How to Use Gemini API with Spring Boot AI: Complete Integration Guide

Google’s Gemini AI models are transforming how developers build intelligent applications. With Spring AI, integrating Gemini into your Spring Boot applications has never been easier. This comprehensive guide walks you through everything you need to know to harness the power of Gemini AI in your Java backend.

Why Integrate Gemini AI with Spring Boot?

Spring Boot powers millions of enterprise applications worldwide. By integrating Gemini AI, you can:

• Add Natural Language Processing - Enable chatbots, content generation, and text analysis
• Build Intelligent APIs - Create AI-powered endpoints for your applications
• Automate Content Creation - Generate reports, summaries, and documentation automatically
• Enhance Search Capabilities - Implement semantic search and intelligent recommendations
• Process Multimodal Data - Work with text, images, code, and PDFs using Gemini’s multimodal capabilities

“Spring AI provides the perfect bridge between enterprise Java applications and cutting-edge AI models like Gemini. The integration is seamless and production-ready.” — Spring AI Documentation

Prerequisites

Before we begin, ensure you have:

• Java 21 or higher installed
• Spring Boot 3.2+ project setup
• Google AI Studio account (for API key)
• Maven 3.8+ or Gradle 8+
• Basic understanding of Spring Boot and REST APIs

Method 1: Direct Gemini API Integration (Recommended for Most)

The simplest way to integrate Gemini is using the direct API approach without Vertex AI. This method is perfect for developers who want quick setup without Google Cloud complexity.

Step 1: Get Your Gemini API Key

1. Visit Google AI Studio
2. Sign in with your Google account
3. Click “Create API Key”
4. Copy and securely store your API key

# Set as environment variable (recommended)
export GEMINI_API_KEY="your-api-key-here"

# Or add to .env file
GEMINI_API_KEY=your-api-key-here

Step 2: Add Dependencies

Add the Spring AI Google GenAI starter to your pom.xml:

<dependencies>
    <!-- Spring Boot Starter Web -->
    <dependency>
        <groupId>org.springframework.boot</groupId>
        <artifactId>spring-boot-starter-web</artifactId>
    </dependency>

    <!-- Spring AI Google GenAI -->
    <dependency>
        <groupId>org.springframework.ai</groupId>
        <artifactId>spring-ai-starter-model-google-genai</artifactId>
        <version>1.0.0-M6</version>
    </dependency>

    <!-- Lombok (Optional) -->
    <dependency>
        <groupId>org.projectlombok</groupId>
        <artifactId>lombok</artifactId>
        <optional>true</optional>
    </dependency>
</dependencies>

<repositories>
    <!-- Spring AI Milestone Repository -->
    <repository>
        <id>spring-milestones</id>
        <name>Spring Milestones</name>
        <url>https://repo.spring.io/milestone</url>
        <snapshots>
            <enabled>false</enabled>
        </snapshots>
    </repository>
</repositories>

For Gradle users:

dependencies {
    implementation 'org.springframework.boot:spring-boot-starter-web'
    implementation 'org.springframework.ai:spring-ai-starter-model-google-genai:1.0.0-M6'
    compileOnly 'org.projectlombok:lombok'
    annotationProcessor 'org.projectlombok:lombok'
}

repositories {
    maven { url 'https://repo.spring.io/milestone' }
}

Step 3: Configure Application Properties

Create or update src/main/resources/application.yml:

spring:
  application:
    name: gemini-ai-app
  ai:
    google:
      genai:
        api-key: ${GEMINI_API_KEY}
        chat:
          options:
            model: gemini-2.0-flash
            temperature: 0.7
            max-output-tokens: 8192

Or using application.properties:

# Application Name
spring.application.name=gemini-ai-app

# Google GenAI Configuration
spring.ai.google.genai.api-key=${GEMINI_API_KEY}
spring.ai.google.genai.chat.options.model=gemini-2.0-flash
spring.ai.google.genai.chat.options.temperature=0.7
spring.ai.google.genai.chat.options.max-output-tokens=8192

Step 4: Create the AI Service

Create a service class to encapsulate Gemini AI logic:

package com.example.gemini.service;

import org.springframework.ai.chat.client.ChatClient;
import org.springframework.ai.chat.model.ChatModel;
import org.springframework.ai.chat.model.ChatResponse;
import org.springframework.ai.chat.prompt.Prompt;
import org.springframework.stereotype.Service;
import reactor.core.publisher.Flux;

