What counts as AI infrastructure? A guide for production workloads

Most definitions of AI infrastructure are component lists. They inventory GPUs, object storage, networking switches, and software frameworks without answering the question that matters to architects and senior engineers: What infrastructure do you need for AI workloads to function reliably at scale?

This guide takes a different approach. It describes AI infrastructure as a system of performance and reliability contracts, a stack in which each layer makes commitments to the layers above it, and where failure at any layer propagates upward in ways that are predictable, detectable, and preventable when the architecture is understood. It covers the consensus definition, key components, and the operational data tier. It addresses the architectural divide between training and inference. And it closes with a framework for evaluating infrastructure against the demands of production workloads.

What is AI infrastructure?

AI infrastructure is the integrated set of hardware, software, networking, and data systems required to build, deploy, and operate AI models. Infrastructure must support the full AI lifecycle: data ingestion and preparation, model training, deployment, real-time inference, and the continuous improvement cycles that follow. Each stage places different, sometimes competing, demands on the underlying stack.

What separates AI infrastructure from general IT infrastructure is more than the presence of GPUs. Here are some other differences.

Hardware and software, together

AI infrastructure is not a collection of discrete components that can be evaluated in isolation, but a tightly coupled system in which the performance characteristics of each layer constrain what is possible at every other layer. High-throughput GPU clusters are only as useful as the networking fabric that moves data between them without becoming a bottleneck. That networking fabric is only as useful as the storage layer that feeds it at the required rate. Storage is only as useful as the data management systems that serve the right data at the right latency to the right process.

This coupling is one of the defining characteristics of production AI infrastructure. General IT infrastructure often tolerates component-level optimization because the dependencies between layers are loose. AI workloads, especially distributed training and real-time inference, require system-level co-design.¹ 

How AI infrastructure differs from traditional IT infrastructure

Traditional IT infrastructure is built around CPU-centric general processing. CPUs execute instructions sequentially and excel at tasks requiring complex logic, low-latency memory access, and fine-grained control flow. They are well-suited to transactional workloads, web serving, and business logic, where fast sequential processing and keeping processor caches synchronized are especially important. 

However, AI workloads require parallel processing at a scale that CPUs cannot provide efficiently. Today’s GPUs contain thousands of smaller, simpler cores optimized for the simultaneous execution of thousands of arithmetic operations. AI training, like training a large language model, involves trillions of floating-point multiplications that must be parallelized across thousands of these cores.  The architectural difference represents a different computational model.

Traditional internet infrastructure was built by buying CPUs, memory, and hard drives that were cost-efficient but unreliable, and then building software systems to mask failures. AI infrastructure inverts this model: it requires a high-performance computational system consisting of hundreds or even thousands of powerful GPUs in which the coupling is tight, failures are not abstracted away, and the software stack is optimized to extract maximum performance from the specialized hardware rather than to tolerate hardware limitations.²

The AI lifecycle that this infrastructure must support

AI infrastructure must serve five lifecycle stages, each with different requirements.

1. Data ingestion and preparation. Raw data must be collected, cleaned, labeled, and transformed into formats suitable for training. This stage requires high-throughput batch processing, distributed storage capable of handling petabyte-scale datasets, and data pipeline orchestration that manages complex dependencies.

2. Model training. Training large models requires sustained GPU utilization across clusters of thousands of accelerators, fault tolerance mechanisms that recover gracefully from hardware failures, and high-bandwidth networking to synchronize model weights across nodes.

3. Deployment. A trained model must be packaged, versioned, and made accessible to inference systems. This stage requires model registries, containerization, and orchestration infrastructure.

4. Inference. The stage that generates business value, real-time inference, places different demands on infrastructure than training does, because it requires sub-millisecond response latency, the ability to handle bursty and unpredictable traffic patterns, and consistent performance regardless of concurrent load.

5. Continuous improvement. Models in production require monitoring, retraining, and updating. Infrastructure must support feedback loops that feed production signals back into training pipelines without disrupting live serving.

