Intro

Generative AI could add up to $4.4 trillion in value globally, offering insurers a way to improve underwriting, risk assessment, and personalization. Insurers already have vast unstructured data and, according to a McKinsey survey, expect 10–20% productivity gains from AI.
So the key question no longer is if they will use AI, but how they’ll deploy it. This article shows how to choose the right model – cloud, on-premises, or hybrid so you can tap into AI’s benefits while staying compliant, efficient, and cost-effective.

Picking the best AI implementation model – what’s non-negotiable for insurance companies

Security

When it comes to security, there’s no room for compromise. Your AI platform must be rock-solid from day one. First, every bit of customer data – applications, claims, medical records, telematics streams must be encrypted with industry-leading standards like AES-256, both at rest and in transit. If you choose an on-premises implementation, be sure your infrastructure fully supports these encryption frameworks; if you go cloud, insist on customer-managed keys so your organization retains ultimate control over who can unlock that data.

Next, identity and access management (IAM) must be ironclad. Granular, role-based access controls are non-negotiable. Your on-prem system must integrate seamlessly with your existing LDAP or Active Directory, and your cloud AI platform must enforce multi-factor authentication (MFA), single sign-on (SSO), and least-privilege policies at every layer. By demanding this level of precision, you’ll make sure only the right people can access the right resources at the right time.

Finally, never settle for a vendor that can’t demonstrate a Secure Development Lifecycle (SDL). Any credible cloud or on-prem provider should have completed thorough threat modeling, rigorous code reviews, third-party penetration tests, and regular vulnerability scans. Insist on seeing documentation of these assessments. Only then can you be sure your AI engine is built on a foundation of security best practices that keep your business (and your customers) safe.

Scalability

When claim volumes spike whether during open enrollment or after a major storm your AI infrastructure must instantly scale. On-premises hardware that’s right-sized for average demand will buckle under sudden surges, delaying critical payouts and killing customer trust. By contrast, a cloud or hybrid model lets you “fail over” to additional compute in seconds, ensuring your fraud-detection and claims-processing engines stay online at peak load.

Fraud detection at point-of-sale demands sub-100 ms response times. If your on-premises setup can’t guarantee millisecond-level inference, you risk letting suspicious applications slip through or frustrating agents with slow underwriting feeds. A hybrid or edge-enabled architecture can route real-time scoring to the lowest-latency endpoint, giving you the speed you need without sacrificing control.

Also, retraining sophisticated catastrophe models whether Monte Carlo simulations or deep neural networks often consumes hundreds of GPUs for days. Owning that level of GPU capacity on-premises requires massive CapEx and leaves expensive hardware idle most of the year. If you use cloud, you spin up thousands of GPUs for a 48-hour retraining window and then scale back, paying only for what you use.

Compliance

When insurers develop AI solutions, they need to comply with both AI-specific regulations and long-standing industry laws – and these can vary significantly across regions. For example, GDPR, Solvency II, and IDD apply in Europe, while the U.S. has its own framework, including NAIC model laws.

There’s also the EU AI Act, which came into force in mid-2024, adding a new layer of strictly AI-related requirements.

One big consideration when choosing between cloud and on-premise, is data residency: if personally identifiable information (PII) or sensitive underwriting data isn’t allowed to cross borders, then a fully cloud-hosted AI setup may not be compliant.

Insurers also need to make sure that the AI they’re building (whether client-facing or for operational purposes) meets the requirements for specific certifications. These include ISO 27001, SOC 2 Type II, or PCI DSS if payment data is involved.

Lastly, audit and reporting capabilities of the AI platform are also crucial. This affects the choice of where the solution will be hosted, because regulators expect full traceability. This means access to detailed logs, version histories, and exportable reports that clearly show who accessed what, when, and why.

Since new laws are rolled out so intensely, about 10-15% of insurance company leaders admit that they’re hesitant about choosing between on-premise, hybrid, and cloud solutions.

Integration and interoperability with core insurance systems

In addition to regulatory compliance, insurers also need to consider the practical side of building AI on premises, in a hybrid setting, or fully on the cloud. What I have in mind here is how well it fits into the company’s existing tech stack.

For example, AI needs to plug directly into core policy administration and claims systems like Guidewire, Duck Creek, or Sapiens. The same logic applies to cross-company tools like CRM, BI, or reporting software.

Perhaps more importantly, whether the AI is hosted on-site or in a hybrid or cloud setting, data exchange needs to happen seamlessly in real time. For example, let’s take rating engines, which often process data from a variety of inputs like weather, telematics, or credit scores. On-prem deployments need secure APIs to handle these feeds, while cloud setups should offer low-latency connectivity through services like AWS Direct Connect or Azure ExpressRoute.

