99% |
 | **Dedicated Success Rep** | :material-close: | :material-close: | :material-check: | :material-check: |
 | **Professional Consulting** | :material-close: | :material-close: | :material-check: | :material-check: |
 | **Early Access Features** | :material-close: | :material-close: | :material-close: | :material-check: |
 
 *Throughput = traces + logs per second*
 
+*Availability targets are not a service level agreement. Visualize is the one exception: its contractual commitment is 99.0%, with service credits. Your subscription terms are in the [Subscription Agreement](https://immersivefusion.com/legal/){ target="_blank" }.*
+
 *AI (Tessa) usage is a daily budget that scales with your monitored nodes, not an unlimited allowance. No plan is ever blocked: when a budget is spent, Tessa keeps working with lighter, faster responses until it refills.*
 
 ## Plan Details
@@ -50,7 +52,7 @@ Experience application immersion with zero risk, allowing you to gain valuable i
 Enhance your application immersion capabilities with expanded availability and comprehensive support.
 
 - Shared infrastructure
-- 99% uptime SLA
+- 99% availability target; the contractual commitment is 99.0%, with service credits
 - 200 traces + logs per second
 - 30 days data retention
 - 35 AI Assistant queries per day
@@ -61,7 +63,7 @@ Enhance your application immersion capabilities with expanded availability and c
 Improve your application visualization capabilities by incorporating historical data retention and dedicated resources.
 
 - Dedicated infrastructure
-- 99.9% uptime SLA
+- 99.9% availability target
 - 1,000 traces + logs per second
 - 90 days data retention
 - Large daily AI Assistant budget + Code Fix
@@ -74,7 +76,7 @@ Improve your application visualization capabilities by incorporating historical
 Elevate your application monitoring and management with the stability, scalability, and expertise.
 
 - Dedicated infrastructure
-- 99.99% uptime SLA
+- 99.99% availability target
 - 2,000 traces + logs per second
 - 365 days data retention
 - Large daily AI Assistant budget + Code Fix + Infra

docs/Instrument/collector/index.md

@@ -174,7 +174,7 @@ service:
 | Exporter | Description |
 |----------|-------------|
 | `otlp` | Exports via OTLP gRPC |
-| `otlphttp` | Exports via OTLP HTTP |
+| `otlphttp` | Exports via OTLP HTTP. Not accepted by DeepCube; use `otlp` |
 | `debug` | Prints telemetry to stdout (for development) |
 
 ## Docker Compose Example
@@ -202,6 +202,7 @@ services:
     build: .
     environment:
       - OTEL_EXPORTER_OTLP_ENDPOINT=http://otel-collector:4317
+      - OTEL_EXPORTER_OTLP_PROTOCOL=grpc
       - OTEL_SERVICE_NAME=my-app
     depends_on:
       - otel-collector

docs/Instrument/dotnet/index.md

@@ -174,7 +174,7 @@ Instead of hardcoding values, use environment variables. The OpenTelemetry .NET
 | `OTEL_EXPORTER_OTLP_ENDPOINT` | `https://otlp.deepcube.ai` | OTLP collector endpoint |
 | `OTEL_EXPORTER_OTLP_HEADERS` | `api-key=YOUR-API-KEY` | Authentication header |
 | `OTEL_SERVICE_NAME` | `your-service-name` | Service name shown in DeepCube |
-| `OTEL_EXPORTER_OTLP_PROTOCOL` | `grpc` | Protocol (grpc or http/protobuf) |
+| `OTEL_EXPORTER_OTLP_PROTOCOL` | `grpc` | Protocol (must be `grpc`) |
 
 When using environment variables, your code simplifies to:
 

docs/Instrument/go/index.md

@@ -293,7 +293,7 @@ The OpenTelemetry Go SDK reads these variables when using the autoconfigure opti
 | `OTEL_EXPORTER_OTLP_ENDPOINT` | `https://otlp.deepcube.ai` | OTLP collector endpoint |
 | `OTEL_EXPORTER_OTLP_HEADERS` | `api-key=YOUR-API-KEY` | Authentication header |
 | `OTEL_SERVICE_NAME` | `your-service-name` | Service name shown in DeepCube |
-| `OTEL_EXPORTER_OTLP_PROTOCOL` | `grpc` | Protocol (grpc or http/protobuf) |
+| `OTEL_EXPORTER_OTLP_PROTOCOL` | `grpc` | Protocol (must be `grpc`) |
 