AI infrastructure guide: The core components

Before changing a standard system or approach, you need to understand why it was designed that way.  Here are the five major component categories in building AI infrastructure. 

1. Compute: GPUs, TPUs, and AI accelerators

The GPU is the defining hardware component of AI infrastructure. GPUs were originally designed for rendering graphics, which requires executing the same operations, such as matrix transformations, dot products, and interpolations, on millions of data points simultaneously. That parallelism turns out to be what neural network training also requires. The computation necessary to perform advanced reasoning inference is now 100 times greater than what was required for one-shot inference when ChatGPT first launched.³

Beyond GPUs, tensor processing units (TPUs) represent purpose-built AI accelerators. Accelerators optimized for training are designed for large-batch throughput, with sustained computation over hours or days. Accelerators optimized for inference are designed to return results quickly and predictably, even when many requests are being processed at the same time.

MLCommons' MLPerf Inference benchmarks, the industry's most widely adopted framework for measuring inference performance, distinguish between offline batch processing, which emphasizes throughput; server-mode inference, which serves request streams within latency targets; and single-stream inference, which reduces the latency of individual requests.⁴ Performance characteristics that produce a strong score in one scenario may degrade performance in another.

2. Storage, from training datasets to real-time serving

Storage for AI workloads has two components. 

Training storage handles large datasets quickly. A distributed file system needs to feed hundreds of GPUs simultaneously. 

Inference storage is random-access, not sequential. A model serving requests needs to retrieve embeddings, feature vectors, session state, or user context in response to individual queries, not read large sequential batches. Latency requirements change from aggregate throughput to individual read latency, measured at the tail, under concurrent load.

3. Networking: Bandwidth, latency, and interconnects

In distributed training, the network is a primary performance constraint. When thousands of GPUs train a model in parallel, they must synchronize gradients, the numerical updates that move the model toward its objective, after every computational step. The speed of this synchronization determines how fast training proceeds. A network that introduces latency or packet loss during gradient synchronization slows a training run by hours or days.

Two network architectures compete in high-performance AI clusters: InfiniBand and high-speed Ethernet. InfiniBand provides lower latency and higher per-port bandwidth, but at greater cost and with more complex operations requirements. RDMA over Converged Ethernet (RoCE) helps high-speed Ethernet infrastructure perform nearly as fast as InfiniBand for AI workloads.

The architectural lesson is that networking made for training clusters does not necessarily work well as inference infrastructure. Inference serving does not require the same all-to-all gradient synchronization patterns that make InfiniBand important for training. Inference networking requires consistently low latency from the client to the serving layer, with enough bandwidth to handle burst traffic without queuing delays.

4. Software frameworks and orchestration

Hardware capabilities become production systems through software. The primary training frameworks, PyTorch and TensorFlow, help engineers define models, specify training objectives, and distribute computation across GPU clusters without writing low-level code for each operation.

Kubernetes has become the dominant orchestration layer for AI-serving infrastructure. It manages container scheduling, resource allocation, scaling, and health monitoring across clusters. MLOps tooling, including model registries, experiment tracking systems, feature stores, and serving frameworks, sits above Kubernetes, providing the workflow infrastructure that makes training and deployment repeatable and auditable.

The operational layer isn’t always given the attention it needs in infrastructure discussions. Hardware and networking dominate the conversation because they cost the most. But teams spend more engineering time running the software layer than managing hardware. The consistency of software configuration, such as framework versions, CUDA library compatibility, container images, and model registry state, affects the reproducibility of training runs and the reliability of inference serving.

5. Cooling and power, the physical constraints

GPU power consumption has increased with each accelerator generation. The NVIDIA A100 required approximately 400 watts per card in 2020. The H100 required 700 watts in 2022. Blackwell-generation cards require up to 1,200 watts. Industry roadmaps project chips requiring 2,000 watts or more within the next two to three years.

With increased power densities, traditional air cooling isn’t enough. Air-cooled data centers typically manage up to 20 kilowatts per rack. A rack of Blackwell GPUs requires 100 kilowatts or more.⁵ NVIDIA's reference architectures for Blackwell-based deployments specify direct liquid cooling as the default.⁶

Cooling and power planning can no longer be deferred to facilities teams after hardware is specified. Power density, cooling architecture, and power distribution unit capacity must be co-designed with processing specifications from the outset. 

