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Choose a Spring AI version that matches your application
Start with compatibility, not a dependency copied from an example. The Spring AI reference lists stable releases and current setup guidance; check the dependency and compatibility details for the exact release you choose. Spring AI 2.0 GA was announced on June 12, 2026, with a baseline designed for Spring Boot 4.0/4.1 and Spring Framework 7.0, according to the Spring AI 2.0 GA announcement. If your application is on Spring Boot 3, use a compatible Spring AI 1.x release and verify its requirements before upgrading; do not assume the 2.0 line is a drop-in fit.
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Spring AI provides provider starters and a common model-facing API, including ChatClient, tool calling, advisors, and vector-store integrations. The common API can reduce coupling, but it does not erase provider-specific capabilities or behavior. Choose a provider based on the model capabilities you need, availability and latency where you deploy, data handling and retention terms, authentication and network constraints, expected usage cost, and how much provider-specific functionality you intend to use. The Spring AI reference lists integrations, but does not establish comparative prices, latency, regional availability, or service terms; verify those with the provider.
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Put AI behavior at an existing application boundary—such as a service used by a controller or a job—rather than spreading provider calls through controllers and business logic. The caller should receive a type your application owns, not a provider SDK response or an unstructured string it must interpret.
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@Service
class SummaryService {
private final ChatClient chatClient;
SummaryService(ChatClient.Builder builder) {
this.chatClient = builder.build();
}
Summary summarize(String text) {
return chatClient.prompt()
.user(text)
.call()
.entity(Summary.class);
}
}
record Summary(String text) {}This illustrative shape uses Spring AI’s ChatClient and maps the response to a Java record. The ChatClient documentation describes its fluent API as idiomatic for Spring developers, similar in style to WebClient or RestClient. Check the API, dependency coordinates, provider starter, and configuration against the Spring AI version you select; this example is not a tested, complete application.
Keep provider credentials out of source control. Configure them through the deployment’s secret-management mechanism or environment-specific configuration, following the selected provider starter’s current property names. Spring AI’s getting-started guidance covers creating a Spring Boot web app, adding a provider starter, configuring its API key, and making a ChatClient call; its property examples are configuration guidance, not a reason to commit a real key.
Return structured data, then validate it
.entity(Summary.class) asks Spring AI to map model output to a declared type. That gives application code named fields instead of requiring it to parse free-form prose. The structured output documentation describes schema generation and deserialization for typed output.
A successful conversion does not mean the result is true, safe, or valid for your business rules. Treat model output as untrusted external input:
- Check required fields, ranges, allowed values, and cross-field invariants before using the result.
- Handle malformed output, deserialization errors, provider errors, and timeouts as ordinary failure paths.
- Decide what the caller should receive on failure: a controlled application error, a retry where appropriate, or a non-AI fallback. Do not silently turn an invalid answer into a valid business decision.
Spring AI’s 2.0 GA announcement notes that provider-native structured output can still produce nonconforming JSON and describes validation and self-correction support. Those mechanisms can help, but application-level validation remains necessary.
Keep optional patterns optional
Advisors compose request and response behavior, including patterns such as memory and retrieval. They are useful when the feature needs those behaviors, but are not prerequisites for a single model call. The Advisors documentation explains ordering and recommends configuring defaults at builder time. Add an advisor when it solves a specific application need; avoid introducing a chain of abstractions merely because it is available.
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Make the external dependency visible to operations
A service boundary limits code changes, not operational impact. A model call can add latency, fail, consume tokens, or return a different answer for similar inputs. Spring AI documents metrics and tracing for AI operations, including ChatClient calls and streams, with tracing context propagation and token-usage metrics. Use the available signals to monitor latency, errors, model or provider selection, and consumption. See the Spring AI observability reference for the supported signals and configuration.
Prompt and completion content is not exported by default, and prompt/completion logging defaults to false because those payloads can be large and sensitive. Keep content out of routine telemetry unless you have a clear need and have addressed privacy, access, and retention. Start by observing operational metadata rather than collecting user prompts.
Integrate without changing every caller
- Check the version fit. Match the Spring AI release to the application’s Spring Boot and Framework versions using the current reference.
- Add one provider integration. Include its starter, configure credentials outside source control, and keep provider configuration in the application’s configuration layer.
- Define an application-owned contract. Give callers a method and return type that express the feature, not the provider’s wire format.
- Implement and validate the model path. Map output to a type, enforce business invariants, and define what happens for invalid output or provider failure.
- Instrument before broad rollout. Track latency, errors, model selection, and token use; avoid logging prompts and completions by default.
That design lets the rest of the application depend on a stable seam while the AI implementation remains replaceable. It does not make the model deterministic or the remote service invisible—and a sound integration makes those limitations observable rather than hiding them.
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