Diverse infrastructure and economic factors are at play as executives and IT leaders look to transition from AI experimentation and training to functional production systems. Capitalizing on these investments requires moving beyond model benchmarking to inferencing at scale; that is, using trained models to process new data and achieve practical business results in real time. Current research underscores the enormity of the transition, with inferencing on track to comprise more than half of all AI compute by 2030.

However, achieving success involves overcoming three primary challenges: latencies, consistent throughput and cost, along with other strategic hurdles (e.g., memory bandwidths, data drift, operational complexity, etc.). In this article, we explore key questions related to scaling inference workloads and the need to maintain consistent accuracy while delivering proven results. We also consider the primary best practices to reinforce long-term scalability.

Scaling AI inference for production workloads

Inferencing represents the operational phase of AI where previously trained models function within production environments and respond independently when presented with new input data in real-world contexts, whether that’s healthcare, finance, manufacturing or other industries. Unlike model training, which can demand specialized hardware and massive GPU clusters, inferencing is less compute-intensive. However, it requires lower latencies, steady compute and enduser/device proximity to operate effectively.

Today’s AI initiatives often involve multi-step agentic workflows in which compound AI architectures work together within orchestrated pipelines. Achieving desired business outcomes involves multiple interacting systems (e.g.., system 1, system 2) that work as one processing paradigm along with orchestration and verification features. For example, system one relies on components like model cascading for fast, patterned data retrieval; system two components perform complex planning and reasoning to execute real-world actions. 

Certain prerequisites are crucial to scaling inference and for ensuring 24/7 continuous uptime. In addition to balancing heavy traffic and managing compute memory, inference servers provide high throughput, dynamic batching and close proximity to use cases and population centers to automatically scale based on high or low traffic patterns. 

“Frequently accessed, latency-sensitive data should remain close to compute, while colder data can use more economical tiers,” says Anita Pandey, VP of Growth at Clockwork Systems. “Minimizing unnecessary copies and prioritizing latency-critical reads over background writes can reduce delay, power consumption and operational complexity,” she adds.

Of course, C-suite executives and IT leaders need to remain cognizant of the complexities involved with optimizing inference. In general, benchmarking inference performance offers a key tactic to measuring AI deployment success. However, it can also be exceedingly difficult to apply those benchmarks. While established metric standards exist (e.g., MLPerf), the process is complicated by highly unpredictable computational demand patterns and the interactions involved with retrieving and supplying data for agentic AI to function at scale in the real world. 

For example, matrix multiplications involve millions of tiny, frequent computations that take place across different processors. The key-value cache (i.e., short-term memory) is instrumental in accelerating those transactions by maintaining data in memory instead of constantly retrieving it from storage. The KV cache thus trades memory space for compute time and, by extension, reduces latencies — a critical differentiator for everything, from remote surgeries in healthcare to autonomous vehicles in transportation.

Choosing an AI inference platform and deployment model

Inferencing platforms (e.g., Bento, Vertex AI, AWS SageMaker, etc.) provide the baseline infrastructure for trained AI models to make predictions and execute actions. The next step is scaling those initiatives to deliver high-quality AI performance in a cost-effective manner to meet market demands. Once a company transitions from model training to inferencing, they have different choices for operationalizing those initiatives. The three options include cloud services, vendor supported platforms and open source tools, or a hybrid approach that combines aspects of each. 

Established cloud services (e.g., AWS, MS Azure, GCP, etc.) supply on-demand compute power and offer pre-built services for steady large-scale use cases. However, latencies can pose obstacles as agentic initiatives grow. For example, e-commerce companies that experience traffic spikes need to automatically scale product recommendations and dynamic pricing to meet millions of customer demands. Slow or delayed responses can cause customer churn that severely impacts an organization’s bottom line.

On the other hand, open source AI platforms (e.g., Hugging Face, Amazon SageMaker, Kubeflow, etc.) are customizable and self-directed. They offer integrations with open source tools, libraries and servers (e.g., vLLM, Ollama, llama.ccp, etc.) to provide more affordable pricing and capabilities for auditing/monitoring. For instance, companies in the insurance industry can instantly scale real-time analyses of claims data and process millions of documents and images while eliminating manual bottlenecks. However, open source platforms also entail significant operational and integration complexity. 

A hybrid approach offers both on-premises control and cloud service flexibility that enterprises can use to ensure data sovereignty, high performance and affordability. In the healthcare industry, hospitals can deploy an open source model on local, privacy-compliant clusters to handle common procedures, then escalate to a secure enterprise cloud for more complex diagnostic analyses. A hybrid combination ensures a more secure data environment for certain industries along with intelligent network routing for edge deployments at scale, such as in mining, construction and manufacturing. 

“As demand grows, the bottleneck can move from processors to memory, storage, networking, power or data movement. Inference is increasingly a distributed-systems challenge. Congestion, failures and poor scheduling can leave expensive resources underused even when headline utilization looks high. Leaders should distinguish throughput from goodput and averages from tail performance.” Pandey points out.

Best practices for reliable AI inference at scale

As enterprises take on the challenges of inferencing at scale, they’re looking to serve as many users, applications and geographies as possible. They’re also navigating a range of technical, operational and regulatory constraints with an eye toward data protection, decentralized deployments and the proper location of compute resources (e.g., on-premise, cloud, etc.). At the same time, C-suite executives and IT leaders still wrestle with software-level constraints, context portability and cloud-native tooling to track and maintain deployments.

Leaders also need to continuously promote and maintain best practices for maintaining inference elasticity. Once scaling framework parameters are addressed, undertaking best practices will help reinforce long-term viability. For example, a fundamental part of scalability is preventing vendor lock-in through hardware-agnostic pipeline designs to ensure future flexibility. And choosing a modular architecture in the early stages of model training ensures not only that the right capabilities are in place, but also the ability to pivot and meet new challenges. 

Pandey observes that “Executives should also plan for continuous change. Model architectures, context sizes and application patterns are evolving quickly, while infrastructure may remain in service for years. A design that is economical for today's assistant could become restrictive if it cannot support longer contexts, multi-step agents or different demand patterns.”

In addition to optimizing costs across edge and cloud environments, modularity ensures that enterprises have the versatility to mix different types of compute as needed for multi-stage processes. These flexible design options enable businesses to include diverse technologies, from accelerators and high-speed network fabrics to GPU cores with high-bandwidth memory and tensor accelerators.

Another important best practice is consistent monitoring through real-time dashboards (e.g., Prometheus, Grafana, Datadog, etc.) to measure delays, throughput and resource utilization for optimal inference scalability. Automation represents a key ingredient to ensure that proactive alerting and model-specific metrics are in place for performance tracking. And implementing a production-grade inference engine will help to master continuous batching, manage KV cache memory and monitor the prefill/decode phases.

Finally, it’s critical to deploy inference workloads in the proper locations to overcome latencies and meet the requirements of servers that process millions of requests and achieve the most optimal cost-benefit ratio. Similar oversight applies to centralized cloud resources, which need to balance high-throughput batching with GPU memory consumption. 

“Scaling should follow business value. Leaders need to prioritize use cases, forecast adoption and establish the unit economics of each service before expanding it broadly. Leaders shouldn’t overlook skills availability, application integration, model and data governance, resilience, supply-chain exposure or the organizational cost of fragmented environments. Observability is essential so teams can see where time, energy and money are consumed across the request path,”  Pandey states.