@Service
public class GeminiAiService {

    private final ChatModel chatModel;
    private final ChatClient chatClient;

    public GeminiAiService(ChatModel chatModel) {
        this.chatModel = chatModel;
        this.chatClient = ChatClient.create(chatModel);
    }

    /**
     * Simple text generation
     */
    public String generateResponse(String userMessage) {
        return chatClient.prompt()
                .user(userMessage)
                .call()
                .content();
    }

    /**
     * Generate with system prompt
     */
    public String generateWithSystemPrompt(String userMessage, String systemPrompt) {
        return chatClient.prompt()
                .system(systemPrompt)
                .user(userMessage)
                .call()
                .content();
    }

    /**
     * Streaming response
     */
    public Flux<String> streamResponse(String userMessage) {
        return chatClient.prompt()
                .user(userMessage)
                .stream()
                .content();
    }

    /**
     * Generate with custom options
     */
    public String generateWithOptions(String userMessage, Double temperature, Integer maxTokens) {
        return chatClient.prompt()
                .user(userMessage)
                .options(options -> options
                        .temperature(temperature)
                        .maxTokens(maxTokens))
                .call()
                .content();
    }
}

Step 5: Create the REST Controller

Expose AI capabilities through REST endpoints:

package com.example.gemini.controller;

import com.example.gemini.service.GeminiAiService;
import org.springframework.http.MediaType;
import org.springframework.http.ResponseEntity;
import org.springframework.web.bind.annotation.*;
import reactor.core.publisher.Flux;

import java.util.Map;

@RestController
@RequestMapping("/api/ai")
@CrossOrigin(origins = "*")
public class GeminiAiController {

    private final GeminiAiService aiService;

    public GeminiAiController(GeminiAiService aiService) {
        this.aiService = aiService;
    }

    /**
     * Simple chat endpoint
     */
    @PostMapping("/chat")
    public ResponseEntity<Map<String, String>> chat(@RequestBody Map<String, String> request) {
        String message = request.get("message");
        String response = aiService.generateResponse(message);
        return ResponseEntity.ok(Map.of(
                "message", message,
                "response", response
        ));
    }

    /**
     * Chat with system prompt
     */
    @PostMapping("/chat/system")
    public ResponseEntity<Map<String, String>> chatWithSystem(@RequestBody Map<String, String> request) {
        String message = request.get("message");
        String systemPrompt = request.get("systemPrompt");
        String response = aiService.generateWithSystemPrompt(message, systemPrompt);
        return ResponseEntity.ok(Map.of(
                "message", message,
                "response", response
        ));
    }

    /**
     * Streaming chat endpoint
     */
    @PostMapping(value = "/chat/stream", produces = MediaType.TEXT_EVENT_STREAM_VALUE)
    public Flux<String> streamChat(@RequestBody Map<String, String> request) {
        String message = request.get("message");
        return aiService.streamResponse(message);
    }

    /**
     * Content generation endpoint
     */
    @PostMapping("/generate")
    public ResponseEntity<Map<String, Object>> generateContent(@RequestBody ContentRequest contentRequest) {
        long startTime = System.currentTimeMillis();
        String response = aiService.generateWithOptions(
                contentRequest.prompt(),
                contentRequest.temperature(),
                contentRequest.maxTokens()
        );
        long timeTaken = System.currentTimeMillis() - startTime;

        return ResponseEntity.ok(Map.of(
                "prompt", contentRequest.prompt(),
                "response", response,
                "timeTaken", timeTaken
        ));
    }

    /**
     * Health check endpoint
     */
    @GetMapping("/health")
    public ResponseEntity<Map<String, String>> health() {
        return ResponseEntity.ok(Map.of(
                "status", "UP",
                "service", "Gemini AI"
        ));
    }

    // Record for request body
    public record ContentRequest(
            String prompt,
            Double temperature,
            Integer maxTokens
    ) {}
}

Step 6: Run and Test

# Set environment variable
export GEMINI_API_KEY="your-api-key-here"

# Run the application
./mvnw spring-boot:run

# Test the API
curl -X POST http://localhost:8080/api/ai/chat \
  -H "Content-Type: application/json" \
  -d '{"message": "Explain quantum computing in simple terms"}'

Sample Response:

{
  "message": "Explain quantum computing in simple terms",
  "response": "Quantum computing is a type of computing that uses quantum mechanics principles..."
}

Method 2: Vertex AI Integration (Enterprise)

For enterprise applications requiring advanced features, compliance, and Google Cloud integration, use Vertex AI.

