AI infrastructure for enterprise connects the computing, data, models, and operational controls required to run AI applications across business workflows. When these components are planned separately, a promising pilot can become slow, expensive, or difficult to govern as more users and systems depend on it.
At SotaTek, we help organizations develop and integrate AI applications around their existing data, business systems, and deployment requirements. This guide explains how to match infrastructure to the workload, choose where components should run, and establish the controls needed for production.
Key Takeaways
- Begin with the business workflow, data boundaries, and service objectives before selecting hardware or a cloud platform.
- Plan interactive inference, retrieval, batch processing, and model development as different workloads with different resource needs.
- Connect infrastructure layers through managed identities, documented interfaces, observability, and clear ownership.
- Evaluate cloud, private, hybrid, and edge deployment against total cost, data location, operating capability, and recovery requirements.
- Measure usable business outcomes alongside latency, availability, security, and resource consumption.
What Is AI Infrastructure for Enterprise?
AI infrastructure for enterprise is the foundation used to build, deploy, operate, and improve AI applications. It includes compute and accelerators, data pipelines, storage, networking, model-serving software, application integrations, and the people and processes that manage them.
An employee asking a knowledge assistant a question triggers more than a model call. The application authenticates the user, retrieves permitted documents, sends selected context to a model, returns an answer, and records operational information. If any component is unavailable, slow, or incorrectly configured, the user experience and the organization's risk can change. Understanding this full path is the first step in designing AI infrastructure for enterprise workflows.
Infrastructure for AI versus AI for infrastructure
Infrastructure for AI provides resources and services that AI applications consume. AI for infrastructure uses AI to help operate IT environments. For example, to summarize incidents or support root-cause analysis. The two can work together, but this guide focuses on the first: designing the foundation that enterprise AI workloads need.
An enterprise may need a shared platform for several use cases while keeping their data, quotas, and deployment policies separate. Our guide to enterprise AI architecture covers how these shared layers connect to applications and governance.
Key Layers of AI Infrastructure
The layers of AI infrastructure for enterprise are interdependent. More GPU capacity, for example, will not resolve a retrieval bottleneck or an unavailable identity provider. Each layer needs a defined purpose, interface, owner, and recovery plan.
| Layer | Main components | What to decide |
| Compute | CPUs, GPUs, accelerators, memory, and runtime environments. | Which workload needs acceleration and how much capacity must be reserved? |
| Data and storage | Source systems, object storage, databases, indexes, and backups. | Which data is needed, where does it reside, and how current must it be? |
| Network | Connectivity between applications, models, data, and external services. | What latency, bandwidth, private connectivity, and egress controls are required? |
| Model serving | Model registry, inference endpoints, gateways, and queues. | Which model versions are approved and how are they deployed or rolled back? |
| Orchestration | Containers, schedulers, workflow engines, and scaling policies. | How are workloads prioritized, isolated, and recovered? |
| Security and operations | Identity, policy, secrets, telemetry, evaluation, and cost allocation. | Who can use each resource, and who is accountable for its behavior? |
Start with a service contract
For each use case, document its users, task, source systems, permitted actions, sensitivity, availability target, and acceptable response time. Define a named owner for the application, the data, the model endpoint, and incident response. This contract gives platform teams a basis for choosing capacity and makes trade-offs in AI infrastructure for enterprise workloads visible before procurement.
For a discussion of deployment decisions across existing environments, see our guides to hybrid cloud and public, private, and hybrid cloud models.
