For years, real-time inference has been the go-to approach for leveraging large language models (LLMs) due to its speed and immediate results. However, in many cases, this default approach can be a costly luxury. The question is: Do you really need real-time outputs, or are you using it simply because it's the standard? Take a moment to reflect on the tasks you use LLMs for, such as document analysis or content generation. How often do you actually need real-time responses? For many use cases, especially those beyond chatbots and instant responses, batch inference is rapidly emerging as a cost-effective and scalable alternative.

Batch inference: A smarter solution

Batch inference allows you to achieve higher-quality outputs for the same or even lower cost by using larger, more powerful models that would be far more expensive for real-time processing. As the need for more complex models and larger datasets grows, batch inference offers a way to optimize your resources. By processing large datasets in scheduled "batches" – such as daily, weekly, or overnight – you can run more powerful models without paying the premium for real-time speed. This not only makes it a smarter, more scalable solution, but it also lets you maintain high-quality results while maximizing your cost-efficiency.

Why choose batch inference?

Real-time inference is ideal for situations where immediate results are necessary, but when you're looking to get the most value from your resources, batch inference offers a smarter solution. Here are just a few reasons why:

• Significant cost savings: Real-time inference can be expensive due to the need for continuous processing and higher computing resources. Batch inference lets you schedule jobs when it makes sense for your budget, optimizing resource usage and slashing unnecessary costs.

• Scalability for large-scale jobs: Whether you're dealing with terabytes of data or tackling a complex project, batch inference can be easily scaled up to meet the needs of different use cases. This flexibility makes it a perfect solution for machine learning tasks, especially as your data grows.

• Higher quality accuracy: Batch inference offers better accuracy per dollar by allowing you to run larger models at a lower overall cost. This means you can choose to prioritize higher-quality outputs without the cost burden of real-time processing, making it easier to achieve top-tier results within budget.

When is bath inference the right solution?

Batch inference is the ideal approach for many use cases where cost savings are a priority and real-time outputs aren't necessary – such as periodic tasks that are done on a regular schedule (daily, weekly, overnight, etc.). This includes scenarios like:

• Document analysis: Whether you're processing massive legal documents, financial statements, or medical records, batch inference offers an effective way to handle large datasets without the cost associated with real-time processing.

• Content generation: For applications that involve generating large volumes of content, such as product descriptions, marketing copy, or automated reporting, batch inference allows you to produce text efficiently, ensuring optimal use of resources.

• Synthetic data generation: When creating large volumes of artificial data for training machine learning models, testing systems, or simulating scenarios, batch inference enables efficient processing of datasets in bulk.

Get started with batch inference on kluster.ai

As the demand for larger datasets and more complex models continues to grow, batch inference is no longer just a smart option – it's a critical tool for managing costs and scaling AI workflows effectively. If you've been defaulting to real-time inference out of habit or because it's the most visible option, it's time to ask yourself: Do you really need real-time outputs for every use case? For many applications, batch inference offers a better balance of performance, cost, and scalability, allowing you to achieve higher-quality results without the premium costs of real-time processing.

Ready to shift your approach and get more from your resources? Click the Start building button below to explore batch inference on kluster.ai and take control of your AI workflows.