Private LLM vs Public API: What UK Financial Services Firms Need to Know

Every UK financial services firm is asking the same question: how do we harness the power of large language models without compromising client data, breaching regulations, or exposing the business to unacceptable risk? The answer depends largely on a fundamental architectural decision -whether to use public AI APIs or deploy a private LLM within your own infrastructure.

This is not a simple technology choice. It has direct implications for data sovereignty, regulatory compliance, cost management, and your firm's competitive positioning. This article provides a detailed, practical comparison to help financial services leaders make an informed decision.

Understanding Public AI APIs

Public AI APIs are the most accessible way to use large language models. Services like the OpenAI API, Azure OpenAI Service, and Google Vertex AI allow developers to send prompts to cloud-hosted models and receive responses via an API call. You pay per token (roughly per word) processed, and the provider handles all the infrastructure.

The appeal is obvious: minimal setup, no infrastructure management, and access to the latest models within days of release. For many businesses, this is the fastest path to AI adoption.

However, for financial services firms, public APIs introduce several significant concerns:

• Data leaves your environment: Every prompt you send travels across the internet to the provider's infrastructure. Even with encryption in transit, your data is processed on shared infrastructure that you do not control.
• Vendor data handling: While major providers now offer contractual commitments not to train on your data, the operational reality is that your prompts and responses exist -however briefly -on their servers. Logging practices, staff access controls, and data retention policies are all outside your visibility.
• Shared infrastructure: Public APIs typically run on multi-tenant infrastructure. While providers implement isolation between customers, you are sharing compute resources with every other customer on that platform.
• Limited audit capability: You can log what you send and receive, but you have no visibility into what happens between those two points. For an FCA-regulated firm, this opacity can be problematic.
• Vendor lock-in: Each provider has its own API format, pricing model, and model selection. Switching providers requires re-engineering your integration.

Understanding Private LLM Deployment

A private LLM deployment runs large language models within your own cloud infrastructure - typically within a Virtual Private Cloud (VPC) on AWS, Azure, or Google Cloud. The models themselves are accessed through services like AWS Bedrock, which provides managed access to leading foundation models including Anthropic's Claude and Meta's Llama, all within your private environment.

In a private deployment:

• Your data never leaves your VPC. Prompts and responses are processed within your cloud environment using private network connections (such as AWS PrivateLink).
• You have full control over access, logging, encryption, and data retention.
• The infrastructure is dedicated to your organisation -there is no multi-tenancy concern.
• You choose which models to deploy and when to update them, giving you control over consistency and validation.

This is the architecture behind our Secure AI Platform, which we deploy for regulated UK businesses that require enterprise-grade security and compliance.

Detailed Comparison

Let us examine the key dimensions that matter most to financial services firms.

Data Sovereignty

Public API: Data is transmitted to and processed on the provider's infrastructure. Even with data processing agreements in place, the data physically exists outside your environment during processing. For firms handling sensitive client financial data, this creates a dependency on the provider's security posture.

Private LLM: Data remains within your VPC at all times. You control the geographic region, the encryption keys, and the access policies. There is no ambiguity about where your data is or who can access it.

Regulatory Compliance (FCA and GDPR)

Public API: Using a public API for processing client data requires thorough due diligence on the provider as a third party. Under FCA expectations, you need appropriate contractual arrangements, exit strategies, and ongoing monitoring of the provider. Under UK GDPR, you need a Data Processing Agreement, a lawful basis for the transfer, and a Data Protection Impact Assessment. This is achievable, but it creates ongoing compliance overhead and residual risk.

Private LLM: Because data never leaves your environment, many third-party risk management requirements are significantly simplified. The AI service is part of your own infrastructure stack, subject to the same controls as your other internal systems. DPIA requirements still apply, but the risk profile is fundamentally different when data does not leave your control.

Audit Trail

Public API: You can log requests and responses on your side, but you have no visibility into the provider's internal processing. If a regulator asks exactly how a piece of data was handled between your API call and the response, you are dependent on the provider's assurances.

Private LLM: You own the complete audit trail. Every prompt, every response, every access event is logged within your infrastructure using your own logging and monitoring tools. This is the level of auditability that compliance officers and regulators expect.

Customisation

Public API: Customisation is limited to what the provider offers -prompt engineering, fine-tuning (where available), and system messages. You work within the provider's constraints.

Private LLM: Full control over model selection, system prompts, guardrails, input/output filtering, and integration patterns. You can tailor the AI experience precisely to your firm's workflows and compliance requirements.

Cost Structure

Public API: Pay-per-token pricing means costs scale directly with usage. For low-volume usage, this is cost-effective. However, as usage grows across an organisation, per-token costs can escalate quickly. A single team processing thousands of documents daily can generate surprisingly large monthly bills.

