AI API Gateway
for Mobile Apps

Mobile-Specific Considerations

Mobile apps face unique challenges when integrating AI API gateways: battery life, network reliability, data limits, and app size constraints.

iOS Integration

Swift SDK Setup

// Package.swift
dependencies: [
    .package(url: "https://github.com/example/ai-gateway-swift", from: "2.0.0")
]

// Initialize SDK
import AIGateway

let client = AIGatewayClient(
    apiKey: "your-api-key",
    configuration: .init(
        baseURL: URL(string: "https://api.yourgateway.com/v1")!,
        timeout: 60,
        retryPolicy: .exponentialBackoff(maxRetries: 3)
    )
)

// Make request
let response = try await client.chatCompletion(
    messages: [.user("Hello, AI!")],
    model: "gpt-4"
)

Background Processing

// Handle background API calls efficiently
class BackgroundAIService {
    func processInBackground(_ request: AIRequest) async {
        let task = Task {
            // Process AI request
            let result = try await client.process(request)
            // Store result locally
            await CacheManager.shared.store(result)
        }
        
        // Register with BackgroundTasks framework
        BGTaskScheduler.shared.submit(task)
    }
}

Android Integration

Kotlin SDK Setup

// build.gradle
implementation 'com.example:ai-gateway-android:2.0.0'

// Initialize SDK
val client = AIGatewayClient(
    apiKey = "your-api-key",
    config = ClientConfig(
        baseUrl = "https://api.yourgateway.com/v1",
        timeout = 60.seconds,
        retryPolicy = RetryPolicy.ExponentialBackoff(maxRetries = 3)
    )
)

// Make request (coroutine)
lifecycleScope.launch {
    val response = client.chatCompletion(
        messages = listOf(Message.User("Hello, AI!")),
        model = "gpt-4"
    )
}

WorkManager Integration

// Background AI processing with WorkManager
class AIProcessingWorker(
    context: Context,
    params: WorkerParameters
) : CoroutineWorker(context, params) {
    
    override suspend fun doWork(): Result {
        return try {
            val request = inputData.getString("request")
            val result = client.process(request)
            
            // Store result
            CacheManager.store(result)
            
            Result.success()
        } catch (e: Exception) {
            Result.retry()
        }
    }
}

// Schedule work
val workRequest = OneTimeWorkRequestBuilder<AIProcessingWorker>()
    .setInputData(workDataOf("request" to requestJson))
    .setConstraints(
        Constraints.Builder()
            .setRequiredNetworkType(NetworkType.CONNECTED)
            .setRequiresBatteryNotLow(true)
            .build()
    )
    .build()

WorkManager.getInstance(context).enqueue(workRequest)

Performance Optimization

Request Batching

Combine multiple AI requests into single API calls to reduce network overhead:

// Batch multiple prompts
val batchRequest = BatchRequest(
    requests = listOf(
        AIRequest(prompt = "Analyze sentiment 1"),
        AIRequest(prompt = "Analyze sentiment 2"),
        AIRequest(prompt = "Analyze sentiment 3")
    )
)

val responses = client.batchProcess(batchRequest)

Response Caching

• Memory cache: Fast access for frequently used responses (LRU eviction)
• Disk cache: Persistent storage for offline access
• Semantic cache: Match similar queries to cached responses

Offline Capabilities

Offline-First Architecture

• Local model fallback: Use on-device models for basic AI tasks when offline
• Request queuing: Queue API requests and process when connectivity returns
• Cached responses: Return cached results for repeated queries
• Graceful degradation: Communicate limited functionality to users