 !!! note "Environment variables in Go"
     Unlike some other language SDKs, the Go SDK does not automatically read `OTEL_EXPORTER_OTLP_ENDPOINT` by default. You need to either configure the exporter programmatically (as shown above) or use the `autoexport` package:
@@ -302,6 +302,8 @@ The OpenTelemetry Go SDK reads these variables when using the autoconfigure opti
     go get go.opentelemetry.io/contrib/exporters/autoexport
     ```
 
+    `autoexport` defaults to `http/protobuf`, so set `OTEL_EXPORTER_OTLP_PROTOCOL=grpc` when you use it.
+
 ## Verify It's Working
 
 1. Run your instrumented application

docs/Instrument/index.md

@@ -11,9 +11,11 @@ All you need is your **OTLP endpoint** and **API key**:
 | Setting | Value |
 |---------|-------|
 | **Endpoint** | `https://otlp.deepcube.ai` |
-| **Protocol** | `grpc` (default) or `http/protobuf` |
+| **Protocol** | `grpc` |
 | **API Key Header** | `api-key: YOUR-API-KEY` |
 
+DeepCube accepts OTLP over gRPC only. OTLP over HTTP (`http/protobuf`) is not accepted, and exports that use it fail.
+
 Get your API key by logging in at [portal.deepcube.ai](https://portal.deepcube.ai){ target="_blank" }, then go to **Administration > Grids** and click **Instrument** on your Grid.
 
 ### Environment Variables
@@ -24,6 +26,7 @@ The fastest way to configure any OpenTelemetry SDK is with environment variables
 |----------|-------|-------------|
 | `OTEL_EXPORTER_OTLP_ENDPOINT` | `https://otlp.deepcube.ai` | OTLP collector endpoint |
 | `OTEL_EXPORTER_OTLP_HEADERS` | `api-key=YOUR-API-KEY` | Authentication header |
+| `OTEL_EXPORTER_OTLP_PROTOCOL` | `grpc` | Protocol (must be `grpc`; some SDKs default to `http/protobuf`) |
 | `OTEL_SERVICE_NAME` | `your-service-name` | Identifies your service in DeepCube |
 
 ## Quick Start by Language
@@ -64,6 +67,7 @@ Choose your language to see the minimal configuration needed to start sending te
     # Run your application with auto-instrumentation
     java -javaagent:opentelemetry-javaagent.jar \
       -Dotel.exporter.otlp.endpoint=https://otlp.deepcube.ai \
+      -Dotel.exporter.otlp.protocol=grpc \
       -Dotel.exporter.otlp.headers=api-key=YOUR-API-KEY \
       -Dotel.service.name=your-service-name \
       -jar your-app.jar

docs/Instrument/java/index.md

@@ -22,11 +22,15 @@ curl -L -o opentelemetry-javaagent.jar \
 ```bash
 java -javaagent:opentelemetry-javaagent.jar \
   -Dotel.exporter.otlp.endpoint=https://otlp.deepcube.ai \
+  -Dotel.exporter.otlp.protocol=grpc \
   -Dotel.exporter.otlp.headers=api-key=YOUR-API-KEY \
   -Dotel.service.name=my-java-app \
   -jar your-application.jar
 ```
 
+!!! warning "Keep the protocol set to gRPC"
+    Versions 2.0 and later of the Java agent and the Spring Boot starter default to `http/protobuf`, which DeepCube does not accept. Set the protocol to `grpc` wherever you configure them.
+
 The agent automatically instruments:
 