Cloud vs on prem vs hybrid solutions – a quick comparison

So, which deployment model should you go with – cloud, on-premise or hybrid? Let’s take a look at the benefits and drawbacks of each to help you decide.

TL;DR

Cloud

Advantages of running AI in the cloud

• Elastic scalability & burst capacity – cloud platforms allow insurers to elastically scale compute (CPUs/GPUs) and storage up or down based on real-time demand which is key during open enrollment or catastrophe-driven claim surges. This “cloud-as-burst” model prevents on-premises hardware from becoming a bottleneck during peak periods.
• Lower upfront capital expenditure – instead of buying and depreciating servers, insurers pay only for what they use, making pilot projects and proof-of-concepts more affordable.
• Built-in compliance & security posture – leading cloud providers maintain ISO 27001, SOC 2 Type II, PCI DSS, and local insurance-specific certifications (e.g., BaFin, IVASS) out of the box. They also furnish customer-managed keys (CMKs) for encryption at rest/in transit, reducing the burden on insurers to build these controls from scratch.

Disadvantages of running AI in the cloud

• Data residency & regulatory constraints – in many regions such as EU countries under GDPR or markets with strict insurance rules, sensitive underwriting details and customer PII must stay within national borders. If your chosen cloud provider can’t guarantee that data never leaves the country, you risk running afoul of local regulations.
• Hidden data transfer costs – even though you only pay for the compute you use, moving lots of training data or model backups out of the cloud can rack up hefty egress fees. If you don’t plan and tag your resources carefully, those monthly bills can suddenly jump much higher than expected.
• Risk of vendor lock-In – if you rely on proprietary tools like managed feature stores or custom inference runtimes, it becomes tough to move your models or data elsewhere later. Over time, shifting model weights, feature transformations, or pipelines to a different environment (on-prem or another cloud) can mean extensive rework.

On-premises

Advantages of running AI on-prem

• Full data control & residency assurance – by keeping data and models within their own data center or co-located facility, insurers avoid any ambiguity around cross-border data transfers. This is critical for jurisdictions with strict localization laws or internal data governance policies that prohibit third-party hosting.
• Predictable long-term costs at scale – after the initial CapEx investment in servers, networking, and cooling, ongoing costs like power, maintenance, and depreciation become largely fixed. For insurers with stable, consistently high compute demands (e.g., daily batch re-underwriting), on-prem can be more economical throughout the years.
• Low-latency, high-performance inference – when you need to store large data streams in real time like telematics for usage-based insurance or IoT-driven claims on-premises GPUs can return results in under 100 ms without any public-internet delays. That keeps your fraud detection and pricing engines running within SLA requirements.
• Seamless integration with legacy systems – many insurers keep their policy administration, billing, and claims systems on-premises. Running AI alongside those systems cuts integration work and removes the need for secure API gateways or VPC peering to the cloud.

Disadvantages of running AI on-prem

• High upfront CapEx & maintenance overhead – buying servers, storage arrays, virtualization software, and network switches demands a huge upfront investment. Then you have ongoing costs for patching, firmware updates, and physical maintenance which can send your overall total cost of ownership through the roof.
• Limited elasticity & burst capability – unless data centers are sized for worst-case loads, on-premises clusters can become saturated during seasonal spikes (e.g., natural disaster-driven claims). Procuring new hardware to expand capacity can take weeks or months during which performance degrades, causing claim backlogs.
• Longer time to deploy & upgrade – setting up new GPU servers, applying OS patches, and configuring MLOps tools (like Kubernetes and feature stores) can take weeks on-prem. By contrast, cloud platforms let you use “infrastructure as code” to spin up clusters in minutes. This means that on-premises setup can slow down innovation.
• Requires in-house MLOps/DevOps expertise – managing an on-prem AI environment calls for skilled DevOps/MLOps engineers to handle hardware troubleshooting, network configuration, security patching, and capacity planning. Insurers with limited in-house AI operations bench may find this too resource-intensive.

