AI Inference
AI inference is the process of running a trained machine learning model on new input data to produce predictions, classifications, or generated content. In the context of LLMs, inference refers to the computation that happens when a user sends a prompt and the model generates a response. The speed, cost, and quality of inference vary significantly across providers.
In Financial Services
Real-World Example
A large European bank deploys Azure OpenAI for its customer-facing AI assistant. The bank requires all inference to happen within EU data centers to comply with GDPR. Customer queries are sent to GPT-4o hosted on Azure's France region, responses are generated in under 2 seconds, and all inference logs are retained for regulatory audit. The bank pays per token, with monthly inference costs reaching $50,000 for handling 1 million customer conversations.
Why It Matters for Finance
Inference infrastructure is a strategic decision for financial AI. The right provider can mean the difference between compliant and non-compliant AI deployment, between profitable and unprofitable AI operations, and between responsive and slow customer experiences. Financial institutions should evaluate inference providers on data residency, latency SLAs, pricing models, and model availability before committing to a platform.
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Frequently Asked Questions
What is AI inference in financial services?
AI inference in financial services is the process of running a trained AI model on new data to generate predictions, classifications, or text. For example, when a bank sends a customer query to an LLM and receives a response, that is inference. Inference happens in real-time for chatbots or batch-mode for document processing.
Which inference provider is best for regulated financial institutions?
AWS Bedrock and Azure OpenAI are preferred by regulated financial institutions because they offer data residency guarantees, private cloud deployments, and compliance certifications. They allow banks to keep data within their chosen geographic boundary while accessing frontier LLMs.
How does inference latency affect trading applications?
Inference latency directly impacts trading applications where milliseconds matter. Groq offers the fastest inference speeds at under 100ms for most models, making it suitable for real-time analysis. For trading signal generation, low latency inference is critical to act on market-moving information before competitors.