Match Infrastructure to AI Workloads
A single platform may support several workloads, but each puts pressure on a different part of the stack. Classifying them helps avoid sizing AI infrastructure for enterprise around the needs of one pilot.
| Workload | Resource pattern | What matters most |
| Interactive inference | Requests arrive unpredictably and need a timely response. | Queueing, model memory, latency, concurrency, and available replicas. |
| Retrieval-augmented generation (RAG) | Documents are processed into an index; queries retrieve context before inference. | Ingestion speed, index freshness, access controls, retrieval quality, and model latency. |
| Batch extraction | Large volumes are processed against a completion deadline. | Throughput, queues, retry behavior, cost per accepted record, and downstream capacity. |
| Fine-tuning or training | Accelerator-intensive jobs run for a defined period. | GPU memory, interconnects, checkpoints, data throughput, and job scheduling. |
| Edge inference | Models operate near a device, site, or production line. | Local latency, device resources, connectivity, update mechanisms, and physical security. |
Estimate model memory before purchasing capacity
NVIDIA's GPU memory guidance breaks inference memory into model weights, the key-value (KV) cache, activations, communication buffers, and runtime overhead. Its weight estimate is parameter count multiplied by bytes per parameter, divided by the tensor-parallel degree when applicable.
For example, an 8-billion-parameter model in BF16 has approximately 16 GB of weights: 8 billion parameters multiplied by two bytes. That is a starting estimate, not the memory required for the complete service. Context length, concurrent sessions, KV cache, runtime allocations, and model architecture add to the requirement. Validate the actual model, precision, and serving engine on the intended hardware before finalizing AI infrastructure for enterprise use.
Case study: Capacity separates real-time and offline work
In a 2025 Microsoft customer story, enterprise search provider Capacity describes using Phi small language models for both query processing and offline content enrichment. Its stack includes Azure Kubernetes Service, Azure AI Search, PostgreSQL, and services for language and video processing. Separating preprocessing from query-time work helps the company prepare searchable metadata ahead of a user request.
Microsoft reports that a summarization task decreased from 12–14 seconds to 4–5 seconds in the described implementation. The figures are vendor-reported results for that workflow, not a universal model or infrastructure benchmark. The practical lesson is to move suitable enrichment work out of the interactive path, then measure whether quality and latency remain acceptable.
Choose a Deployment Model
AI infrastructure for enterprise can combine managed model APIs, rented accelerators, private environments, and edge devices. The placement decision should follow the workload's data, demand, integration, and operating constraints.
| Deployment | Good fit | Questions to resolve |
| Managed cloud or model API | Early adoption, variable demand, or teams that prefer managed operations. | What are the provider's quotas, data terms, regional options, and availability commitments? |
| On-premises or private cloud | Workloads requiring a controlled environment or predictable local integration. | Can the organization supply capacity, skills, patching, power, cooling, and support? |
| Hybrid | Workloads whose data, applications, or models must operate across environments. | How will identity, networking, monitoring, and incident ownership work across boundaries? |
| Edge | Use cases requiring local responses or resilience to weak connectivity. | How will devices be secured, updated, monitored, and recovered remotely? |
Compare total cost, not only accelerator price
Include compute, model or API usage, storage, networking, software, monitoring, support, staffing, redundancy, and equipment lifecycle costs. For private infrastructure, account for idle capacity and procurement lead time. For cloud services, model sustained and peak usage, data-transfer costs, quota increases, and provider dependencies.
Treat the calculation as a scenario analysis, not a fixed rule. A model used occasionally by a small team has a different cost profile from a high-volume service with stable demand and strict data-location requirements. When planning AI infrastructure for enterprise use, revisit placement as utilization, model size, or contract terms change.
Design a Production AI Infrastructure Architecture
A production architecture should show where requests enter, how data is retrieved, which model handles each task, and where authorization is enforced. In AI infrastructure for enterprise knowledge assistants, the path might look like this:
1. The application authenticates the user and passes verified identity and tenant context to trusted services.
2. A retrieval service searches an index using the user's permitted scope and returns authorized content.
3. An application orchestrator sends the selected context to an approved model through a gateway or service endpoint.
4. The response is validated and returned; proposed tool actions pass through a separate authorization decision.
5. Monitoring records latency, failures, policy decisions, and cost with appropriate controls on sensitive data.
How Microsoft explains the model gateway
Microsoft's gateway architecture places a gateway between applications and model endpoints. Its conceptual design groups federated authentication, rate limiting, routing, monitoring, caching, load balancing, and queues. It can route traffic to model deployments in different locations, including on-premises environments.
