Building AI and LLM Inference in Your Environment? Be Aware of These Five Challenges
Building AI and LLM inference and integrating it in your environment are major initiatives, and for many organizations, the most significant undertaking since cloud migration. As such, it’s crucial to begin the journey with a full understanding of the decisions to be made, the challenges to overcome, and the pitfalls to be avoided along the way.
In our last blog, we talked about the possible deployment models for enterprise AI—on-prem, cloud, and hybrid—and how to make the right choice for your organizations. As our series continues, we’ll now turn our focus to the primary challenges you’ll face during your deployment and how they vary across each deployment model. At the end of this blog, you will have a better idea of which deployment model is the most appropriate for you.
- Infrastructure and scalability
- Latency and performance
- Data security and privacy
- Regulatory compliance
- Model management and integration with existing systems
Infrastructure Cost and Scalability
Challenge: AI and LLM inference require significant computational resources like GPUs/TPUs, memory, and storage as well as huge amount of power. The power requirements for very large-scale deployments are unprecedented and enterprises will have to hire a specialized skill set in AI to manage these environments.
On-premises: Enterprises must invest heavily in compute resources and upgrade their existing power and cooling infrastructure to ensure it is scalable to meet these new requirements. This presents a huge upfront cost and a risk of overspending.
Cloud: Cloud platforms offer a ready-made environment for AI which means enterprises do not have to deal with huge upfront cost. However, managing the cost while scaling up or down can be challenging and unpredictable at times particularly when workloads are not optimized. In addition, they will incur data ingress, data egress costs. Having a cloud-native solution may also mean enterprises will experience vendor lock-in.
Hybrid: Having a hybrid approach may make sense to most enterprises as they can optimize the cost and avoid vendor lock-in. However, a hybrid environment requires customers to be careful about orchestration to ensure it is seamless and does not result in bottlenecks.
Latency and Performance
Challenge: Real-time AI inference requires a high-performance environment that can necessitate edge processing and efficient data routing especially for real time applications like chatbots and recommendation systems. While data inspection is critical for security, it must not impose a latency or performance penalty.
On-premises: An on-prem deployment can offer low latency if the infrastructure is close to end-users but also requires hardware and software to be optimized to deliver high performance.
Cloud: Cloud deployments often face latency issues as data travels to and from remote servers. In addition, cloud providers that are struggling to meet rapidly rising AI demands often sacrifice latency for throughput. Enterprises may have to choose multi-region deployments to ensure the deployments are closer to end-users.
Hybrid: In any AI deployment model, resource-intensive workloads call for high-speed connections, load balancing/GSLB, and redundant infrastructure. A hybrid cloud model allows organizations to tune and optimize performance and availability more flexibly based on factors such as data locality, scalability, and cost.
Data Security and Privacy
Challenge: With the data-intensive nature, which includes handling the sensitive data of AI, the attack surface of an enterprise has exponentially increased and is increasingly vulnerable. As AI and LLM inference deployments become critical infrastructure for an organization, cyberattacks are increasingly targeting these environments with an intent to bring the system down, and steal sensitive information. Secondly, as more and more employees use AI for their daily tasks, there is a higher risk of users inadvertently uploading sensitive information to models, risking data leakage.
On-premises: Enterprises have greater control over data, which reduces the risk to a certain extent, but they will need to update and simplify their existing security tools with a platform-centric approach. An on-prem deployment of AI and LLM models can be more overwhelmed by a DDoS attack as most of the appliance-based solutions cannot scale to protect against multi-vector and volumetric DDoS attacks. An on-prem customer should work with a security vendor that not only has a hybrid solution for DDoS that can scale up to prevent any size of DDoS attacks, and one that can scale across to prevent AI-related threats such as prompt injections, data leakage, data and model poisoning and other OWASP Top 10 LLM threats.
Cloud: Enterprises looking to deploy AI in a fully cloud environment will have less control over their data and will have to address data residency requirements of various regulations like GDPR and HIPAA. These organizations can look at purchasing security services from the same cloud provider or from a third-party managed security service provider (MSSP), but careful vendor selection is key. It’s also important to have a clear understanding of the shared responsibility model. This model can become costly and complex over time.
Hybrid: This approach offers enterprises a balance of control and flexibility of the data. This model requires strong data governance and encryption to protect data flows between environments and must also ensure consistent security policies across cloud and on-prem environments. This model can potentially offer better ROI over time.
Regulatory Compliance
Challenge: Given the data-intensive nature of AI, it’s no surprise that regulatory compliance can be one of the biggest implementation challenges organizations face. Mandates like GDPR, CCPA, and the EU AI Act impose strict requirements in areas such as data governance, access controls, cybersecurity measures, data residency, privacy, user consent, and data deletion/correction. Beyond these baseline measures, AI and LLM deployments face additional compliance requirements including:
- Algorithmic accountability – ensuring ethics and non-bias in AI decision-making
- Transparency and explainability – disclosing and clearly explaining how the organization’s LLMs make decisions and what data they use
- Vendor management – assessing the compliance of third-party AI solutions or data sources incorporated into the system
On-premises: An on-prem AI deployment can facilitate compliance with greater control, localized data, and customized security protocols for industry-specific regulations. However, an on-prem AI or LLM system also requires substantial infrastructure investment and human expertise.
Cloud: A public cloud AI deployment can pose compliance hurdles. Companies must ensure that their cloud providers comply with relevant regulations and may need data processing agreements (DPAs) to clarify vendor roles and responsibilities. Data residency issues may also arise. On the other hand, while costs for public cloud compliance can add up over time, the process can become more operationally efficient.
Hybrid: A hybrid cloud AI deployment strikes a balance between control and flexibility, allowing organizations to address data residency requirements while still leveraging cloud capabilities. Compliance can become more challenging, however, as the distribution and movement of data between on-prem and cloud environments increases the surface area falling under regulatory mandates.
Management and Integration with Existing Systems
The impact of AI and LLM workloads on enterprise infrastructure networks brings significant management challenges, including:
- Decentralization – shifting from a centralized architecture to an edge-centric environment for faster processing and reduced latency
- Bandwidth management – ensuring that GenAI applications don’t saturate the network and degrade performance for other critical business applications
- Utilization management – balancing GPU/TPU usage for AI workloads while maintaining network performance for other applications
- Multi-cloud connectivity – providing data access across multiple environments for AI training and inference
- Integration – connecting AI and LLM systems to existing systems like legacy infrastructure
On-premises: Enterprises selecting an on-prem deployment will have better control over their data but it will require significant upfront investment in licensing, and may require additional human resources with specific skill sets. Selecting this model will, however, make it easier to integrate with existing legacy infrastructure.
Cloud: Public cloud offers optimal scalability and agility, but brings complications in terms of traffic management, data privacy and security and performance and cost-effectiveness at scale. But it will be harder to integrate with existing customer legacy infrastructure.
Hybrid: Hybrid cloud allows a balance of control and flexibility but also requires extensive integration, careful cost control and resource management across both cloud and on-prem environments.
While the challenges of implementing AI and LLMs can be significant, they can be mitigated by choosing the most suitable deployment model. In general, large enterprises or large service providers should explore an on-premises or hybrid approach, while smaller and midsize enterprises may do best with a hybrid or fully cloud-based deployment. A final decision should be based on careful consideration of the specific the needs, priorities, and resources of the organization.
As our blog series continues, we’ll explore key considerations and best practices for AI implementation in greater depth to help you move forward with confidence.