Additional Prerequisites

• Google Cloud Platform account
• Project with Vertex AI API enabled
• gcloud CLI installed

Step 1: Authentication

# Set your project
gcloud config set project YOUR_PROJECT_ID

# Authenticate
gcloud auth application-default login

Step 2: Add Vertex AI Dependencies

<dependency>
    <groupId>org.springframework.ai</groupId>
    <artifactId>spring-ai-starter-model-vertex-ai-gemini</artifactId>
    <version>1.0.0-M6</version>
</dependency>

Step 3: Configure Vertex AI

spring:
  ai:
    model:
      chat: vertexai
    vertex:
      ai:
        gemini:
          project-id: ${GOOGLE_CLOUD_PROJECT_ID}
          location: us-central1
          chat:
            options:
              model: gemini-2.0-flash
              temperature: 0.7
              response-mime-type: text/plain

Advanced Features and Use Cases

1. Function Calling (Tool Use)

Enable Gemini to call your Java methods:

@Service
public class WeatherService {

    @Tool(description = "Get current weather for a location")
    public String getWeather(@ToolParam(description = "City name") String location) {
        // Simulated weather data
        return String.format("The weather in %s is 22°C with clear skies.", location);
    }

    @Tool(description = "Get stock price for a company")
    public String getStockPrice(@ToolParam(description = "Stock ticker symbol") String symbol) {
        // Simulated stock data
        return String.format("%s is currently trading at $150.25", symbol.toUpperCase());
    }
}

Update your service to use tools:

@Service
public class GeminiAiService {

    private final ChatModel chatModel;
    private final WeatherService weatherService;

    public GeminiAiService(ChatModel chatModel, WeatherService weatherService) {
        this.chatModel = chatModel;
        this.weatherService = weatherService;
    }

    public String chatWithTools(String userMessage) {
        return ChatClient.create(chatModel)
                .prompt(userMessage)
                .tools(weatherService)
                .call()
                .content();
    }
}

Test function calling:

curl -X POST http://localhost:8080/api/ai/chat/tools \
  -H "Content-Type: application/json" \
  -d '{"message": "What is the weather in New York?"}'

2. Multimodal Processing (Images + Text)

Gemini can analyze images alongside text:

@Service
public class MultimodalService {

    private final ChatModel chatModel;

    public MultimodalService(ChatModel chatModel) {
        this.chatModel = chatModel;
    }

    public String analyzeImage(byte[] imageData, String prompt) throws IOException {
        var userMessage = new UserMessage(
            prompt,
            List.of(new Media(MimeTypeUtils.IMAGE_PNG, imageData))
        );

        ChatResponse response = chatModel.call(new Prompt(List.of(userMessage)));
        return response.getResult().getOutput().getContent();
    }

    public String analyzePDF(byte[] pdfData, String prompt) throws IOException {
        var userMessage = new UserMessage(
            prompt,
            List.of(new Media(new MimeType("application", "pdf"), pdfData))
        );

        ChatResponse response = chatModel.call(new Prompt(List.of(userMessage)));
        return response.getResult().getOutput().getContent();
    }
}

Controller for file uploads:

@PostMapping(value = "/analyze/image", consumes = MediaType.MULTIPART_FORM_DATA_VALUE)
public ResponseEntity<Map<String, String>> analyzeImage(
        @RequestParam("file") MultipartFile file,
        @RequestParam("prompt") String prompt) throws IOException {

    String analysis = multimodalService.analyzeImage(file.getBytes(), prompt);
    return ResponseEntity.ok(Map.of(
            "analysis", analysis
    ));
}

3. Structured Output (JSON Mode)

Force Gemini to return structured JSON:

@Service
public class StructuredOutputService {

    private final ChatModel chatModel;

    public StructuredOutputService(ChatModel chatModel) {
        this.chatModel = chatModel;
    }

    public String extractEntities(String text) {
        String prompt = """
            Extract entities from the following text and return as JSON:
            - persons: array of names
            - organizations: array of company/org names
            - locations: array of places
            - dates: array of dates mentioned

            Text: %s
            """.formatted(text);

        return ChatClient.create(chatModel)
                .prompt(prompt)
                .options(options -> options
                        .responseMimeType("application/json"))
                .call()
                .content();
    }
}

Example Output:

