How to Run and Scale AI Java Applications in Production: An Overview for Developers with no Machine Learning Expertise

Organizations are increasingly interested in adopting artificial intelligence (AI) and generative AI (GenAI) to improve operations and offer next-generation services to customers and end users. The demand for AI-powered solutions means that developers are tasked with incorporating the technology in the applications they develop and maintain.

While creating a proof of concept (POC) or a minimum viable product (MVP) in a development or sandbox environment for an application that leverages AI is relatively straightforward, enterprise Java developers typically face two key challenges. Firstly, they may not be aware that Java applications can easily incorporate AI. Secondly, they can encounter issues managing and scaling reliable, responsive Java services that feature AI functions.

The challenge of delivering applications that successfully integrate AI isn’t only theoretical. A new study from the Massachusetts Institute of Technology (MIT)’s Networked Agents and Decentralized AI (NANDA) initiative, The GenAI Divide: State of AI in Business 2025, found that 95% of corporate generative AI pilots stall before they ever scale. In this blog post, we look at how backend developers can develop, deploy and run smart, production-ready enterprise Java applications that feature AI.

How to Integrate AI in Java Apps as a Non-AI Expert

We cannot be experts in everything and, in most cases, it’s not feasible to create large language models (LLMs) or small language models (SLMs), which require high volumes of high-quality data to be effective. Luckily, you don’t need neither these datasets nor to create LLMs/SLMs to add useful AI features to your Java application. In fact, developers don’t need to create AI models from scratch, but simply integrate existing solutions, selecting the one that is best suited to address the specific needs of a given application.

The most common AI projects, such as searches that understands meaning, text summarization, code suggestions, basic image classification or chat helpers, can be delivered by calling hosted models, e.g. through HTTP/RESTful APIs, or using lightweight Java inference libraries and SDKs.

AI Model Integration Strategies

How your AI model is integrated within your Java application has significant impact on performance, scalability, security and maintenance. Therefore, it is important to select the option that can best address your requirements.

For example, calling REST APIs with a HTTPS client is best when you want no local infrastructure or resources involved and are comfortable with sending data out-of-house, over the internet. Conversely, Java AI Libraries are ideal if you need to run models locally for privacy/security, speed, latency and/or cost, want tighter code integration and are okay with maintaining infrastructure.

In addition, open-weight models, such as gpt-oss-20b and gpt-oss-120b (both available under Apache 2.0), are increasingly popular among enterprises looking for greater control over their AI stack or to avoid vendor lock-in.

Key Dev Tools for AI Integration

Helping Java developers looking to incorporate AI into their applications is a rapidly growing body of tools specifically designed to support these professionals develop applications that feature AI features, such as LangChain4J.

Check these resources to learn more about:

• AI dev tools for enterprise Java developers
• How to integrate AI agents in your applications

How to Make AI-Rich Java Apps Operational

You successfully integrated AI into your application in a development/testing environment and are now looking to move to production. Before you blindly follow the steps and best practices used for traditional software, you need to bear in mind that extra considerations are needed. Going back to the findings of the MIT NANDA’s report, if you treat AI like a simple “feature add-on” without thinking through scalability, integration and operational concerns, your project risks joining the 95% that never make it past the pilot stage.

In particular, four elements you want to consider are:

• Model performance and output quality
• Application scalability
• Resource utilization and cost
• Application visibility

Model Performance and AI Output Quality

AI models are ‘black boxes’ that are non-deterministic and not static. This means they provide probabilistic outputs, i.e. when given the same inputs, they can produce different responses. Even if your Java application integrates a high-performing model at launch, that model’s effectiveness can change considerably as model updates and new versions, user behavior, business data or market conditions evolve.

As a result, a system can appear to be fully functional while, in reality, its usefulness is steadily eroding. More precisely, the service responds to requests quickly (low latency), processes them at scale (high throughput) and raises no obvious errors (no crashes or anomalies). By all conventional engineering standards, the system is healthy. Yet, hidden beneath this apparent stability, the model at the heart of the system can be quietly failing, as the the AI runs but it doesn’t work well, with its predictions being increasingly wrong.

