Scaling GenAI to Production: Strategies for Enterprise-Grade AI Deployment

If you’ve tried to learn about deploying GenAI systems in production, you’ve probably noticed a frustrating pattern. While there are countless tutorials showing how to run language models on a laptop or create quick prototypes, there’s very little guidance on building production-ready systems that can serve an entire organisation.

The internet is filled with Langchain-based chatbots examples, but what happens when you need to scale that to handle thousands of users, ensure consistent performance, and maintain security? While these resources are valuable for learning, they rarely address the challenges of running GenAI in production:

• How do you handle increasing user loads?
• What’s the right way to monitor model performance?
• How do you manage costs at scale?
• How do you implement proper security and governance?

In this article, we’ll explore what is required at a high level and why each of these items is important. Subsequent articles will explore each topic in more depth.

Challenges of Scaling AI Infrastructure

Scaling GenAI systems beyond proof-of-concept requires building solutions that are sustainable, secure, and strategically aligned with your business objectives. According to a survey by Stack Overflow only about 30% of GenAI projects make it into production, highlighting a significant gap between innovation and implementation. There are several challenges that prevent companies from effectively deploying GenAI:

• Resource Constraints: Handling increased user demand and scaling infrastructure to meet growing workloads can be a major challenge, especially for businesses with limited resources.
• Performance Monitoring: Ensuring consistent model performance and identifying potential issues or bottlenecks in a production environment requires robust monitoring and observability capabilities.
• Cost Management: As usage and data volumes grow, maintaining cost-effectiveness and optimising cloud resource utilisation becomes critical to the long-term viability of GenAI deployments.
• Security and Governance: Implementing proper security controls, data privacy measures, and governance frameworks to manage the risks associated with generative AI is essential for enterprise adoption.

Scaling Strategies

To handle increased demand, organisations need effective scaling strategies. While traditional scaling approaches like horizontal and vertical scaling are well-established, new challenges rise in AI-driven environments.

Organizations must account for limited GPUs, unpredictable traffic, and multiple cloud providers, while still keeping costs in check and delivering prompt responses. Key decisions should be driven by following questions:

• How can we optimize GPU usage in an environment with limited resources?

GPUs are in high demand, so it’s important to make sure they’re reserved for tasks with the greatest computational or latency needs. You should consider moving simpler or less frequent processes to CPUs or specialized accelerators that are more readily available. Consider moving “easy” LLM tasks to smaller or more specialised models. Consider distilling higher-performing models into lower cost models.

• How can we scale efficiently across smaller, less dominant cloud providers?

Moreover, relying on just one top-tier cloud provider might lock you into scarce or expensive GPU resources. We’ve worked with companies that are leveraging multiple GPU providers to avoid vendor lock-in and mitigate resource shortages. Leveraging multiple clouds can help find available GPUs more easily. Keep in mind that this approach introduces logistical challenges, such as managing workloads and synchronizing data across different platforms. Automated orchestration tools and containerization are essential to handle this complexity.

• How can we minimize idle compute costs while still maintaining responsiveness to user requests?

For workloads that aren’t continuous or have highly variable traffic, scaling to zero when idle can lead to significant cost savings. However, be aware of the time it takes for these instances to “warm up” before they can serve new requests. Predicting demand and using caching or queue-based systems can help mitigate cold-start issues.

• How does batch and real-time inference work for LLM workloads?

Some applications, like customer service chatbots or real-time fraud detection, can’t tolerate long delays. Here, low latency is critical, so you might need a more robust always-on infrastructure that ensures immediate response times.

Other tasks—such as periodic content moderation or report generation—don’t need immediate turnaround. Grouping them into batches can cut costs by letting you spin up resources only during designated time windows. This approach also allows you to improve GPU utilisation by processing multiple tasks at once.

Many businesses find a mix of real-time and batch processing works best. For example, frequently accessed queries or common scenarios can be handled with a precomputed lookup or a smaller real-time model, while infrequent requests trigger a more resource-intensive inference pipeline. This way, you maintain speed where it matters while keeping costs down for less critical operations.

