Generative AI Assets Deployment Options in Accenture and Other System Integrators

When enterprises want to put generative AI into production, they rarely build from scratch alone. Many turn to a global system integrator (SI) such as Accenture to design, deploy, and operate the solution. Understanding generative AI assets deployment options in Accenture means understanding how SIs package their services, what deployment models they offer, and where the underlying GPU infrastructure actually comes from. This guide walks through the main SI deployment patterns, how they compare, and what to ask about the infrastructure layer underneath.

How SIs like Accenture package generative AI deployment

Large consulting firms and SIs have built practices around generative AI that wrap strategy, model customization, integration, and operations into structured offerings. Accenture, for example, has invested in dedicated generative AI services that span advisory work, model tuning, platform engineering, and managed operations. Similar patterns exist across Deloitte, Capgemini, and other global SIs.

The common thread is that the SI owns the delivery of the generative AI asset, but the infrastructure that runs inference and training almost always comes from a third-party cloud or GPU provider. The SI brings methodology, talent, and integration. The cloud provider brings compute. Knowing where one ends and the other begins is the first step in evaluating any SI proposal.

The three main SI deployment models for generative AI assets

Most SI-led generative AI deployments fall into one of three models. Each differs in how much the client owns versus how much the SI operates, and each has different cost and control tradeoffs. The generative AI deployment model you pick shapes everything downstream: who pays for idle compute, who holds the model weights, and how hard it is to switch providers later.

1. SI-managed deployment: The SI builds the generative AI asset and continues to operate it on a cloud platform, often under a multi-year managed services contract. The client consumes the output (APIs, applications, dashboards) and pays the SI for both delivery and ongoing operations.
2. Co-built deployment: The SI and client team work together to design and deploy the asset. The SI provides architecture, implementation, and knowledge transfer, then hands over operations to the client's internal team once the solution is stable.
3. Self-built with SI advisory: The client builds and owns the full stack but uses the SI for strategy, architecture review, or specialized work such as model fine-tuning. Infrastructure is procured directly from a cloud or GPU provider.

SI-managed deployment: when the integrator runs the show

In an SI-managed model, the consulting firm takes end-to-end responsibility for the generative AI deployment. They select the model, tune it, integrate it with enterprise systems, and run it on cloud infrastructure they provision and control. This works well for organizations that want a finished capability without building an internal AI engineering team.

The tradeoff is cost and lock-in. Managed services contracts from global SIs carry premium pricing, and switching providers mid-contract can be difficult because the SI controls the deployment, the model weights, and the infrastructure relationships. If you're evaluating generative ai deployment through this lens, ask who owns the fine-tuned model weights and what happens to the infrastructure contract if you switch integrators.

Co-built deployment: shared risk, shared ownership

Co-built deployments split the work. The SI brings architecture expertise and accelerators (reference architectures, tested deployment patterns, integration templates). The client team learns the stack during delivery so they can operate it independently afterward. This model suits organizations that want to build internal capability but need an experienced partner to shorten time to production.

The handover point matters. A clear handover means the client team understands the inference pipeline, the monitoring setup, and the infrastructure provisioning. A vague handover means the SI stays involved longer than planned.

Self-built with advisory: maximum control

Some enterprises choose to build the entire generative AI stack internally, using an SI only for targeted advisory. The client procures GPU infrastructure directly, selects and fine-tunes models, and handles integration. The SI might provide a two-week architecture sprint or a model evaluation framework, then steps back.

This path gives the most control and the lowest long-term cost per unit of work, but it requires a capable internal team. Organizations without experienced MLOps engineers tend to underestimate the operational burden of running production inference, which is why many start here and shift to co-built after hitting scaling problems.

Comparing SI deployment options side by side

The table below compares the three models on the dimensions that matter most for planning.

Dimension	SI-managed	Co-built	Self-built + advisory
Client ownership of model weights	Often SI-held	Shared at handover	Fully client-owned
Time to production	3-9 months	4-12 months	6-18 months
Ongoing SI cost	High (managed services fee, 15-25% of project)	Medium (delivery + handover)	Low (advisory only, 5-10% of project)
Internal team required	Minimal	Moderate (5-10 people)	Large (10+ people)
Infrastructure control	SI selects and manages	Joint decision, client operates	Client selects and manages
Switching cost	High	Medium	Low

Where the infrastructure layer fits in every SI model

Regardless of which SI deployment model you choose, the generative AI asset needs somewhere to run. This is the layer that SIs rarely build themselves. They provision infrastructure from cloud providers, GPU cloud platforms, or on-premises clusters. The SI configures and manages it, but the compute itself comes from an infrastructure provider. A well-architected generative AI deployment treats this layer as a first-class decision, not an afterthought.