To avoid this and detect AI output issues early, regularly testing and reviewing output quality is essential. You’ll need to track accuracy, detect hallucinations and bias as well as identify model drift, i.e. the gradual decline in predictive power over time. This proactive approach prevents user frustration and ensures that your AI features continue to deliver reliable value in production.

Currently, there are a number of solutions to help you evaluate model performance and its outputs in production environments as well as promptly respond to such issues, including Java-based frameworks such as MOA (Massive On-line Analysis).

Application Scalability

The computational complexity of AI tasks, large data handling and concurrent processing demands make AI workloads resource-intensive, often requiring more CPU, GPU or memory than standard services. This means that app scalability considerations should not solely focus on how to handle more user requests, but also how to manage AI inference efficiently.

When it comes to architectural strategies, microservices and containers are ideal for incorporating intelligent capabilities. Most new applications are built as independent, modular services, which makes adding advanced functionality relatively straightforward, as AI-related services can scale independently without forcing the entire application to do the same. However, older monolithic workloads typically require modernization before they can support AI integrations.

Event-driven architectures complement this by decoupling services: instead of having your main application wait for an AI model to finish computing, you can push tasks into a queue and let worker services process them asynchronously. In addition, lazy loading and caching frequently requested inferences can prevent unnecessary computation.

Resource Utilization and Cost

Integrating AI functions often increases compute and storage demands, which can quickly escalate operating costs if left unchecked. Efficient resource utilization is therefore a critical design consideration. Beyond traditional optimization tactics, evaluate deployment strategies: is a cloud-native setup with managed services more cost-effective, or does a hybrid/on-prem solution better suit your business constraints? Balancing performance and budget will help you make sure your AI-rich Java application is sustainable in the long run.

Application Visibility

AI-driven applications introduce a new layer of complexity that standard observability alone cannot capture. The difficulty in tracing how inputs shape outputs leads to a lack of interpretability of AI responses, which can cause issues for conventional observability tools. As a result, troubleshooting, debugging and performance monitoring are significantly more complex in Gen AI systems. While logging, tracing and metrics remain essential, you also need visibility into model-level behaviors. We discussed above the importance of monitoring response quality and model drift. In addition to these, a number of parameters associated with token use can be extremely beneficial

Token Utilization in AI

A token is a unit of language that an AI model can understand, e.g. a word. The number of tokens that a model processes to effectively process an input or produce an output influences the cost and performance of an AI-integrated application. More precisely, higher token consumptions typically lead to higher operational expenses and response latency.

Monitoring metrics such as token efficiency, usage patterns, current rates and costs is key for effective token usage tracking and, ultimately, application performance. For enterprises running production-scale workloads, token monitoring is a valuable cost-control strategy. Tracking token efficiency, usage patterns, current rates and costs makes it easier to forecast spend, set guardrails (like maximum token usage per request) as well as fine-tune prompts to reduce unnecessary overhead.

AI-Oriented Observability

In production, visibility is key, hence it is worth considering the extension of existing observability stack with AI-specific dashboards that provide insights into how models are performing in real-world conditions, e.g. token utilization. Enhanced visibility allows your engineering teams to troubleshoot quickly, detect emerging issues and continuously fine-tune both the model and the surrounding infrastructure.

Final Thoughts on How to Successfully Run AI in Production

Adding AI to an application can be hugely valuable. However, AI isn’t set-and-forget. Unlike traditional software, machine learning models are not static instructions but dynamic approximations of patterns in data. This substantial difference to traditional applications means that one of the biggest hurdles in AI projects is bridging the gap between coding and Gen AI expertise.

Developers excel at building robust applications but may lack deep AI knowledge. Conversely, AI specialists are highly skilled in model research and experimentation, not on building scalable production systems. As a result, models that perform well in a controlled environment often struggle when deployed at scale.

Since models evolve, applications with AI integrations change too. Java teams should therefore treat their services like living systems that demand continuous monitoring, AI-specific observability tools, regular testing, iteration and optimization. Long-term success depends on how well a modern app is observed, maintained and adapted. The organizations that recognize this will avoid key issues and fully reap the benefits of AI in production.