Implementing Effective Guardrails

Another important component to consider is the integration of effective guardrails. As usage increases, the risk of generating inappropriate or harmful content increases. Robust filtering mechanisms can prevent such outputs, and can be implemented at various stages:

• The simplest and surprisingly effective approach is to use input validation to assess user inputs to detect and block harmful or malicious content before processing. Keyword blacklists set a consistent baseline for acceptable content.
• Embedding validation can be used to detect if the user input is similar to known harmful content.
• Output validation can be used to detect and block harmful or malicious content after processing.
• External models can be trained or fine-tuned to detect harmful content.
• And many more… It largely depends on your use case and the sensitivity of the data you are processing.

Tools like Guardrails AI offer packaged solutions to define and enforce such constraints, enhancing the reliability of your systems.

Securing data pipelines becomes more complex at scale, so protecting data during ingestion, processing, and storage is also crucial. This involves encrypting data, using secure protocols, and regularly auditing the data flow.

Authentication and authorization are critical components of guardrails. With more users and systems interacting with AI models, controlling access prevents unauthorised use.

Monitoring and Evaluating Systems

Continuous monitoring and evaluation are vital for maintaining the performance and reliability of AI systems in production. Real-time monitoring allows organisations to track model outputs and performance metrics, detecting any anomalies or deviations from expected behaviour.

Real-time monitoring also involves tracking key indicators like response time, accuracy, and resource utilisation. By detecting issues early, companies can address them before they impact users. This is especially important in large-scale deployments where small problems can quickly escalate.

Read more about LLM monitoring in one of our previous articles and review more traditional ML monitoring tools here.

Evaluating systems goes beyond technical metrics. It includes assessing how well the AI system meets business objectives and user needs. Regular evaluations help identify areas for improvement, ensuring the system remains aligned with organizational goals.

By adopting MLOps (or LLMOps) practices, developers can iterate faster within business-set constraints. Automated pipelines for testing, deployment, and monitoring enable rapid development cycles while maintaining control and compliance. This helps organizations scale their AI systems efficiently, ensuring they are robust, secure, and effective at meeting business demands.

Governance

As businesses move GenAI products into production, setting up proper governance and guardrails becomes essential, especially for companies operating in regulated markets like the European Union. AI governance addresses the inherent risks that can arise from human biases and errors present in the creation and maintenance of AI systems. Governance provides a structured approach to mitigate these potential issues through sound AI policies, regulations, and data governance practices.

AI governance can take various forms across different contexts. One well-known example is the EU’s General Data Protection Regulation (GDPR), which includes specific guidelines for handling personal data within AI applications. Additionally, the EU AI Act, which is more directly focused on AI regulation, aims to establish a comprehensive legal framework for AI systems, categorizing them by risk levels and setting strict requirements for high-risk applications. Similarly, the AI Principles developed by the OECDand adopted by over 40 countries advocate for responsible AI practices, emphasising transparency, fairness, and accountability. Many companies are also establishing internal ethics boards or committees to oversee AI initiatives, ensuring alignment with ethical norms and societal expectations.

The intensity of AI governance measures often depends on factors like the business’s size, the complexity of the AI systems they deploy, and the specific regulatory requirements they must adhere to.

• Informal governance: This minimal approach relies primarily on the values and guiding principles. There may be some loosely defined processes, such as ethics reviews or internal discussions, but no formal structure for AI oversight.
• Ad-hoc governance: A step up from informal measures, this approach involves creating targeted policies and procedures for AI usage and development. Ad-hoc governance is often reactive, put in place to address particular risks or issues as they arise, but may lack comprehensiveness.
• Formal governance: The most rigorous form of AI governance, this approach entails a fully developed framework aligned with the organisation’s values and regulatory obligations. Formal governance typically includes risk assessments, ethical review processes, and structured oversight mechanisms.

Conclusion

As the adoption of generative AI systems continues to grow, the path from prototype to production has become increasingly challenging. While there are many resources available for building initial proofs-of-concept, transitioning these systems to serve enterprise-level demands requires overcoming significant infrastructure, performance, cost, and governance hurdles.

By leveraging effective scaling strategies, robust monitoring and observability, cost optimization techniques, and well-defined governance frameworks, a company can establish the foundations for enterprise-ready GenAI deployments. These solutions must be designed with scalability, security, and alignment to business objectives in mind from the outset. As the industry matures, we can expect to see more guidance and best practices emerge to help companies successfully navigate the journey from prototype to production-grade GenAI systems.

Sign up to our newsletter to receive future articles and deeper insights into each of these topics.