Introduction
In enterprises today, building AI enabled products and services is becoming standard practice. One of the most critical design questions is whether to host models internally or rely on APIs, and the answer has direct implications for data security, cost, and scalability. This decision must be addressed early in the design stage, as it shapes how your AI systems will run in production.
For clarity, this blog does not cover building AI applications from scratch or migrating enterprise systems from data centres to cloud providers like AWS, Azure, or GCP. The focus is on model hosting choices for AI, with security as the core concern and cost and scalability close behind.
Enterprises may already run most of their infrastructure in the cloud, yet the decision on model hosting remains separate and critical.
In this blog, I will show how to approach this decision in a structured way. We will explore the trade-offs between internal hosting and API adoption, the security practices that must run in parallel, and reference architectures that help enterprises strike the right balance. By the end, you will have a clear view of how to design AI systems that are both secure and production ready.
Why Data Security Matters
The quality of any AI system is determined not only by the model, but by the data it consumes. In enterprises, this often includes sensitive or regulated information, which makes secure handling essential for compliance and trust.
Frameworks such as HIPAA (healthcare), the California Consumer Privacy Act (CCPA), the General Data Protection Regulation (GDPR) in Europe, and industry-specific mandates like PCI DSS (financial services) impose strict requirements on how data is stored, shared, and processed. Non-compliance brings heavy penalties, reputational damage, and loss of customer confidence.
In short, AI success depends on two things: the quality of the data that feeds the model, and protecting data as it moves through the pipeline.
How Models Actually Use Data
Models use enterprise data in three main ways: fine-tuning, prompting, or retrieval. Each method changes how data is handled and how much risk it carries.
Even with a strong data engineering and science pipeline in place, the challenge is moving curated data securely into a vector database. Curated datasets are converted into numerical embeddings and stored in vector databases such as Pinecone or Weaviate for retrieval. Typically, only the required details are embedded and stored, ensuring the model receives the minimal necessary context rather than entire raw datasets.
Retrieval, through vector databases, is the most common way enterprises move curated data into AI pipelines, especially in Retrieval-Augmented Generation (RAG) workflows.
For fine-tuning, curated raw datasets must be carefully selected to avoid exposing sensitive or unnecessary information during training.
Models typically consume enterprise data as discussed above. Other variations exist, such as parameter-efficient tuning or model distillation, but these are extensions of the same core approaches
Figure-01: Data Usage Approaches
Fine-Tuning vs Prompting vs Retrieval
Fine-Tuning
The model’s weights are updated with enterprise data. It learns domain-specific knowledge, but sensitive details may be memorised and exposed. Once fine-tuned, the model no longer needs the dataset at runtime, yet the risk is baked into the weights.
Prompting (Direct Injection)
Data is passed into the prompt at inference. The model does not store it permanently, but exposure depends on where the prompt is processed. With external APIs, retention and logging policies become critical.
Retrieval (RAG with Vector Databases)
Curated data is embedded and stored in a vector database. At inference, only relevant fragments are retrieved and passed to the model. This approach keeps raw data inside enterprise control while still enabling accurate, business-specific outputs.
“Protecting data in motion is as important as model choice”
| Approach | How It Works | Key Risk |
| Fine-Tuning | Updates model weights with enterprise data. | Sensitive details may be memorised. |
| Prompting | Data is passed into the prompt at inference. | Exposure if prompts include sensitive data. |
| Retrieval (RAG) | Stores embeddings in a vector DB, retrieves only what’s needed. | Needs embedding pipeline. |
Table-01: Comparison of fine-tuning, prompting, and retrieval
Enterprise Takeaway
Fine-tuning brings depth but at higher cost and risk. Prompting is quick but exposes sensitive data if not tightly controlled. Retrieval stands out as the most practical enterprise approach, balancing accuracy, scalability, and compliance.
Fine-tuning, prompting, and retrieval define how data interacts with models. The next decision is where the model runs. Enterprises must choose between hosting models internally within controlled infrastructure, or accessing them through external APIs. We begin with internal hosting, where control is strongest but so are the operational demands.
Internal Hosting
In most conversations I have with leadership teams, the first instinct is simple: keep the data close. Enterprises operate on sensitive information, whether it is patient records in healthcare, customer transactions in finance, or identity data in government. Moving this data outside their walls is seen as unnecessary risk.
