Aggregating APIs from multiple microservices usually means exposing one client-facing API that composes data and actions from several backend services. The common ways to do it are:

1) API Gateway

A single entry point that routes requests to the right microservice.

Use when:

• You want centralized auth, rate limiting, logging, TLS termination
• Clients mostly call one service at a time

Pros:

• Simple for clients
• Cross-cutting concerns in one place

Cons:

• Not ideal for combining data from multiple services into one response
• Can become a bottleneck if overloaded with orchestration logic

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2) Backend for Frontend (BFF)

A specialized API layer for a specific client type:

• Web app BFF
• Mobile BFF
• Partner API BFF

Use when:

• Different clients need different data shapes or workflows
• You want to aggregate several service calls per screen/page

Pros:

• Tailored responses for each client
• Less over-fetching/under-fetching

Cons:

• More APIs to maintain if you have many client types

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3) API Composition / Aggregation Service

A dedicated service that calls multiple microservices and combines their responses.

Typical flow:

1. Client calls aggregator
2. Aggregator calls Service A, B, C
3. Aggregator merges results
4. Returns a single response

Use when:

• You need a “dashboard” view or composite resource
• A request depends on data from multiple services

Implementation tips:

• Use async/concurrent calls where possible
• Set timeouts and fallbacks per service
• Cache stable data
• Avoid deep chaining of service calls

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4) GraphQL

Instead of manually creating aggregation endpoints, expose a GraphQL API that resolves data from multiple services.

Use when:

• Clients need flexible queries
• You want to reduce multiple round trips
• Responses vary a lot by screen/use case

Pros:

• Strong fit for aggregation
• Client can request exactly what it needs

Cons:

• Adds complexity in schema/resolvers
• Needs careful control of N+1 queries, caching, auth

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5) Event-driven aggregation

If the goal is not a real-time API response but a read model/reporting layer, build a materialized view from events.

Use when:

• You need analytics, search, dashboards
• Eventual consistency is acceptable

Pros:

• Fast reads
• Decouples services

Cons:

• Not real-time
• More infrastructure

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Practical design guidance

Prefer these patterns:

• Gateway for routing + security
• BFF or composition service for data aggregation
• GraphQL if clients need flexible data access
• Event-driven read model for reporting

Avoid:

• Letting microservices call each other in long chains
• Putting all business logic in the gateway
• Synchronous aggregation across too many services for latency-critical paths

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Example architecture

For a product detail page:

• Product Service → product info
• Inventory Service → stock status
• Pricing Service → current price
• Reviews Service → ratings/reviews

A BFF/aggregator might:

• call all four services in parallel
• normalize fields
• return one response to the UI

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Key concerns

• Latency: parallelize requests, set budgets
• Reliability: partial failures, retries, circuit breakers
• Security: propagate identity/authorization carefully
• Consistency: decide whether you need strong or eventual consistency
• Versioning: keep contracts stable across services

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If you want, I can also give you:

1. a reference architecture diagram,
2. a sample implementation in Node.js/Java/.NET, or
3. guidance on whether to choose API Gateway vs BFF vs GraphQL for your case.