{
  "persons": ["John Smith", "Jane Doe"],
  "organizations": ["Google", "Microsoft"],
  "locations": ["San Francisco", "New York"],
  "dates": ["March 2026", "Q1 2025"]
}

4. Conversation History (Chat Memory)

Maintain context across multiple messages:

@Service
public class ConversationService {

    private final ChatModel chatModel;

    public ConversationService(ChatModel chatModel) {
        this.chatModel = chatModel;
    }

    public String chatWithHistory(String sessionId, String userMessage) {
        // Retrieve conversation history from storage
        List<Message> history = getConversationHistory(sessionId);

        // Add current user message
        history.add(new UserMessage(userMessage));

        // Get AI response
        ChatResponse response = chatModel.call(new Prompt(history));
        String aiResponse = response.getResult().getOutput().getContent();

        // Save AI response to history
        history.add(new AssistantMessage(aiResponse));
        saveConversationHistory(sessionId, history);

        return aiResponse;
    }

    private List<Message> getConversationHistory(String sessionId) {
        // Implement based on your storage (Redis, Database, etc.)
        return new ArrayList<>();
    }

    private void saveConversationHistory(String sessionId, List<Message> history) {
        // Implement storage logic
    }
}

Configuration Options Reference

Model Options

Chat Configuration Properties

spring:
  ai:
    google:
      genai:
        chat:
          options:
            model: gemini-2.0-flash              # Model to use
            temperature: 0.7                      # Creativity (0.0-1.0)
            max-output-tokens: 8192              # Max tokens in response
            top-k: 40                             # Sampling diversity
            top-p: 0.95                           # Cumulative probability
            candidate-count: 1                    # Number of responses
            response-mime-type: text/plain        # Output format

Parameter Explanations

Temperature (0.0 - 1.0)

• 0.0-0.3: Factual, deterministic responses
• 0.4-0.7: Balanced creativity and accuracy
• 0.8-1.0: Highly creative, potentially unpredictable

Max Output Tokens

• Controls maximum response length
• 1024: Short answers
• 4096: Medium responses
• 8192+: Long-form content

Top-K and Top-P

• Control sampling diversity
• Lower values = more focused
• Higher values = more diverse

Best Practices

1. Security

// Never hardcode API keys
@Configuration
public class SecurityConfig {

    @Bean
    public String geminiApiKey(@Value("${GEMINI_API_KEY}") String apiKey) {
        return apiKey;
    }
}

// Use environment variables
// application.properties
spring.ai.google.genai.api-key=${GEMINI_API_KEY}

2. Error Handling

@Service
public class ResilientAiService {

    private final ChatModel chatModel;

    public ResilientAiService(ChatModel chatModel) {
        this.chatModel = chatModel;
    }

    @Retryable(
        retryFor = ApiException.class,
        maxAttempts = 3,
        backoff = @Backoff(delay = 1000)
    )
    public String generateWithRetry(String prompt) {
        try {
            return aiService.generateResponse(prompt);
        } catch (Exception e) {
            log.error("AI generation failed", e);
            throw new ApiException("Failed to generate response", e);
        }
    }

    @CircuitBreaker(name = "gemini", fallbackMethod = "fallbackResponse")
    public String generateWithCircuitBreaker(String prompt) {
        return aiService.generateResponse(prompt);
    }

    public String fallbackResponse(String prompt, Exception e) {
        return "AI service temporarily unavailable. Please try again later.";
    }
}

3. Rate Limiting

@RestController
@RequestMapping("/api/ai")
public class RateLimitedController {

    private final GeminiAiService aiService;

    @RateLimiter(name = "gemini", fallbackMethod = "rateLimitFallback")
    @PostMapping("/chat")
    public ResponseEntity<Map<String, String>> chat(@RequestBody Map<String, String> request) {
        // Implementation
    }

    public ResponseEntity<Map<String, String>> rateLimitFallback(Map<String, String> request) {
        return ResponseEntity
            .status(429)
            .body(Map.of("error", "Rate limit exceeded"));
    }
}

4. Logging and Monitoring

@Configuration
public class ObservabilityConfig {

    @Bean
    public ObservationRegistry observationRegistry() {
        return ObservationRegistry.create();
    }

    @Bean
    public AiObservationConvention aiObservationConvention() {
        return new DefaultAiObservationConvention();
    }
}

// Enable in application.properties
management.endpoints.web.exposure.include=health,metrics,prometheus
management.metrics.enable.spring.ai=true