This matters because the infrastructure layer determines three things that directly affect your total cost and performance.

• Unit economics: The per-GPU-hour rate and billing model (per-hour rental vs per-request serverless) set the baseline cost of every inference call and every training run. A 30 percent difference in hourly rate translates directly into a 30 percent difference in your SI project's infrastructure bill.
• Scalability: Whether the infrastructure can scale to zero during quiet periods or requires reserved capacity shapes how much you spend on idle compute. Serverless inference that scales to zero can cut infrastructure cost dramatically for workloads with variable traffic.
• Compliance: Enterprises in regulated industries need SOC 2, ISO 27001, and data residency guarantees. The SI's methodology does not substitute for the infrastructure provider's compliance certifications. Both layers need to meet the bar.

What to ask your SI about the infrastructure layer

When an SI proposes a generative AI deployment, the infrastructure decisions are often buried in the statement of work. Here are the questions that surface them.

1. Who provisions the GPU infrastructure, and under whose account does it run? If the SI provisions under their own account, you pay their markup. If they provision under your account, you see the raw infrastructure cost.
2. What is the per-GPU-hour rate, and is it per-hour rental or per-request serverless? The billing model should match your traffic pattern, not the SI's preferred procurement model.
3. Can the infrastructure scale to zero? If you're running a generative AI workload with variable traffic, paying for reserved capacity 24/7 wastes money during idle hours.
4. What compliance certifications does the infrastructure provider hold? SOC 2 and ISO 27001 are table stakes for enterprise workloads. Do not accept a provider that cannot document them.
5. Who owns the fine-tuned model weights if you switch SIs? If the SI holds the weights, switching cost is high. If you hold them, you can move to a different integrator or bring operations in-house.

GMI Cloud as the inference infrastructure behind SI projects

GMI Cloud is an AI-native inference cloud built for production AI. For SI-led generative AI deployments, the infrastructure layer is where GMI Cloud fits. Whether the SI chooses a managed model, a co-built model, or a self-built approach, GMI Cloud provides the GPU compute, the inference engine, and the compliance posture that enterprise generative AI deployment requires.

GMI Cloud holds SOC 2 and ISO 27001 certifications, which means the infrastructure layer meets the compliance bar that SIs need to sign off on enterprise deployments. The platform offers 99.99 percent availability and operates GPU regions across North America, Europe, and Asia-Pacific, with under 200ms average cross-region latency. For SI teams that need to deploy generative AI assets across multiple geographies, that coverage matters.

The platform's two engines map onto the deployment patterns SIs use most. The Inference Engine provides serverless Model-as-a-Service with scale-to-zero billing, which fits SI-managed deployments where traffic is variable and the SI wants to avoid idle infrastructure cost. The Cluster Engine provides bare metal GPU, container service, and managed cluster options for co-built or self-built deployments where the client or SI needs root access and full control over the stack.

NVIDIA H100 GPUs start at $2.00 per GPU-hour, H200 at $2.60, B200 at $4.00, and GB200 NVL72 at $8.00. SI teams can start on demand, move to dedicated capacity as the workload stabilizes, and apply commitment-based savings for sustained deployments. You can review current rates on the GMI Cloud pricing page and deploy from the console.

Matching the SI model to the infrastructure model

The deployment model an SI recommends should align with the infrastructure that supports it. An SI-managed deployment with variable traffic pairs well with serverless inference that scales to zero, so the client does not pay for idle GPU hours during quiet periods. A co-built deployment with steady training workloads pairs well with bare metal GPU rental at a fixed hourly rate, which gives predictable cost and full throughput with no hypervisor overhead.

The generative ai deployment decision is not just about which SI to hire or which model to fine-tune. It is about aligning three layers: the SI delivery model, the infrastructure billing model, and the workload's traffic pattern. When those three align, the deployment runs efficiently. When they don't, the client pays for idle capacity, premature commitments, or infrastructure that doesn't match the workload. Generative AI assets deployment options in Accenture and similar SIs succeed when the infrastructure layer is chosen with the same scrutiny as the consulting engagement.

Choose the SI model, then scrutinize the infrastructure

Start by defining your deployment model: managed, co-built, or self-built. Then ask the SI hard questions about the infrastructure layer. Who provisions it? What is the per-GPU-hour rate? Does it scale to zero? Who owns the weights? The SI brings the delivery expertise, but the infrastructure determines your unit economics for as long as the asset runs. Get both layers right, and your generative AI deployment will have the cost structure and operational flexibility to scale beyond the initial engagement.