LLMs at scale: Getting real business answers from massive datasets

Finance, treasury and procurement teams are drowning in data. But buried in that data are opportunities to boost efficiency, stay competitive, and meet compliance demands. Recent advances in large language models (LLMs) allow businesses to process massive data sets asynchronously, a capability long used in content moderation and auditing, now being extended to transactional data for insights across supply chain and customer domains.

In this post, we share:

• Why LLM batch processing is useful for large, structured datasets
• Which business problems batch processing can solve
• How to implement LLM inference in practical terms
• How OpenAI and Amazon Bedrock compare for enterprise use

Using LLMs with huge datasets

You may be familiar with using an LLM interactively, “chatting” to complete tasks or generate ideas. The same real-time interaction is possible between systems via single-request API calls, mirroring human–computer dialogue. However, this approach still demands immediate compute power, making it costly and inefficient when working with hundreds, thousands, or even millions of records. Batch LLM inference, by contrast, enables large-scale processing in the background as a single structured job, delivering analysis and insights at scale.

Business problems solved using LLM inference

If your business is already using AI, you may have run into challenges around cost, prompt engineering, or tool limitations. Batch processing solves many of these by letting you submit and manage thousands of structured prompts at once—saving time and unlocking new capabilities, especially in data-rich domains like:

• Supplier data cleansing
  Detect duplicate or outdated records across ERP systems to improve supplier onboarding and reduce errors.
• Vendor payment behaviour
  Analyse and enrich vendor data to predict which suppliers are likely to accept early payments, and under what terms.
• Working capital optimisation
  Model different payment terms and methods to balance liquidity and supplier satisfaction.
• Compliance, spend categorisation and rationalisation
  Use LLMs to review contracts, supplier documentation and invoices to monitor adherence to terms and opportunities to reduce costs.

Batch inference is not suited for real-time applications like customer support or conversational interfaces—but it’s ideal for large-scale offline analysis like these.

How to work with an LLM in batch mode

Working with LLMs in batch mode is different from chat-style interaction. It requires preparing structured inputs, managing uploads, and handling the results. The basic steps are:

1. Prepare your data
   Create a JSONL file (JSON Lines), where each line is a separate prompt or request. Each line must follow the format required by the LLM you’re using.
2. Upload or send your data
   On Amazon Bedrock, this means uploading to S3. For OpenAI, you send it via API. No-code tools like Zapier or Make.com can help if you’re using OpenAI and not a developer.
3. Start the batch job
   You’ll specify the data source, model, and output destination. Once submitted, processing happens in the background.
4. Track progress
   Most platforms do not notify you automatically. It’s worthwhile checking the job status manually to avoid unnecessary delays as they can fail without notice.
5. Download results
   Output is usually in the same JSONL format and can be accessed via the platform’s API or cloud storage.

OpenAI vs Amazon Bedrock: A critical comparison

Choosing between Amazon Bedrock and OpenAI for batch inference depends on your priorities: Bedrock offers more control, scalability, and model variety but demands significantly greater setup and technical overhead. OpenAI, on the other hand, enables faster development and easier iteration thanks to its simplicity and small-batch flexibility—ideal for teams prioritising speed and ease of use over infrastructure customisation.

Note: Costs and features are accurate at the time of publication (May 2025). However, this is a rapidly evolving field, and everything is subject to sudden, unannounced changes.

Conclusion

Batch inference isn’t just a technical feature, it’s a strategic advantage. Finance teams using LLMs to unlock insights at scale are not just saving time, they’re making smarter, faster decisions. Whether the goal is better supplier relationships, improved ESG compliance, or working capital efficiency, the ability to process transactional data intelligently will define tomorrow’s leaders.

At Previse, we believe in turning data into decisions—reliably, securely, and at scale.

References

• Amazon Bedrock Batch Inference
• Automate Amazon Bedrock batch inference: Building a scalable and efficient pipeline
• Supported foundation models in Amazon Bedrock
• Amazon Bedrock pricing.
• Code examples for batch inference
• Batch processing with the Batch API