The missing layer: The operational data tier

Let’s talk more about the operational data tier between the raw storage layer and the model serving layer. This is the layer that serves the data models' needs at inference time, such as embeddings, session state, user context, and feature vectors, with the latency and consistency production inference requires.

Understanding why this layer is separate from storage explains why so many AI deployments encounter performance problems that throwing more processing at it doesn’t solve.

Storage is not the same as the data tier

Object storage systems, such as storage systems that work with the same API used by Amazon Web Services, Amazon S3, data lakes, and distributed file systems, are designed for durability and throughput. They store large volumes of data reliably and serve it quickly. They are excellent for training datasets, model checkpoints, log archives, and any workload that reads large sequential batches.

Inference workloads require something different. When a model receives a request, it needs to retrieve specific records, such as a user's session state, a set of relevant embeddings, or a cached feature vector within microseconds. Object storage is not designed for this. It is optimized for large sequential reads, not low-latency random access under high concurrency. Introducing object storage into an inference pipeline creates a performance architecture that cannot meet production SLAs.

This is what the operational data tier is for. It is a data system designed for sub-millisecond reads, high write concurrency, and consistency guarantees under load.

What the data tier does

Production AI inference requires four things from the operational data tier. 

1. Latency at the tail. Average latency is not a useful production metric. A data system that serves 99% of requests in 2 milliseconds but takes 200 milliseconds for the remaining 1% means many users will see delays, because at scale, 1% of requests is still a lot of requests. The relevant metric is p99, or the latency threshold 99% of requests fall below. For inference data access, that threshold is typically measured in single-digit milliseconds or less.

2. Throughput under burst. AI application traffic is rarely smooth. User events cluster in time, such as morning peak traffic, viral content spikes, or coordinated batch processing, which produces sudden demand increases up to 10 times more than normal. A data tier that performs well at steady-state load but degrades under burst conditions fails when the application needs it most.

3. Consistency under concurrent writes. Inference systems often need to both read and update state during a request. A model may need to read a user's session history, generate a response, and write an updated session record, all within the latency budget of one API call. Systems that cannot provide consistent reads after writes, or that require application-level workarounds to prevent stale reads, introduce correctness problems that are difficult to detect and debug.

4. High availability while maintaining performance. Redundant systems that maintain availability by accepting degraded performance under failover conditions don’t work well. Production systems need availability guarantees that hold under node failures without sacrificing the latency and throughput characteristics that applications depend on.

Where the data tier breaks in production

When the live data storage layer is not clearly designed, or when teams try to replace it with object storage or caching systems, specific types of problems appear.

1. Cache stampedes occur when many concurrent requests simultaneously encounter a cache miss for the same key and attempt to regenerate the cached value at the same time. Sometimes called the “thundering herd” problem, this causes a self-reinforcing load spike that brings down the underlying data system. At the AI inference scale, where thousands of requests per second may be trying to gain access to the same popular content or user context, cache stampedes occur frequently and create big problems.

2. Consistency drift happens when the operational data tier cannot guarantee that a read operation reflects the most recent write. In stateful applications such as personalization systems, recommendation engines, and fraud detection pipelines, stale reads produce incorrect model inputs that make responses to users worse but are hard to diagnose.

3. Tail latency amplification is perhaps the biggest problem. Even rare performance problems affect a significant fraction of all requests in systems that combine results from multiple distributed components.⁷ An AI system that makes multiple calls to a data tier will see its p99 latency approach the worst-case latency of the slowest individual call. If the mean latency figure is acceptable, but the p99 figure is not, the problem is usually tail amplification.

Sometimes teams try to fix these problems with workarounds rather than fixing the underlying infrastructure. Retry loops, circuit breakers, staggered cache refreshes, and read-repair patterns each make the code more complex to run,  increase the amount of code, and make it harder to see the cost of the original infrastructure decision. Workarounds are not solutions; they are technical debt that adds latency. 