Common Use Cases

1. Customer Support Chatbot

@Service
public class SupportBotService {

    private static final String SYSTEM_PROMPT = """
        You are a helpful customer support assistant for TechCorp.
        - Be polite and professional
        - Provide accurate information
        - Escalate complex issues to human agents
        - Use knowledge base when available
        """;

    public String handleSupportQuery(String query) {
        return aiService.generateWithSystemPrompt(query, SYSTEM_PROMPT);
    }
}

2. Content Generation API

@PostMapping("/generate/blog-post")
public ResponseEntity<BlogPostResponse> generateBlogPost(@RequestBody BlogPostRequest request) {
    String prompt = """
        Write a blog post about %s.
        Target audience: %s
        Tone: %s
        Length: approximately %d words
        Include: introduction, main points, conclusion
        """.formatted(
            request.topic(),
            request.audience(),
            request.tone(),
            request.wordCount()
        );

    String content = aiService.generateWithOptions(prompt, 0.7, 4096);

    return ResponseEntity.ok(new BlogPostResponse(content));
}

3. Code Review Assistant

@Service
public class CodeReviewService {

    private static final String CODE_REVIEW_PROMPT = """
        Review the following code for:
        1. Bugs and potential issues
        2. Code quality and best practices
        3. Performance optimizations
        4. Security vulnerabilities

        Provide specific, actionable feedback.

        Code:
        %s
        """;

    public String reviewCode(String code) {
        return aiService.generateResponse(CODE_REVIEW_PROMPT.formatted(code));
    }
}

4. Data Extraction Pipeline

@Service
public class DataExtractionService {

    public Map<String, Object> extractStructuredData(String text, String schema) {
        String prompt = """
            Extract data from the text following this JSON schema:
            %s

            Text:
            %s

            Return only valid JSON.
            """.formatted(schema, text);

        String jsonResult = ChatClient.create(chatModel)
                .prompt(prompt)
                .options(opts -> opts.responseMimeType("application/json"))
                .call()
                .content();

        return objectMapper.readValue(jsonResult, Map.class);
    }
}

Troubleshooting

Common Issues and Solutions

1. API Key Authentication Errors

Error: 401 Unauthorized - Invalid API key

Solution:

• Verify API key is correct
• Ensure environment variable is set: export GEMINI_API_KEY=your-key
• Check for extra spaces or quotes

2. Rate Limiting

Error: 429 Too Many Requests

Solution:

• Implement exponential backoff
• Use circuit breaker pattern
• Consider upgrading to higher tier

3. Model Not Found

Error: 404 Model not found

Solution:

• Verify model name is correct
• Check model availability in your region
• Update to latest Spring AI version

4. Timeout Errors

Error: Read timed out

Solution:

spring:
  ai:
    google:
      genai:
        connection-timeout: 30s
        read-timeout: 60s

Performance Optimization

1. Connection Pooling

@Configuration
public class HttpClientConfig {

    @Bean
    public HttpClient httpClient() {
        return HttpClient.newBuilder()
                .connectTimeout(Duration.ofSeconds(30))
                .build();
    }
}

2. Response Caching

@Service
public class CachedAiService {

    @Cacheable(value = "ai-responses", key = "#prompt")
    public String generateWithCache(String prompt) {
        return aiService.generateResponse(prompt);
    }
}

// Enable caching
@EnableCaching
@SpringBootApplication
public class Application { }

3. Async Processing

@Service
public class AsyncAiService {

    @Async
    public CompletableFuture<String> generateAsync(String prompt) {
        return CompletableFuture.completedFuture(
            aiService.generateResponse(prompt)
        );
    }
}

// Enable async
@EnableAsync
@SpringBootApplication
public class Application { }

Testing Your Integration

Unit Tests

@SpringBootTest
class GeminiAiServiceTest {

    @Autowired
    private GeminiAiService aiService;

    @Test
    void shouldGenerateResponse() {
        String response = aiService.generateResponse("Say hello");

        assertThat(response).isNotNull();
        assertThat(response).isNotBlank();
    }

    @Test
    void shouldHandleSystemPrompt() {
        String systemPrompt = "You are a helpful assistant.";
        String response = aiService.generateWithSystemPrompt(
            "What is 2+2?",
            systemPrompt
        );

        assertThat(response).contains("4");
    }
}

Integration Tests

@SpringBootTest(webEnvironment = SpringBootTest.WebEnvironment.RANDOM_PORT)
@AutoConfigureMockMvc
class GeminiAiControllerIntegrationTest {