 - Spring Web MVC, WebFlux
@@ -81,6 +85,7 @@ otel:
   exporter:
     otlp:
       endpoint: https://otlp.deepcube.ai
+      protocol: grpc
       headers:
         api-key: YOUR-API-KEY
   service:
@@ -91,6 +96,7 @@ Or in `application.properties`:
 
 ```properties
 otel.exporter.otlp.endpoint=https://otlp.deepcube.ai
+otel.exporter.otlp.protocol=grpc
 otel.exporter.otlp.headers.api-key=YOUR-API-KEY
 otel.service.name=my-spring-boot-app
 ```
@@ -253,7 +259,7 @@ The Java agent and SDK autoconfigure module read these variables:
 | `OTEL_EXPORTER_OTLP_ENDPOINT` | `https://otlp.deepcube.ai` | OTLP collector endpoint |
 | `OTEL_EXPORTER_OTLP_HEADERS` | `api-key=YOUR-API-KEY` | Authentication header |
 | `OTEL_SERVICE_NAME` | `your-service-name` | Service name shown in DeepCube |
-| `OTEL_EXPORTER_OTLP_PROTOCOL` | `grpc` | Protocol (grpc or http/protobuf) |
+| `OTEL_EXPORTER_OTLP_PROTOCOL` | `grpc` | Protocol (must be `grpc`) |
 | `OTEL_TRACES_EXPORTER` | `otlp` | Trace exporter type |
 | `OTEL_METRICS_EXPORTER` | `otlp` | Metrics exporter type |
 | `OTEL_LOGS_EXPORTER` | `otlp` | Logs exporter type |
@@ -263,6 +269,7 @@ You can also use JVM system properties (prefix with `-D` and replace underscores
 ```bash
 java -javaagent:opentelemetry-javaagent.jar \
   -Dotel.exporter.otlp.endpoint=https://otlp.deepcube.ai \
+  -Dotel.exporter.otlp.protocol=grpc \
   -Dotel.exporter.otlp.headers=api-key=YOUR-API-KEY \
   -Dotel.service.name=my-java-app \
   -jar your-app.jar

docs/Instrument/kubernetes/index.md

@@ -36,7 +36,7 @@ kubectl apply -f https://github.com/open-telemetry/opentelemetry-operator/releas
 
 ### Create an Instrumentation Resource
 
-Define an `Instrumentation` custom resource that tells the Operator how to configure auto-instrumentation:
+Define an `Instrumentation` custom resource that tells the Operator how to configure auto-instrumentation. This resource covers Node.js, which exports directly to DeepCube:
 
 ```yaml
 # instrumentation.yaml
@@ -57,14 +57,34 @@ spec:
         secretKeyRef:
           name: deepcube-api-key
           key: api-key
+  nodejs:
+    image: ghcr.io/open-telemetry/opentelemetry-operator/autoinstrumentation-nodejs:latest
+```
+
+!!! warning "Use a Collector for Java, Python, .NET, and Go"
+    DeepCube accepts OTLP over gRPC only. The Operator's auto-instrumentation defaults to `http/protobuf` for Java, Python, .NET, and Go, and the Operator's Python image does not support gRPC at this time. Only Node.js defaults to gRPC. Send the other languages through a [Collector](#otel-collector-as-daemonset), which receives OTLP over HTTP on port 4318 and exports to DeepCube over gRPC.
+
+For those languages, deploy the Collector, then create a second resource that points at it. Replace `YOUR-COLLECTOR-HOST` with the DNS name of your Collector's Kubernetes Service, and make sure that Service exposes port 4318:
+
+```yaml
+# instrumentation-collector.yaml
+apiVersion: opentelemetry.io/v1alpha1
+kind: Instrumentation
+metadata:
+  name: deepcube-collector-instrumentation
+  namespace: default
+spec:
+  exporter:
+    endpoint: http://YOUR-COLLECTOR-HOST:4318
+  propagators:
+    - tracecontext
+    - baggage
   dotnet:
     image: ghcr.io/open-telemetry/opentelemetry-operator/autoinstrumentation-dotnet:latest
   java:
     image: ghcr.io/open-telemetry/opentelemetry-operator/autoinstrumentation-java:latest
   python:
     image: ghcr.io/open-telemetry/opentelemetry-operator/autoinstrumentation-python:latest
-  nodejs:
-    image: ghcr.io/open-telemetry/opentelemetry-operator/autoinstrumentation-nodejs:latest
   go:
     image: ghcr.io/open-telemetry/opentelemetry-operator/autoinstrumentation-go:latest
 ```
@@ -83,50 +103,51 @@ kubectl create secret generic deepcube-api-key \
 