Private LLM: Infrastructure costs are more predictable -you pay for the compute resources whether they are fully utilised or not. At higher usage volumes, the per-query cost is typically lower than public API pricing. The break-even point varies, but for most mid-market financial services firms with 50+ regular AI users, private deployment is cost-competitive or cheaper within the first year.

Latency and Performance

Public API: Response times depend on the provider's current load, your network connection, and the model's queue depth. During peak demand periods, latency can increase or requests may be rate-limited.

Private LLM: Because the model runs within your own infrastructure with private network connectivity, latency is consistent and predictable. There is no contention with other customers, and you can scale compute resources based on your own demand patterns.

Model Choice and Flexibility

Public API: Each provider gives you access to their own models. OpenAI provides GPT models, Anthropic provides Claude, Google provides Gemini. Using multiple models means integrating with multiple providers.

Private LLM: Platforms like AWS Bedrock provide access to models from multiple providers through a single, consistent interface -all within your private environment. You can evaluate and switch between models without changing your architecture.

FCA Regulatory Expectations

The FCA has not banned the use of AI in financial services -far from it. However, the regulator has made clear that firms are expected to maintain appropriate governance over AI systems. Several key regulatory themes are directly relevant to the public vs private decision.

Consumer Duty

The Consumer Duty requires firms to deliver good outcomes for retail customers. If AI is used in any customer-facing process -from communications to product recommendations to complaints handling -the firm must be able to demonstrate that the AI contributes to good outcomes and does not cause foreseeable harm. A private deployment gives you the control and auditability to make this demonstration credibly.

Operational Resilience

The FCA's operational resilience framework requires firms to identify important business services and set impact tolerances. If AI becomes embedded in important business services, the firm needs assurance that the AI service will remain available. With a public API, you are dependent on an external provider's uptime. With a private deployment, you control the infrastructure and can build in redundancy and failover capabilities appropriate to your resilience requirements.

Third-Party Risk Management

The FCA expects firms to manage risks from third-party providers proportionately to their criticality. A public AI API that processes client data is likely to be classified as a material outsourcing arrangement, triggering extensive due diligence, contractual, and monitoring requirements. A private deployment reduces the third-party dependency significantly, though cloud infrastructure providers still require appropriate oversight.

The Cost Reality: Private Is Not as Expensive as You Think

A common misconception is that private AI deployment is prohibitively expensive compared to public APIs. The reality is more nuanced.

Public API costs are deceptively low at the per-token level. Processing a single document might cost a few pence. But multiply that by hundreds of employees, thousands of documents per day, and 250 working days per year, and annual costs can reach six figures quickly.

A private deployment on AWS Bedrock has infrastructure costs that are usage-dependent, but the per-query cost at scale is typically significantly lower. More importantly, the cost is predictable and controllable. You are not subject to the provider's pricing changes or token cost increases.

When you factor in the compliance costs of managing a public API as a regulated third-party arrangement -the due diligence, legal review, ongoing monitoring, and risk management overhead -the total cost of ownership often favours a private deployment for firms of any meaningful scale.

Decision Framework: When to Choose Each Option

A public AI API may be appropriate when:

• You are running a proof of concept or pilot with non-sensitive data
• The use case involves no client personal data or confidential information
• Usage volumes are low and likely to remain low
• Speed of initial deployment is the primary concern
• The use case is non-critical and not part of an important business service

A private LLM deployment is the right choice when:

• You are processing client data, personal data, or confidential information
• Regulatory compliance is a hard requirement (which it is for FCA-regulated firms)
• You need complete audit trails for regulatory reporting
• AI will be embedded in important business services or customer-facing processes
• Usage will scale across multiple teams and use cases
• Cost predictability matters for budgeting and planning
• You need control over model selection, updates, and behaviour

For most FCA-regulated financial services firms handling client data, the answer is clear: a private deployment is the appropriate architecture for production AI use cases.

Making the Move

The public vs private decision is one of the most consequential technology choices a financial services firm will make in the coming years. Getting it right from the start avoids the cost and disruption of migrating later -and avoids the regulatory exposure of operating on an inappropriate architecture in the interim.

At Evolve, we help UK financial services firms deploy AI on a private, secure platform that meets FCA expectations from day one. We handle the infrastructure, the security architecture, and the compliance mapping so that your teams can focus on the use cases that drive commercial value.

Whether you are at the evaluation stage or ready to move to implementation, our services are designed to meet you where you are. From initial assessment through to production deployment and ongoing support, we provide the expertise that financial services firms need to adopt AI with confidence.

Book a free strategy session to discuss your firm's specific requirements. We will map your regulatory obligations, assess your infrastructure readiness, and outline a clear path to secure, compliant AI deployment.