    @Autowired
    private MockMvc mockMvc;

    @Test
    void shouldReturnChatResponse() throws Exception {
        String requestBody = "{\"message\": \"Hello\"}";

        mockMvc.perform(post("/api/ai/chat")
                .contentType(MediaType.APPLICATION_JSON)
                .content(requestBody))
                .andExpect(status().isOk())
                .andExpect(jsonPath("$.response").exists());
    }
}

Deployment Considerations

Docker Configuration

FROM eclipse-temurin:21-jdk-alpine

WORKDIR /app

COPY target/gemini-app.jar app.jar

ENV GEMINI_API_KEY=""

EXPOSE 8080

ENTRYPOINT ["java", "-jar", "app.jar"]

Kubernetes Secrets

apiVersion: v1
kind: Secret
metadata:
  name: gemini-api-key
type: Opaque
stringData:
  GEMINI_API_KEY: your-api-key-here
---
apiVersion: apps/v1
kind: Deployment
metadata:
  name: gemini-app
spec:
  template:
    spec:
      containers:
      - name: gemini
        image: gemini-app:latest
        env:
        - name: GEMINI_API_KEY
          valueFrom:
            secretKeyRef:
              name: gemini-api-key
              key: GEMINI_API_KEY

Cost Management

Gemini API pricing varies by model and usage. Monitor your consumption:

@Service
public class UsageTrackingService {

    private final AtomicInteger tokenCounter = new AtomicInteger(0);

    public String trackUsage(String prompt, String response) {
        int inputTokens = estimateTokens(prompt);
        int outputTokens = estimateTokens(response);

        tokenCounter.addAndGet(inputTokens + outputTokens);

        log.info("Total tokens used: {}", tokenCounter.get());

        return response;
    }

    private int estimateTokens(String text) {
        // Rough estimate: 1 token ≈ 4 characters
        return text.length() / 4;
    }
}

Resources and Next Steps

Official Documentation

• Spring AI Reference
• Google AI Studio
• Gemini API Documentation

Recommended Libraries

Next Steps

1. Start Small - Begin with simple chat endpoints
2. Add Monitoring - Implement logging and metrics
3. Handle Errors - Add retry and circuit breaker patterns
4. Secure APIs - Implement authentication and rate limiting
5. Scale Gradually - Optimize performance as usage grows

Conclusion

Integrating Gemini AI into Spring Boot applications opens up endless possibilities for building intelligent, AI-powered features. With Spring AI’s abstraction, you can focus on business logic rather than API complexities.

Key Takeaways:

• ✅ Direct Gemini API integration is simple and doesn’t require Vertex AI
• ✅ Spring AI provides clean abstractions for chat, streaming, and tools
• ✅ Multimodal capabilities enable image and PDF processing
• ✅ Function calling allows AI to interact with your Java methods
• ✅ Enterprise-grade features include retry, caching, and monitoring

Start building your AI-powered Spring Boot application today. The combination of Spring Boot’s reliability and Gemini’s intelligence is a powerful foundation for modern applications.

Pro Tip: Always implement proper error handling, rate limiting, and monitoring when deploying AI features to production. Start with the free tier to test and iterate before scaling.

Happy coding! 🚀

Frequently Asked Questions (FAQ)

Q: Do I need a Google Cloud account to use Gemini API?

A: No! For direct API integration, you only need a free Google AI Studio account. Vertex AI requires Google Cloud but offers additional enterprise features.

Q: What’s the cost of using Gemini API?

A: Google offers a free tier with generous limits. Paid tiers start at very competitive rates. Check Google AI pricing for current rates.

Q: Can I use Gemini with existing Spring Boot applications?

A: Absolutely! Just add the Spring AI dependency and configure your API key. The integration is non-invasive and works alongside existing code.

Q: How do I handle sensitive data with AI APIs?

A: Never send PII, secrets, or sensitive data to AI models. Implement data filtering and use on-premises solutions for sensitive workloads.

Q: Is Spring AI production-ready?

A: Yes, Spring AI is being actively developed with enterprise adoption. Use milestone releases for latest features or wait for GA release for maximum stability.

Q: Can I use multiple AI models simultaneously?

A: Yes! Spring AI supports multiple model providers. You can configure different ChatModel beans for different use cases.