Complete Spring AI Stack: Mapping the 2026 Java AI Ecosystem

Many engineers face overwhelming choices when building AI projects in Java. The author creates a comprehensive map of the 2026 Java AI ecosystem, breaking it into five layers and providing concrete guidance for each.

Layer 1 – AI Application Framework

The Java AI landscape currently offers three main frameworks:

Spring AI – part of the Spring ecosystem, production‑ready (1.0 GA), ideal for enterprise Java projects.

LangChain4j – an independent framework with an active community, suitable for projects that do not want to adopt Spring.

Semantic Kernel – a Microsoft‑backed library with rich features, best for Microsoft‑centric stacks.

Recommendation: New projects should start with Spring AI because it reuses Spring Boot configuration, AOP, and testing facilities, resulting in the lowest learning curve.

// Spring AI – typical Spring style, familiar to developers
@Service
public class AiService {
    @Autowired
    private ChatClient chatClient;
    @Autowired
    private VectorStore vectorStore;
    @Value("${app.ai.system-prompt}")
    private String systemPrompt;
    // No extra learning cost
    public String answer(String question) {
        return chatClient.prompt()
            .system(systemPrompt)
            .user(question)
            .call()
            .content();
    }
}

Layer 2 – LLM Model Selection

The author compares several models with 2026 reference prices (per 1 M tokens):

GPT‑4o – $5 input / $15 output – high‑quality general use.

GPT‑4o‑mini – $0.15 / $0.60 – cost‑focused.

Claude 3.5 Sonnet – $3 / $15 – excels at long‑document analysis.

DeepSeek‑V3 – ~$0.27 / ~$1.10 – Chinese‑optimized, very cost‑effective.

Ollama (local) – server cost only – suitable for high privacy requirements.

Spring AI abstracts the model interface, so switching models only requires a YAML change, e.g.:

# Switch to DeepSeek via OpenAI‑compatible interface
spring:
  ai:
    openai:
      api-key: ${DEEPSEEK_API_KEY}
      base-url: https://api.deepseek.com
      chat:
        options:
          model: deepseek-chat
# Switch to Tongyi Qianwen
spring:
  ai:
    dashscope:
      api-key: ${DASHSCOPE_API_KEY}
      chat:
        options:
          model: qwen-max

Layer 3 – Embedding Model Selection

Key embedding options and their characteristics:

text‑embedding‑3‑large – 3072 dimensions, multilingual, $0.13 per 1 M tokens – high quality, English‑focused.

text‑embedding‑3‑small – 1536 dimensions, multilingual, $0.02 per 1 M tokens – cost‑sensitive.

m3e‑large (open‑source Chinese) – 1024 dimensions, free for private deployment – ideal for Chinese scenarios.

mxbai‑embed‑large (Ollama) – 1024 dimensions, free for private deployment – suitable for on‑premise use.

@Configuration
public class EmbeddingConfig {
    @Bean
    @ConditionalOnProperty(name = "app.embedding.provider", havingValue = "openai")
    public EmbeddingModel openAiEmbedding(OpenAiEmbeddingModel model) {
        return model;
    }

    @Bean
    @ConditionalOnProperty(name = "app.embedding.provider", havingValue = "ollama")
    public EmbeddingModel ollamaEmbedding() {
        return OllamaEmbeddingModel.builder()
            .ollamaApi(OllamaApi.builder().baseUrl("http://localhost:11434").build())
            .defaultOptions(OllamaOptions.builder().model("mxbai-embed-large").build())
            .build();
    }
}

Layer 4 – Vector Database Selection

Comparison of popular vector stores:

PgVector – ~5 M vectors, low operational complexity (reuses PostgreSQL), suitable for small‑to‑medium projects.

Qdrant – hundreds of millions of vectors, medium complexity, recommended for medium‑large projects.

Milvus – tens of billions of vectors, high complexity, suited for massive scale.

Weaviate – hundreds of millions of vectors, enterprise‑grade features.

Redis – ~10 M vectors, low complexity, good for real‑time scenarios.

