The fan-out pattern is a strategy for executing multiple, independent tasks in parallel to improve performance and throughput. Imagine a project manager assigning a large, complex task to five different team members at once, rather than handing it off from one person to the next. In software, this means a single incoming request is “fanned out” to multiple downstream services, workers, or APIs simultaneously.

This pattern is essential for building responsive, high-performance systems. Instead of waiting for a series of sequential operations to complete, you run them all at the same time and, in many cases, aggregate the results. The primary goal is to reduce the total time a user or system has to wait for a complex operation to finish.

A typical fan-out architecture involves a few key components working together to manage the flow of requests and responses. Understanding these roles is the first step to designing a robust system.

• The Originator: This is the service or client that initiates the original request. It’s the starting point of the entire workflow.
• The Dispatcher (or Router): This is the brains of the operation. The dispatcher receives the request from the originator and makes the decision to fan it out. It holds the logic for where to send the sub-requests.
• The Workers: These are the downstream services that do the actual work. Each worker receives a request from the dispatcher, processes it independently, and returns a result.
• The Aggregator: This component is responsible for collecting the responses from all the workers. It might combine the results into a single, cohesive response, handle errors from failed workers, and ensure the entire operation meets its deadline.

Not all fan-out operations happen at the same time. The two primary models are parallel and staged.

• Parallel fan-out is the most common approach, where the dispatcher sends requests to all workers simultaneously. This is ideal for maximizing speed when all tasks are fully independent.
• Staged fan-out involves sending requests in a sequence or in conditional groups. For example, a request might first go to a fast, inexpensive service. Only if that service returns a low-confidence result does the dispatcher “fan out” a second request to a more powerful, expensive service. This helps control costs and manage dependencies.

These components and models provide a flexible toolkit for designing efficient, scalable workflows.

The fan-out pattern is incredibly versatile and solves common problems in distributed systems, especially where speed and concurrency are critical.

• Data enrichment: When a user requests a profile, the system can fan out requests to separate services to fetch their purchase history, support tickets, and social media data simultaneously, rather than waiting for each one in sequence.
• Real-time notifications: A single event, like a customer placing an order, can trigger a fan-out to services that send a confirmation email, a push notification to the mobile app, and an SMS message to the user.
• AI and LLM chaining: A query to an AI system can be fanned out to multiple specialized models. One model might handle sentiment analysis, another extracts key entities, and a third generates a summary. The aggregator combines these outputs into a single, rich response.
• Distributed search: A search query in an e-commerce app can fan out to different indexes for products, user reviews, and help articles. The search page can then present aggregated results from all sources.

This list shows how fan-out enables complex, multi-part operations to feel instantaneous to the end-user.

While powerful, implementing a production-ready fan-out system comes with its own set of challenges. If you don’t plan for them, you can inadvertently build a system that is slow, expensive, and difficult to debug.

The main challenges include:

• The slowest responder problem: In a parallel fan-out, the total response time is often dictated by the slowest worker. A single misbehaving service can degrade the performance of the entire system.
• Error handling and partial failure: What happens if three of your five workers succeed but one fails? You need a strategy for partial failure. Should the entire operation fail, or can the aggregator proceed with incomplete data?
• Cost and resource management: Fanning out to many services, especially paid third-party APIs or large language models (LLMs), can become expensive very quickly. Without proper budgeting and controls, costs can spiral.
• Debugging and observability: Tracing a single logical request across a dozen distributed services is notoriously difficult. Without good logging and tracing, finding the root cause of an issue is like searching for a needle in a haystack.

Successfully implementing fan-out requires a deliberate, defensive approach. This playbook covers the key strategies for building a resilient, cost-effective, and observable system.

Don’t just blindly send every request to every worker. The dispatcher should be intelligent.

• Route by task: If the incoming request contains a specific field like {"task": "translate"}, the router should know to send it only to the translation service.
• Route by risk or confidence: For AI applications, you can create a staged fan-out. First, query a small, fast, and cheap model. If its confidence score is below a certain threshold (e.g., 90%), fan out the request to a larger, more expensive model for a higher-quality result. This balances cost and quality.

Every fan-out operation should have a strict budget across latency, cost, and resources. This prevents a single request from consuming excessive resources and impacting the rest of the system.

Here’s an example budget for an AI-powered data enrichment operation:

These budgets help you define the operational envelope and set clear expectations for each component.

To avoid the “slowest responder” problem, every worker branch needs its own Service Level Agreement (SLA) and timeout.

• Per-Branch SLA: Define the expected response time for each worker. For example, the user-history-service must respond in under 300ms.
• Soft Timeout: This is the ideal maximum time. If a worker misses the soft timeout, the aggregator might proceed with the data it has, ensuring the user still gets a fast (though potentially incomplete) response.
• Hard Timeout: This is the absolute latest a response will be accepted. After this, the aggregator considers the worker to have failed, cancels the request, and moves on.

Distributed systems fail, so build your fan-out logic to handle it gracefully.

• Retry with jitter: When a worker fails with a transient error (like a 503 Service Unavailable), it’s common to retry. However, if all dispatchers retry at the same instant, you can overload the downstream service. Adding “jitter” — a small, random delay before each retry — spreads out the load and helps the struggling service recover.
• Circuit breakers: If a specific worker service fails repeatedly, a circuit breaker can “trip,” causing the dispatcher to stop sending traffic to that service for a set period. This prevents the system from wasting time on a known-bad dependency and gives the failed service time to recover.

Don’t switch from a monolithic operation to a full fan-out overnight. Roll it out gradually.

1. Start with a single worker: Begin with the dispatcher-aggregator in place but routing to only one service.
2. Add a second worker in shadow mode: Add the next worker, but don’t use its result in the final aggregation. Simply log it and monitor its performance.
3. Enable for a small percentage of traffic: Use feature flags to enable the full fan-out with aggregation for 1% of users.
4. Monitor and increase: Closely watch your dashboards for latency, error rates, and costs. If everything looks healthy, gradually increase the traffic percentage until you reach 100%.

While Kinde doesn’t provide a fan-out dispatcher as a service, it offers foundational tools that help you securely manage the users, permissions, and events that trigger these complex workflows in your application.

For example, you can use Kinde’s webhooks to kick off a fan-out process. When a new user signs up, Kinde can fire a user.created event. A service in your infrastructure can listen for this event and then fan out requests to provision the user’s account in your CRM, analytics platform, and email marketing tool.

Furthermore, you can use Kinde’s feature flags to safely implement the rollout plan described above. By tying the new fan-out logic to a feature flag, you can enable it for internal test users, then for a small percentage of real users, and gradually roll it out to everyone while monitoring for any issues. This gives you fine-grained control over your production releases and minimizes risk.

• Feature flags
• Webhooks
• APIs