Enterprise artificial intelligence is entering a new phase as companies that spent the past two years experimenting with large language models are now moving those systems into live environments. It’s causing a shift in investment and engineering resources toward inference infrastructure.
Inference refers to the stage where a trained model processes new data and produces results. When a customer service chatbot answers a query or an AI system analyzes a financial document, that is inference at work. While training creates the model by processing vast datasets to learn patterns, inference applies that learned knowledge to perform specific tasks at scale. As enterprises deploy AI systems that manage thousands or millions of requests daily, inference becomes the dominant operational challenge and cost driver.
This fall, PYMNTS looked at inference and why it now matters more than training for most enterprises. Training a large language model happens once or periodically. Inference happens continuously every time a user interacts with an AI system. A single model might manage millions of inference requests per month, each requiring computational resources, adding latency and incurring costs. For companies running artificial intelligence in customer-facing applications, inference performance directly affects user experience, system reliability and operational expenses.
Infrastructure Follows Production Demands
This operational reality is reshaping the enterprise AI infrastructure market. Baseten, a platform focused specifically on inference infrastructure, raised $150 million in Series C funding in January, bringing its total funding to $216 million, to tackle that issue.
Baseten addresses core infrastructure challenges that emerge when companies move beyond experimentation. The platform manages model deployment, manages compute resources across different hardware types and optimizes performance for production workloads. It supports models from major providers including OpenAI, Anthropic and open-source alternatives, giving enterprises flexibility in model selection while maintaining consistent operational infrastructure.
The company serves enterprises that need reliable, performant inference at scale. Customers include Fortune 500 companies running AI systems that process high volumes of requests with strict performance requirements.
Input Preprocessing Becomes Critical Component
Baseten recently acquired Parsed, a company that builds technology for structuring and preprocessing inputs before they reach AI models. This acquisition addresses a specific technical challenge in production inference systems. Raw inputs such as unstructured documents, images or complex data formats often need processing before a model can reliably interpret them. Parsed’s technology handles this preprocessing step, extracting relevant information and formatting it appropriately for model consumption.
The Parsed acquisition strengthens Baseten’s inference infrastructure by improving reliability and efficiency. When inputs are properly structured before reaching a model, inference becomes more predictable. Models receive data in consistent formats, reducing errors and improving response quality. This preprocessing also affects performance and cost.
For enterprises running production AI systems, input quality and consistency matter significantly. A customer service system processing thousands of queries per hour needs reliable inference across varied input types. A financial analysis tool processing regulatory documents needs consistent extraction and structuring before model inference.
As PYMNTS has reported, hyperscalers are also expanding aggressively into inference through custom chips and tightly integrated platforms. AWS promotes Inferentia, Google is pushing TPU v5e, and Microsoft is developing its Maia AI chips, pairing each with proprietary serving frameworks and cloud services. These strategies emphasize end-to-end control, bundling compute, storage and AI tooling into unified platforms designed to keep workloads inside a single cloud ecosystem.
Source: https://www.pymnts.com/