 ```bash
 kubectl apply -f instrumentation.yaml
+kubectl apply -f instrumentation-collector.yaml
 ```
 
 ### Annotate Your Pods
 
-Add an annotation to your pod spec to enable auto-instrumentation for your language:
+Add an annotation to your pod spec to enable auto-instrumentation for your language. The value names the `Instrumentation` resource to use:
 
 === ".NET"
 
     ```yaml
     metadata:
       annotations:
-        instrumentation.opentelemetry.io/inject-dotnet: "true"
+        instrumentation.opentelemetry.io/inject-dotnet: "deepcube-collector-instrumentation"
     ```
 
 === "Java"
 
     ```yaml
     metadata:
       annotations:
-        instrumentation.opentelemetry.io/inject-java: "true"
+        instrumentation.opentelemetry.io/inject-java: "deepcube-collector-instrumentation"
     ```
 
 === "Python"
 
     ```yaml
     metadata:
       annotations:
-        instrumentation.opentelemetry.io/inject-python: "true"
+        instrumentation.opentelemetry.io/inject-python: "deepcube-collector-instrumentation"
     ```
 
 === "Node.js"
 
     ```yaml
     metadata:
       annotations:
-        instrumentation.opentelemetry.io/inject-nodejs: "true"
+        instrumentation.opentelemetry.io/inject-nodejs: "deepcube-instrumentation"
     ```
 
 === "Go"
 
     ```yaml
     metadata:
       annotations:
-        instrumentation.opentelemetry.io/inject-go: "true"
+        instrumentation.opentelemetry.io/inject-go: "deepcube-collector-instrumentation"
     ```
 
 Example Deployment with auto-instrumentation:
@@ -146,7 +167,7 @@ spec:
       labels:
         app: my-app
       annotations:
-        instrumentation.opentelemetry.io/inject-java: "true"
+        instrumentation.opentelemetry.io/inject-nodejs: "deepcube-instrumentation"
     spec:
       containers:
         - name: my-app
@@ -236,6 +257,8 @@ env:
         fieldPath: status.hostIP
   - name: OTEL_EXPORTER_OTLP_ENDPOINT
     value: "http://$(NODE_IP):4317"
+  - name: OTEL_EXPORTER_OTLP_PROTOCOL
+    value: grpc
   - name: OTEL_SERVICE_NAME
     value: my-app
 ```

docs/Instrument/nodejs/index.md

@@ -246,7 +246,7 @@ The OpenTelemetry Node.js SDK reads these variables automatically:
 | `OTEL_EXPORTER_OTLP_ENDPOINT` | `https://otlp.deepcube.ai` | OTLP collector endpoint |
 | `OTEL_EXPORTER_OTLP_HEADERS` | `api-key=YOUR-API-KEY` | Authentication header |
 | `OTEL_SERVICE_NAME` | `your-service-name` | Service name shown in DeepCube |
-| `OTEL_EXPORTER_OTLP_PROTOCOL` | `grpc` | Protocol (grpc or http/protobuf) |
+| `OTEL_EXPORTER_OTLP_PROTOCOL` | `grpc` | Protocol (must be `grpc`) |
 | `OTEL_TRACES_EXPORTER` | `otlp` | Trace exporter type |
 | `OTEL_METRICS_EXPORTER` | `otlp` | Metrics exporter type |
 | `OTEL_LOG_LEVEL` | `info` | SDK diagnostic log level |
@@ -295,15 +295,7 @@ const sdk = new NodeSDK({
 ### gRPC connection errors
 
 - Ensure `@grpc/grpc-js` is installed: `npm install @grpc/grpc-js`.
-- If behind a corporate proxy, you may need to use the HTTP/protobuf exporter instead:
-
-    ```bash
-    npm install @opentelemetry/exporter-trace-otlp-proto
-    ```
-
-    ```javascript
-    const { OTLPTraceExporter } = require('@opentelemetry/exporter-trace-otlp-proto');
-    ```
+- If you are behind a corporate proxy, make sure it allows gRPC (HTTP/2) traffic to `otlp.deepcube.ai:443`. DeepCube does not accept OTLP over HTTP, so switching to an HTTP exporter does not help.
 
 ### ES Modules (import/export) compatibility
 

docs/Instrument/python/index.md

@@ -270,7 +270,7 @@ The OpenTelemetry Python SDK reads these variables automatically:
 | `OTEL_EXPORTER_OTLP_ENDPOINT` | `https://otlp.deepcube.ai` | OTLP collector endpoint |
 | `OTEL_EXPORTER_OTLP_HEADERS` | `api-key=YOUR-API-KEY` | Authentication header |
 | `OTEL_SERVICE_NAME` | `your-service-name` | Service name shown in DeepCube |
-| `OTEL_EXPORTER_OTLP_PROTOCOL` | `grpc` | Protocol (grpc or http/protobuf) |
+| `OTEL_EXPORTER_OTLP_PROTOCOL` | `grpc` | Protocol (must be `grpc`) |
 | `OTEL_TRACES_EXPORTER` | `otlp` | Trace exporter type |
 | `OTEL_METRICS_EXPORTER` | `otlp` | Metrics exporter type |
 | `OTEL_LOGS_EXPORTER` | `otlp` | Logs exporter type |