So You Want to Use LLMs in Production

Large Language Models (LLMs) are gaining traction across industries, from customer service to content generation. The promise of AI enhancing or even replacing tasks is hard to ignore, but deploying LLMs in a live environment is a significant undertaking. Whether you're a startup or an established enterprise, here's what you need to consider before taking the leap.

Evaluations: How Do You Know It Works?

Before deploying an LLM into production, the key question is: does it actually work? Without structured evaluations, you could be introducing a tool that looks impressive but fails to deliver consistent, reliable results.

Performance should be measured across multiple dimensions, including accuracy, response quality, handling of edge cases, and real-world user engagement. Continuous A/B testing is essential to iterate on the model, fine-tune it, or introduce enhancements such as Retrieval-Augmented Generation (RAG) to improve accuracy.

Version control for models is also critical. Just like software, models evolve, and tracking changes, monitoring improvements, and ensuring backward compatibility is essential for long-term stability.

Safety and Privacy: Guarding Against Data Vulnerabilities

Deploying an LLM in production requires a robust security framework. Malicious actors can exploit vulnerabilities such as prompt injection attacks to manipulate responses or extract sensitive data. OWASP has updated its LLM Security Top 10 (2025), highlighting key risks, including adversarial attacks and data leakage.

To mitigate these risks, businesses need to implement robust content filtering and input sanitisation measures to prevent misuse. Automated threat detection tools can help flag suspicious activity before it becomes a serious issue. Compliance with GDPR, the UK Data Protection Act, or industry-specific regulations is also essential to avoid legal and reputational risks.

Companies like Aqua Security now offer cloud-native security solutions specifically for LLM deployments, helping businesses monitor for vulnerabilities in real-time.

Data: The Lifeblood of Your Model

Data quality is just as important as the model itself. Poorly curated datasets can introduce bias, degrade performance, and expose your system to compliance risks.

Ensuring high data quality is crucial to preventing bias and compliance risks. Automated data pipelines can clean, filter, and de-identify sensitive information before it reaches the model. Regular audits using bias detection frameworks help maintain fairness in outputs. Versioning datasets ensures transparency, allowing businesses to track how different datasets influence model performance.

Startups should also consider privacy-preserving AI techniques, such as federated learning or synthetic data generation, to minimise data exposure risks.

Cost: The Silent Elephant in the Room

Deploying LLMs isn’t just a technical challenge, it’s a financial one. Costs can spiral quickly, especially for startups relying on API-based models with per-token pricing.

Managing the costs of LLM deployment is just as important as optimizing performance. Businesses need to track per-user and per-query costs closely using observability platforms. Fine-tuning smaller models instead of deploying large-scale ones can significantly reduce resource consumption. Techniques like quantisation and serverless GPUs can further optimise inference efficiency and help control cloud expenses.

Services like Orq.ai provide LLMOps infrastructure to help teams optimise costs by offering model hosting, versioning, and scaling tools.

Scalability: Can You Handle the Load?

Your LLM might work well with a handful of users, but can it scale efficiently? Hosting your own model gives you more control but requires significant infrastructure and DevOps expertise. Using third-party APIs like OpenAI, Anthropic, or Mistral reduces overhead but comes with latency, pricing, and rate-limit constraints.

Ensuring that an LLM can scale efficiently is a key challenge. Real-time monitoring helps detect system slowdowns early, preventing performance issues. Implementing auto-scaling infrastructure ensures that the system can handle spikes in demand without over-provisioning resources. Some businesses may opt for hybrid deployments, using lightweight local models for simple queries while relying on cloud-based LLMs for more complex processing.

Deploying LLMs in production in 2025 requires more than just fine-tuning a model and switching it on. From security and privacy considerations to cost management and scalability, businesses must adopt LLMOps best practices to ensure a smooth rollout. With advancements in security tooling, inference optimisation, and observability, there are now better ways to manage these challenges than ever before. Staying ahead means leveraging the right tools, planning for risks, and continuously monitoring performance to ensure your LLM deployment remains efficient, scalable, and secure.