Why Enterprises Consider Internal Hosting
Hosting internally means the movement, storage, and use of data remain under direct control. It gives organisations confidence that they can meet strict requirements from regulators such as HIPAA, GDPR, PCI DSS, or RBI.
There is also the advantage of using enterprise data to create more value. Teams can fine-tune models or add lightweight adapters without exposing raw datasets to external APIs. This results in AI systems that speak the organisation’s language and stay compliant. For many enterprises, internal hosting is not just a preference, it is a safeguard for data trust.
Internal Hosting Benefits
The biggest benefit of internal hosting is control. Data stays inside enterprise systems and is governed directly by the organisation. This builds trust with regulators, customers, and leadership, and allows AI to integrate with existing systems without exposing sensitive information outside the boundary.
Key benefits include:
-
- Data sovereignty: sensitive and regulated data never leaves enterprise systems.
-
- Compliance confidence: easier to demonstrate alignment with compliance frameworks.
-
- Security visibility: complete control over encryption, access, and audit logs.
-
- Seamless integration: direct connection to databases, ERP, and CRM systems without external transfers.
-
- Lower latency: processing inside the enterprise network avoids external round trips.
“Internal hosting gives control, but raises cost and complexity”
Internal Hosting Challenges
A key challenge in enabling AI-powered solutions is that they span data, compute, databases, networks, and every layer of infrastructure. This complexity often pushes costs beyond the budgets set for the financial year. In many cases, the return on investment is delayed, or never fully realised. On the other side, failing to enable these capabilities risks handing market share to competitors who move faster.
AI adoption is not like the digital transformations of the past. Earlier, enterprises migrated from legacy to service-oriented architectures or microservices, or adapted to new tech stacks that offered incremental gains and loosely coupled systems. AI brings an entirely different set of demands, and with it, a sharper set of challenges.
I have summarised the main ones below:
-
- Infrastructure cost: GPUs and storage to process enterprise data are expensive and often underused when demand drops.
-
- Operational complexity: secure pipelines for data movement, monitoring, and retraining add significant overhead.
-
- Scalability limits: expanding GPU clusters to handle surges in data processing is slower than scaling API-based solutions.
-
- Talent requirements: running AI pipelines demands specialised skills across MLOps, optimisation, and compliance.
-
- Model access: open-weight models (Llama, Mistral, Gemma, gpt-oss) can be hosted internally. Gemini is now available on-prem through Google Distributed Cloud. GPT-5 is delivered via OpenAI’s API and Azure AI Foundry, while Claude remains cloud-delivered through Anthropic/Bedrock.
Internal Hosting Reference Architecture
This reference architecture is a simplified view, in an internal hosting setup.
Figure-02: Internal Hosting – Reference Architecture
API Based Hosting
Considering the pressure of time-to-market and the momentum around AI adoption, many enterprises lean towards API-based integration. This is especially true for organisations that have not yet built a dedicated AI vertical capable of delivering tangible solutions.
Why Enterprises Choose API-based Hosting
The main driver is speed. With APIs, enterprises can integrate frontier models such as GPT-5, Claude, and Gemini into products in weeks instead of months. There is no need to buy GPUs, manage storage, or build complex pipelines. Providers like OpenAI, Anthropic, and Google handle the infrastructure, while enterprises focus on delivery. The risk is that data leaves the boundary, and even if it is not stored in a provider’s data centre, it may still be used to train weights, which creates exposure.
API Benefits
Despite risks of data exposure and shared-input training, API integration offers clear benefits. These can help enterprises enter the market quickly or sustain their position. The open question is how long these advantages will hold.
-
- Rapid adoption: faster time-to-market without infrastructure setup.
-
- Frontier models: access to the latest capabilities from OpenAI, Anthropic, and Google.
-
- Elastic scaling: API usage grows or shrinks instantly with demand.
-
- Lower operational overhead: no GPU clusters or MLOps pipelines to manage internally.
-
- Continuous upgrades: model improvements are delivered automatically by providers.
