Edge vs. cloud fan-out is a hybrid architectural pattern where a computational task starts on a user’s local device (the “edge”) and is selectively escalated, or “fanned out,” to more powerful cloud-based models. This approach combines the speed and privacy of on-device processing with the immense power and knowledge of large-scale, hosted AI models, creating a responsive, efficient, and versatile user experience.

Instead of choosing between a lightweight edge model and a heavyweight cloud model, this architecture lets you use both. It provides an immediate, local preview of a result and then enhances or completes it using cloud resources for more complex cases, striking a balance between performance, cost, and capability.

The fan-out workflow intelligently splits processing across local and remote resources. While implementations vary, the core process typically follows a few key steps.

1. Initiation on the Edge: A user triggers an action in an application, like asking for a code completion in an IDE or applying a filter to a photo on a mobile device.
2. Local First-Pass: A small, efficient on-device model immediately processes the request. It generates a “good enough” preview, providing a near-instant response. For example, it might suggest a common code snippet or apply a standard image filter.
3. Escalation Check: The application’s logic determines if the initial result is sufficient or if the task requires more sophisticated processing. This decision can be based on the complexity of the request, user settings, or predefined rules.
4. Fan-Out to Cloud: If escalation is needed, the application sends the request—or a sanitized, privacy-preserving version of it—to a more powerful, specialized cloud-based AI model. This is the “fan-out” step.
5. Cloud Processing: The cloud model performs the heavy lifting, such as analyzing the entire codebase to provide a more context-aware suggestion or performing a complex, generative fill on an image.
6. Reconciliation: The enhanced result from the cloud is sent back to the device. The application then reconciles this new information with the local preview, seamlessly updating the user interface with the higher-fidelity output.

This entire process ensures the user never sees a blank screen, getting immediate feedback from the edge model while the more powerful cloud model works in the background.

Adopting a fan-out architecture offers significant advantages over relying solely on either edge or cloud processing. It allows developers to build more robust, user-friendly, and cost-effective applications.

The key benefits of this hybrid approach include:

• Low Latency: Users get an instant response from the on-device model, making the application feel fast and responsive.
• Enhanced Privacy: Sensitive data, such as personally identifiable information (PII) within a codebase, can be processed locally or anonymized before being sent to the cloud, strengthening user trust.
• Offline Functionality: The application remains useful even without an internet connection, as the core features powered by the edge model continue to work.
• Cost Efficiency: Computationally expensive cloud models are only used when necessary, significantly reducing API costs compared to a cloud-only approach.
• Superior Capabilities: Users get the best of both worlds—the convenience of local processing and access to state-of-the-art AI for complex tasks.

Implementing a fan-out model requires careful planning around data privacy, connectivity, and cost management. Thoughtful design in these areas is crucial for building a system that is both powerful and trustworthy.

You must clearly define what data leaves the device. Before fanning out a request, implement a sanitization layer to strip or anonymize PII. For an IDE extension, this could mean replacing proprietary variable names and comments with generic placeholders, ensuring the user’s intellectual property remains secure.

A robust fan-out system should handle intermittent connectivity gracefully. When a cloud request is triggered while the user is offline, the application should queue the request. Once the connection is restored, the queued requests can be sent to the cloud, and the results can be reconciled. The on-device model acts as a reliable fallback, ensuring the user experience is never fully interrupted.

To optimize costs, avoid sending every complex request to the cloud in real-time. Instead, implement a batching strategy. For a CLI tool that analyzes code, you could batch multiple analysis requests into a single, consolidated API call. This “cloud burst” approach reduces the overhead of per-request fees and can lower overall cloud expenditure.

The fan-out model is highly effective for applications that need to balance real-time interaction with deep, computational analysis.

These examples illustrate how the fan-out pattern delivers a responsive base experience while making powerful, resource-intensive features available on demand.

While Kinde doesn’t provide AI models, it offers the critical infrastructure for managing user access and entitlements in a fan-out architecture. By controlling who can access your cloud-based models and how, you can effectively manage costs and segment features.

With Kinde, you can use feature flags to control the “escalate to cloud” behavior. For example, you can enable cloud processing for users on a “Pro” plan while limiting “Free” users to the on-device model. This allows you to create tiered subscription plans where access to powerful, expensive AI models is a premium feature.

Furthermore, you can use Kinde’s roles and permissions to secure the API endpoint for your cloud model. By requiring a valid authentication token with the correct permissions, you ensure that only authorized users and applications can trigger cloud-based processing, protecting your resources from unauthorized use.

• About feature flags
• Manage feature flags through the Kinde API
• Roles and permissions