| Gateway function | What the source describes | What the enterprise must decide |
| Authentication and rate limits | Centralize selected access and traffic controls. | Which users and applications receive which quotas? |
| Routing and load balancing | Direct requests to appropriate, healthy model endpoints. | Which models, regions, and fallbacks are approved for each data class? |
| Queueing | Send suitable lower-priority work through a queue. | Which tasks can wait, and how are failures or duplicate jobs handled? |
| Monitoring | Observe usage and cost across models. | Which metrics, logs, budgets, and owners are required? |
| Caching | Reuse eligible results where appropriate. | How are identity, data freshness, and permissions checked on reuse? |
Microsoft also warns that a gateway can become a single point of failure and may affect streaming or stateful interactions. Design for availability, test long-lived connections, and include the gateway in failure-mode analysis. When a gateway supports AI infrastructure for enterprise applications, it does not automatically enforce source-document permissions or authorize downstream business actions.
Connect models across environments
In its networking reference for AI inference, Google Cloud describes a unified frontend that routes requests by model name to backends hosted in different environments. Its example uses a private endpoint, an internal load balancer, API management, and configurable safety checks. The design also notes that incompatible backend APIs require translation, which is outside the reference implementation.
For a hybrid implementation, map each request's route, identity, encryption, approved destination, and network cost. A single interface can simplify consumption, but teams still need to understand which provider processes each request and what happens when a backend fails. These decisions connect network design with AI infrastructure for enterprise integration. Our guide to AI integration with ERP and CRM provides additional context for connected workflows.
Secure and Govern Enterprise AI Infrastructure
Security for AI infrastructure for enterprise spans users, data, models, tools, and operations. A network boundary helps restrict connectivity, while application and resource-level authorization determine whether a particular request is allowed.
Control access throughout the workflow
Use separate identities for users, applications, and agents; grant only the permissions needed for the task. Protect credentials outside prompts, isolate tenants or workspaces as appropriate, and enforce current document access before retrieved content reaches the model. For actions such as editing records or sending data externally, validate the requested operation and require approval when the impact warrants it.
Model weights, container images, dependencies, and deployment configurations also require ownership and change control. For AI infrastructure for enterprise workloads, assess external provider retention and processing terms, and decide which logs may contain prompts or business data. Our enterprise AI governance guide explores the related organizational responsibilities.
Design for recovery and evidence
Test model endpoint outages, expired credentials, source-system failure, access revocation, and stale indexes. Define how the system behaves when its authorization service is unavailable and how teams restore the model version, configuration, index, and application together.
Instrument the request path with a correlation ID and record the responsible application, model version, decision, latency, error, and estimated cost where available. Avoid capturing sensitive prompts or full documents by default. Agree on retention, access, and incident escalation before AI infrastructure for enterprise users goes live.
Plan Capacity, Cost, and Reliability
Capacity planning for AI infrastructure for enterprise begins with a representative workload. Capture request volume and peaks, input and output length, concurrency, response-time objectives, data freshness, and the time required for a new replica to become ready. Test a realistic mix rather than a single short prompt.
Worked example: estimate serving replicas
Suppose a representative test shows that one replica can sustain two requests per second while meeting the agreed latency and quality targets. Forecast peak arrival is eight requests per second, and the team chooses a planning target of 70% of that measured rate per replica to leave headroom.
| Calculation | Result | Meaning |
| 2 requests/second × 70% | 1.4 requests/second per replica | Planning capacity, not a vendor guarantee. |
| 8 ÷ 1.4, rounded up | 6 replicas | Initial estimate for peak demand. |
| 6 active replicas + 1 spare | 7 provisioned replicas | Capacity to retain six if one replica fails, subject to routing and placement. |
This is illustrative arithmetic, not a production sizing recommendation. Requests with longer contexts or outputs may need more capacity; queueing, burstiness, model warm-up, quotas, network delays, and GPU availability also affect the result. Validate the estimate with load tests, failure tests, and the actual service-level objective before committing infrastructure.
