Spring AI – API Design Principles for Portability, Consistency & Flexibility

Spring AI is designed with API portability and consistency at its core, so developers can seamlessly build AI-powered applications without worrying about vendor lock-in or implementation complexity. At the same time, it offers enough flexibility to customize behavior using provider-specific extensions.

In this blog, we’ll explore:

1. API Design Principles—Portability and Consistency

2. Sample Usage: Switching Between Sync and Streaming Calls

3. Advanced Customization Using Provider-Specific Extensions

4. 

   
   

1️⃣API Design Principles — Portability and Consistency

One of the main challenges in AI development is vendor fragmentation. Each LLM provider (OpenAI, Anthropic, Hugging Face, Ollama, etc.) exposes unique APIs, which makes it harder to switch providers or use multiple providers within the same application.

Spring AI solves this by:

• Unified API Abstractions – Write once, run across multiple providers.

• Consistent Interfaces – Predictable usage patterns for prompts, embeddings, and chat across providers.

• Easy Provider Swapping – Switch AI vendors with minimal code changes.

• Spring Ecosystem Integration – Reuse familiar Spring Boot patterns (dependency injection, configuration, profiles).

Example: Switching providers with a config change

spring:
  ai:
    openai:
      api-key: ${OPENAI_API_KEY}
    anthropic:
      api-key: ${ANTHROPIC_API_KEY}
    huggingface:
      api-key: ${HF_API_KEY}

👉 You can swap providers without rewriting business logic—just change configuration.

2️⃣ Switching Between Sync and Streaming Calls

AI applications often require different response modes:

• Synchronous (Sync): Best for short, direct responses (e.g., Q&A).

• Streaming: Best for long, progressive responses (e.g., chatbots, real-time UI).

Spring AI provides a single API to switch between sync and streaming, making it portable across providers.

Example: Sync call

String response = chatClient.prompt("Explain Spring AI in 3 bullet points.");
System.out.println(response);

Example: Streaming call

chatClient.promptStream("Explain Spring AI in detail.")
          .subscribe(token -> System.out.print(token));

👉 The same chatClient works for both modes—just switch the method call.

3️⃣ Advanced Customization Using Provider-Specific Extensions

While portability is key, sometimes developers need advanced features unique to a provider (e.g., OpenAI’s function calling, Anthropic’s system prompts, Hugging Face’s model fine-tuning).

Spring AI strikes a balance:

• Core API (portable) – Works across all providers.

• Provider-Specific Extensions – Access unique features without breaking portability.

Example: OpenAI function calling (custom extension)

OpenAiChatClient client = new OpenAiChatClient(openAiApiKey);

ChatRequest request = ChatRequest.builder()
    .message("Get current weather in New York")
    .function("getWeather")
    .build();

ChatResponse response = client.chat(request);
System.out.println(response.getFunctionResult());

Example: Anthropic system-level controls

AnthropicChatClient client = new AnthropicChatClient(apiKey);

ChatResponse response = client.chat(ChatRequest.builder()
    .system("You are a precise financial assistant.")
    .message("Summarize Apple’s Q2 earnings")
    .build());

👉 Developers can stay provider-agnostic most of the time but still leverage powerful vendor-specific features when needed.

comparison infographic showing which providers support sync, streaming, and custom extensions side by side.

Key Takeaways

• Spring AI ensures portability and consistency across providers with a unified API.

• Switching between sync and streaming calls is seamless and requires minimal code changes.

• Provider-specific extensions allow developers to unlock advanced features while retaining a portable foundation.