“APIs bring speed, but data safeguards must come first”
API Challenges
The trade-off is data movement. Requests sent to APIs must leave enterprise boundaries, raising privacy and compliance concerns. Sensitive information may be logged or retained by providers unless strict controls are applied. There are also cost dynamics: per-token pricing can escalate quickly if not optimised.
Key challenges include:
-
- Data exposure: prompts and context leave enterprise systems, creating compliance risks.
-
- Retention policies: providers may log requests unless data retention is disabled.
-
- Cost unpredictability: usage-based billing grows with volume, often harder to forecast than internal infra spend.
-
- Vendor lock-in: reliance on a single provider limits flexibility and may complicate switching.
-
- Network dependency: API availability and latency depend on internet connectivity.
API Based Reference Architecture
This reference architecture is a high-level illustration, focusing on how data moves when models are accessed through external APIs.
Figure-03: API-Based Hosting – Reference Architecture
Mitigation Strategies
Data leaving enterprise systems through APIs is the single biggest risk. The good news is that there are clear ways to reduce exposure and stay compliant while still benefiting from API-based hosting.
Key mitigation strategies include:
-
- Avoid raw data: never send personally identifiable information (PII) or sensitive records directly to an API.
-
- Minimal data only: pass just the context needed for the task, nothing more.
-
- Vector databases: keep embeddings and enterprise knowledge internally, and retrieve only fragments relevant to the query.
-
- Protect embeddings: apply tokenisation and encryption so that vector databases cannot be reverse-engineered.
-
- Private connectivity: use virtual private connections or secure endpoints instead of the public internet.
-
- Retention control: ensure the provider does not store even masked or tokenised data. Review retention policies, and sign NDAs or data processing agreements where required. Model providers typically state that customer data is not used for training, though it is always sensible to confirm this with your service provider. (see Google’s policy: Data you submit and receive)
-
- Audit and monitoring: track all outbound data calls with logs and alerts to detect misuse.
-
- Role-based access: apply least-privilege access for teams interacting with API keys
Anchoring in AI Frameworks
Mitigation techniques reduce the immediate risks of sending data outside the enterprise, but they are not enough on their own to build trust. Enterprises need to anchor their approach in recognised frameworks that guide AI adoption responsibly. Two are especially relevant today.
NIST AI Risk Management Framework (AI RMF)
The NIST AI RMF provides a structured way to identify, measure, and manage risks in AI systems. Its strength is in turning abstract risks into categories that can be mapped to real controls across governance, data, and monitoring. In practice, it helps leadership teams ask the right questions: where does our data flow, what controls do we have, and how do we prove it?
ISO/IEC 42001:2023
ISO/IEC 42001:2023 is the first international standard for AI management systems. It extends ISO’s established disciplines in information security and quality management to AI. Beyond compliance, it acts as a proof point to regulators and customers that AI use is being managed with the same rigour as financial audits or data security certifications. For enterprises, this is not just about passing an audit, it is about demonstrating accountability and assurance.
Together, these frameworks give enterprises a foundation. The technical safeguards are essential, but aligning with NIST or ISO standards ensures AI adoption stands up to regulatory and board-level scrutiny.
“Compliance builds confidence, not just audits”
Conclusion
So what should you do as a decision-making leader today? The truth is there is no one-size-fits-all answer. The choice of where to host your models must be guided by your business context. The first step is for the steering board to accept that every option involves a trade-off between three factors: data, cost, and scalability.
In the short term, the fastest way to stay relevant is through API-based adoption. It allows your products to tap into proven models already running reliably in production. This matters, because as a recent MIT report highlights, most AI pilots fail to scale due to organisational and tooling gaps. Yet enterprises that partnered with the right providers and executed well have already created millions in value.
The risks of API adoption, however, can and must be managed. Techniques such as tokenisation, advanced masking, and redaction before data leaves your boundary, retrieval layers that keep raw data internal, private connectivity through VPC links, and strict contractual safeguards with providers all help ensure that even when speed is the priority, data remains protected.
Over the long run, if you already have an AI vertical, strengthening it will allow you to align models more closely with business needs and keep data within your control. If you do not, building one step by step will help your organisation learn, adapt, and gain confidence in what level of model capability is truly required. When scale and demand converge, hosting models internally may prove to be the most strategic option.
Enterprises that put data first, move fast, and build control will lead the market.