Hybrid

Advantages of running AI in a hybrid model

• Balanced data residency thanks to cloud bursting – a hybrid setup can prove most optimal under some legislations. For example, insurers can keep sensitive PII, underwriting data, and other data that can’t be exported beyond the region on-premise, while offloading large model training or non-sensitive analytics to the cloud. This protects customer data (and satisfies local residency mandates) while also giving the organization flexibility for developing the AI’s capabilities.
• Optimized costs & performance trade-offs – depending on your company’s services and the AI’s desired functionalities, you can run ongoing, core processes (like real-time fraud scoring) on your on-premise hardware. Meanwhile, you could “outsource” large, one-off GPU tasks like retraining catastrophe models to the cloud. This pay-for-only-when-needed approach can yield a better total cost of ownership.
• Gradual migration capabilities for legacy insurtech systems – companies operating on monolithic policy administration environments that operate on premise can adopt cloud AI modules in a step-by-step manner, without restructuring their entire infrastructure. For example, these companies can start implementing AI-driven features like image-based damage assessment, and move more workloads continuously. I believe that this can be a good choice for those who are reluctant towards moving fully to the cloud, at least until AI and insurance regulations fully mature.
• Enhanced disaster recovery – using a hybrid approach, you can back up your critical data to the cloud, in real time. If there’s ever an on-premise based data outage, you can automatically rely on cloud instances and reduce downtime for uses. You’re also preventing data loss in case of critical on-premise hardware failure.

Disadvantages of a hybrid AI model

• Increased archi tectural & operational complexity – managing two environments requires maintaining distinct networking setups (like VPNs and VPC peering), data synchronization workflows, and security policies. To handle this complexity, insurers need to have access to the right expertise, as well as invest in orchestration tools.
• Data integration & consistency challenges – keeping feature stores, model artifacts, and training datasets in sync between on-prem and the cloud can be tricky and prone to errors. When data pipelines aren’t aligned, it’s easy to run into version drift, which makes audits harder and puts more pressure on having solid data governance across both environments.
• Risk of higher combined costs – a hybrid setup has the potential to reduce costs but without proper optimization, it can do the opposite. You’re covering both on-prem expenses like hardware upkeep, power, and staffing, as well as cloud costs such as compute, storage, and data transfer. Without smart capacity planning, these combined expenses can surpass what you’d spend on a fully on-prem or fully cloud solution in the long run.
• Security policy alignment & compliance overhead – policy teams need to make sure that things like encryption keys, IAM roles, network ACLs, and audit logs are consistent across both on-prem and cloud environments. Managing governance in two places takes extra work, especially when trying to maintain uniform compliance with standards like SOC 2 or ISO 27001 across systems that don’t always play by the same rules.

Use your answers to each column as a simple scorecard. Whichever set of questions you answer “Yes” to most often points to the right model:
Mostly “Yes” on on-premises questions → choose on-premises
Mostly “Yes” on cloud questions → choose cloud
Mostly “Yes” on hybrid questions → choose hybrid
If your “Yes” answers are split across two columns, lean toward the model whose critical drivers (e.g., compliance vs. cost vs. performance) have the highest business impact.

Choosing a fitting solution – a real-life example from Fortitude Re

In 2024, Fortitude Re, a major life and annuity reinsurer, entered into a $450 million, 10-year partnership with IBM to overhaul its policy servicing operations using AI and automation. According to Insurance Business, IBM was brought on as the third-party administrator (TPA) through its subsidiary, IBM Insurance Solutions, supporting over 4 million policyholders. IBM stated the goal is to “enhance Fortitude Re’s third-party administration operations” while driving down operational costs.

Instead of managing everything on-premise, Fortitude Re went with a hybrid cloud model. Here’s what drove that decision:

• Faster rollout, less complexity – Fortitude Re was able to leverage IBM’s existing and regulation-ready TPA platform. That meant they didn’t have to build from the ground up, giving them a head start in rolling out AI-driven policy servicing capabilities.
• Higher-quality AI, with none of the upkeep – IBM’s cloud-native AI services (like Watson and Cloud Pak for Data) are continuously improved and optimized. This gave Fortitude Re access to state-of-the-art AI without having to manage model training, updates, or infrastructure on their own.
• Room to grow, with cost stability – the long-term structure of the deal allows Fortitude Re to scale their operations flexibly as policy volumes or AI use cases evolve. And because it’s a services agreement, infrastructure spending becomes a predictable operating expense rather than a fluctuating capital cost.
• Built-in compliance support – IBM offers a Cloud Security and Compliance Center, which helps manage risk and data protection across hybrid and mult-icloud environments. This was key for Fortitude Re, given the sensitive nature of insurance data.
• Access to expertise – finally, by partnering with IBM, Fortitude Re tapped into deep experience in global TPA services, which is something that would’ve taken significant time and resources to build in-house.

Making the right choice for AI in insurance – cloud vs hybrid vs on-premise

For many insurance companies, choosing the right AI solution can be a complex and challenging process. Balancing factors like compliance, costs, and scalability isn’t straightforward. That’s where Clurgo offers valuable support.

With extensive experience, our team can help you navigate your implementation and data hosting options, and select the best fit – whether that’s cloud, hybrid, or on-premise solutions. We’ll help make sure that your decisions are well-informed and aligned with your company’s long-term goals. Reach out to discuss your upcoming AI project and how we can help.