AI & Development
How to integrate the Claude API into iOS and Android apps: model selection, cost control, streaming, tool use.
Adding an LLM to a mobile app is not the same problem as adding one to a web app. Mobile introduces constraints that do not exist in a browser or server context: variable network connectivity, battery and processing limits, strict App Store guidelines about user data handling, and users who expect sub-second response times on a 4G connection in a moving vehicle. This guide covers what actually matters when integrating the Claude API into an iOS or Android app - the architecture choices, cost management patterns, and failure modes that take most teams by surprise.
The Claude model family offers different trade-offs between capability, latency, and cost. For mobile, the choice is usually between two approaches:
Avoid routing all traffic through the most capable model by default. An auto-complete feature that sends every keystroke to claude-opus-5 will cost 10 to 50× more than the same feature on Haiku, with no perceptible quality difference for the use case. Model selection is your first and highest-leverage cost control decision.
This is the most important architectural decision: the API key must never be embedded in your app binary. App Store binaries can be extracted and reverse-engineered, and an embedded API key will be extracted within hours of your app being published. The correct architecture is:
This architecture also gives you a place to inject server-side context (user history, app state, personalisation data) into the API request without exposing it to the client, and a natural insertion point for abuse prevention, logging, and cost monitoring.
Streaming - receiving the model's response as a token-by-token stream rather than waiting for the full response - dramatically improves perceived latency in mobile UIs. A 3-second streamed response where the first word appears in 400ms feels faster than a 1.5-second non-streamed response where nothing appears for 1.5 seconds. Streaming is one of the highest-impact UX improvements in mobile AI, and it is straightforward to implement.
The Anthropic API supports server-sent events (SSE) for streaming. On your backend, you forward the SSE stream to the mobile client. On the client side, you parse the stream and update the UI incrementally. In iOS, this is cleanest using URLSession with a URLSessionDataDelegate. In Android, use OkHttp's EventSource implementation or Retrofit with a Streaming annotation.
One iOS-specific pitfall: the default URLSession configuration buffers responses until the content-length header resolves. For SSE streaming, you need to configure URLSession with a delegate and call `urlSession(_:dataTask:didReceive:)` to process data chunks as they arrive, bypassing the buffering behaviour.
Mobile apps can generate LLM costs that scale rapidly with user growth if you are not deliberate about control mechanisms from the start. The patterns that matter most:
Mobile users lose connectivity. Your AI feature's failure mode when the API is unreachable determines how much trust you lose from users. Three patterns worth implementing:
Claude's tool use feature (function calling) is particularly valuable in mobile contexts because it gives you structured, type-safe data from the model instead of free-form text you then need to parse. If your feature requires the model to extract specific data fields - a date, a category, a set of tags - define a tool with the desired schema and require the model to use it. The result is JSON that matches your schema, which your mobile app can deserialise directly into native data models without string parsing or regex.
Tool use is also the right approach for any feature that requires the model to take actions in your app - searching a knowledge base, querying a database, triggering a workflow. Define the action as a tool, let the model decide when to call it, and handle the tool call result on your backend before returning the final response to the mobile client.
Apple and Google have specific guidelines for apps that use AI-generated content. The key requirements to be aware of:
The production architecture we have settled on for our own apps: mobile client authenticates with Firebase Auth → Firebase Cloud Function validates token, applies rate limiting, injects server-side context, calls Anthropic API → response streamed back to client via a Cloud Function response stream. The Cloud Function also logs token usage per user to Firestore for cost monitoring.
This architecture costs about $0.50, $2.00 per 1,000 active users per month at moderate usage levels, with Haiku as the primary model. The Firebase infrastructure scales automatically, the rate limiting prevents runaway costs, and the logging gives us visibility into which features are actually being used and at what cost - essential information for making model selection decisions as your feature set grows.