Layer 5 – Agentic Toolchain

Agent development is highlighted as a core 2026 skill. Spring AI’s @Tool annotation turns Java methods into agent tools:

@Service
public class BusinessTools {
    @Tool(description = "Query customer order status")
    public OrderStatus getOrderStatus(@ToolParam(description = "Order ID") String orderId) {
        return orderService.getStatus(orderId);
    }

    @Tool(description = "Send email to customer")
    public boolean sendEmail(@ToolParam(description = "Recipient email") String email,
                            @ToolParam(description = "Subject") String subject,
                            @ToolParam(description = "Content") String content) {
        return emailService.send(email, subject, content);
    }

    @Tool(description = "Query product stock")
    public int getStock(@ToolParam(description = "Product SKU") String sku) {
        return inventoryService.getStock(sku);
    }
}

@Configuration
public class AgentConfig {
    @Bean
    public ChatClient agentChatClient(ChatClient.Builder builder, BusinessTools tools) {
        return builder
            .defaultSystem("You are a customer‑service assistant that can query orders, send emails, and check inventory.")
            .defaultTools(tools)
            .build();
    }
}

Complete Stack Recommendations

Three concrete stack combos are provided for different scales and scenarios:

Solution A – Quick Start (Startup/POC)

Framework: Spring AI 1.0
LLM: GPT‑4o‑mini (low cost)
Embedding: text‑embedding‑3‑small
Vector Store: PgVector (reuse existing PostgreSQL)
Cache: Redis
Monitoring: Spring Actuator + Prometheus
Estimated monthly cost: 10 k–50 k CNY

Solution B – Enterprise Standard (Mid‑size)

Framework: Spring AI 1.0
LLM: GPT‑4o + DeepSeek‑V3 (dual‑model routing)
Embedding: text‑embedding‑3‑large (quality first)
Vector Store: Qdrant (100 k+ vectors)
Full‑text Search: Elasticsearch (hybrid search)
Cache: Redis Cluster
Message Queue: Kafka (asynchronous document processing)
Monitoring: Prometheus + Grafana + Jaeger
Estimated monthly cost: 50 k–200 k CNY

Solution C – Private Deployment (Finance/Healthcare/Government)

Framework: Spring AI 1.0
LLM: DeepSeek‑R1‑32B (Ollama private deployment)
Embedding: mxbai‑embed‑large (Ollama)
Vector Store: Qdrant (Docker deployment)
Full‑text Search: OpenSearch (open‑source ES)
Hardware: ≥4 × NVIDIA A100/H100 GPUs
One‑time hardware cost: 1‑3 M CNY

Key Configuration Template (YAML)

# Complete Spring AI production configuration template
spring:
  ai:
    openai:
      api-key: ${OPENAI_API_KEY}
      chat:
        options:
          model: gpt-4o
          temperature: 0.1   # lower randomness in production
          max-tokens: 2048
          timeout: 30s
    embedding:
      options:
        model: text-embedding-3-large
    vectorstore:
      pgvector:
        dimensions: 3072
        distance-type: cosine_distance
        index-type: ivfflat
    observation:
      include-prompt: ${AI_LOG_PROMPTS:false}
      include-completion: false
    datasource:
      url: jdbc:postgresql://${DB_HOST}:5432/knowledgebase
      hikari:
        maximum-pool-size: 20
        minimum-idle: 5
    app:
      ai:
        max-context-tokens: 6000
        similarity-threshold: 0.65
        top-k: 5
        cache-ttl-minutes: 60
        embedding-batch-size: 20
        model-fallback:
          enabled: true
          primary: gpt-4o
          fallback: gpt-4o-mini   # downgrade when primary is throttled

Decision Principles for Technology Selection

Principle 1 – Choose the smallest sufficient solution. If PgVector meets your scale, avoid introducing Milvus to keep operational complexity low.

Principle 2 – Test models at each layer. Do not rely solely on benchmark rankings; validate with your own business data because performance varies by use case.

Principle 3 – Leverage Spring AI’s abstraction. Use interfaces rather than locking into a specific implementation; today’s best choice may change in six months.

Principle 4 – Prioritize observability over features. In production, invisible problems are the most dangerous; monitoring, logging, and tracing must be in place from the first release.

With this roadmap, architects and technical leaders can confidently select components, configure them, and start building AI‑enabled Java applications without waiting for exhaustive research.