Measure cost per useful outcome
Track time to first token, end-to-end latency, queue time, error rate, retrieval quality, and availability alongside infrastructure and model costs. Allocate shared costs consistently and include failed attempts, human review, data transfer, and storage when evaluating AI infrastructure for enterprise workflows.
For example, a cheaper model response can increase total cost if poor extraction leads to repeated calls or manual correction. Compare cost per request with cost per accepted task and task quality; use the result to decide whether a routing, model, or infrastructure change is worthwhile.
Case study: Observe.AI scales managed inference
In an AWS customer story, Observe.AI describes using Amazon Bedrock and Amazon Nova Lite for high-volume contact-center AI workloads. The managed model path was integrated into existing services to support both batch and latency-sensitive use cases while reducing part of the model-management burden.
AWS reports approximately 90,000 requests and 100 million tokens per minute across regions, together with 10–15 times growth in model consumption over roughly one year. These figures describe the reported deployment and do not establish the performance or cost of another system. The example illustrates a managed-inference choice at scale; it does not imply that every workload should use the same model or provider.
How SotaTek Build AI Infrastructure for Enterprise
At SotaTek, we help organizations connect AI applications with the data, infrastructure, and business systems they need. Our work on AI infrastructure for enterprise use cases can include assessing workloads, designing architecture, developing AI applications and agents, integrating enterprise systems, and planning deployment and operations with the client's teams.
Case study: Deploying Sotabox on GreenNode
Overview and challenge: As we prepared Sotabox.ai for cloud deployment, we needed to establish an operational environment within one month while supporting the platform’s rollout to end users. Our team also wanted to reduce infrastructure management effort through platform-as-a-service (PaaS) components and maintain access to technical support during the initial deployment.
Deployment and solution: Working with GreenNode, formerly VNG Cloud, we configured vServer compute, vStorage, a load balancer, managed Redis and PostgreSQL databases, and Kafka. GreenNode provided technical support during setup, integration, and testing, including work involving vMonitor and VKS. Once the core environment was operating, we extended Sotabox features to end users. The teams also tested Dataiku integration on the existing infrastructure as a separate initiative.
The following example shows how these components could support a document-based AI application:
- Document submission: A user uploads a document through an application endpoint behind a load balancer.
- File storage: The application service stores the document in object storage.
- Task creation: The application publishes a processing task to a queue.
- Document processing: A worker receives and processes the queued task.
- Supporting services: A database stores application metadata, while an optional cache can speed up access to frequently requested information.
- Monitoring: A monitoring service tracks system health and detects failures.
Results: GreenNode reports that the deployment ran from February 15 to March 15, 2025, and that DevOps costs decreased by 30%. The source does not state the baseline, cost components, or measurement method for that percentage. It also reports that managed services reduced operational effort; these figures do not independently establish inference performance, security effectiveness, or a guarantee for other implementations.
Infrastructure takeaway: A phased rollout can establish core compute, storage, networking, and managed services before expanding access to users. For a comparable project, teams should validate capacity, data access, observability, and recovery requirements against the actual workload before selecting the final architecture.
From assessment to operation
We start with a bounded use case and its data and integration requirements. The next steps are to select a deployment approach, build the required data and model path, define access and operating controls, and validate performance and quality using representative conditions. Once the workflow meets its agreed criteria, teams can expand AI infrastructure for enterprise applications and shared services in stages.
Our AI development services support this process. For related implementation decisions, explore our guides on:
- AI Agent Development Process: 8 Steps From Use Case to Production
- Top 10 AI Development Partners in Vietnam for 2026
Conclusion
AI infrastructure for enterprise becomes effective when compute, data, model serving, integration, security, and operations are designed around measurable business workloads. Start with a clear service objective, test the complete request path, and make ownership, cost, and recovery part of the architecture from the beginning.
At SotaTek, we help enterprises develop and integrate AI applications with a production path suited to their environments. Contact us to discuss your use case, infrastructure constraints, and implementation priorities.
https://www.sotatek.com/ 
(+84)2466585248 