Vector databases, feature stores, and retrieval layers

AI applications depend on specialized data structures that the operational data tier must serve efficiently.

• Embedding stores hold the high-dimensional vector representations that neural networks use to encode semantic meaning. Retrieval-augmented generation (RAG) pipelines retrieve relevant embeddings from these stores at inference time to provide models with additional context they were not trained on. Serving embeddings for RAG at production scale requires low-latency vector similarity search, which is different from key-value lookup and uses the data tier's indexing and query execution architecture differently.

• Feature stores serve precomputed features to models at inference time. A recommendation model may require hundreds of features per request, such as user history, item attributes, and contextual signals, that must be assembled and served within a certain time. Feature stores need to keep the features used for training the same as the ones used when the model is running in production.  If these two representations start to diverge, known as training-serving skew, the model isn’t as accurate. In fact, this is one of the most common sources of unexplained model performance degradation in production.

• Session state and context stores must keep information consistent across multiple interactions, preserving past conversation and application data so models respond in a clear and connected way.  This is a low-latency read-write workload with consistency requirements that scale with the number of concurrent active sessions.

Aerospike's architecture addresses this type of work.  Designed for high-throughput, low-latency operational data serving, it maintains read-write concurrency, tail latency guarantees, and consistency under load that the operational data tier must provide. Unlike general-purpose caching systems or object stores retrofitted for inference use cases, Aerospike is built for the way AI applications work in production.

Training infrastructure is not the same thing as inference infrastructure 

The most important architectural difference in AI infrastructure is the one between training and inference. Most infrastructure discussions treat these as points on a continuum, but they aren’t. They have different optimization targets, problems, and cost structures. In fact, infrastructure decisions that make training efficient will, in many cases, make inference performance worse.

Training infrastructure requirements

Training a large model uses high-throughput batch computation. It needs to move as much data as possible through as many GPU operations as possible, as quickly as possible, while maintaining fault tolerance over a job that may run for days or weeks.

The priority is throughput, not latency. A training step that takes 50 milliseconds instead of 45 milliseconds is not a problem if the system produces as much throughput as possible across thousands of steps. What matters is that the GPUs are busy, exchanging and combining gradient updates across all machines, which finishes quickly and without wasting much time or computing resources, and failures do not require starting over. 

Research on fault tolerance in large-scale training clusters shows that checkpoint-related overheads use an average of 12% of total training time, rising to 43% for the worst-performing 5% of jobs.⁸ Distributed training systems at 10,000 or more nodes have hardware failures frequently enough that they require fault tolerance. 

Meta's experience building its 129,000-GPU cluster for Llama training illustrates the scale of this challenge. The cluster was assembled by emptying five data centers, and training jobs were designed around the assumption that failures would occur regularly. The infrastructure problem at training scale is not preventing failures, but recovering from them quickly enough that total throughput loss remains acceptable.⁹

Inference infrastructure requirements

In contrast, inference is a real-time serving problem. The objective is to produce a response to each individual request within a bounded latency window, under any traffic pattern the application might generate. While throughput still matters, latency predictability and consistency are more important.

MLCommons' MLPerf Inference benchmarks provide the most widely adopted quantitative framework for evaluating inference performance. In the server scenario, which models real-time serving with Poisson-distributed request arrivals, the benchmarks impose explicit latency targets: for large language model inference on Llama-class models, v5.0 results target a 99th-percentile time-per-output-token of approximately 40 milliseconds. For the reasoning-model inference scenario introduced in MLPerf v6.0, the DeepSeek-R1 interactive scenario imposes a 99th-percentile first-token latency of 1.5 seconds and a 99th-percentile time between tokens of 15 milliseconds.¹⁰ These are not soft guidelines. Systems that cannot meet these targets under load have failed the serving objective.

Inference traffic is also different from training workloads. Training is predictable: a job runs until it finishes. Inference must handle the full unpredictability of user behavior, including sudden traffic spikes. Infrastructure sized for average load fails during spikes, but infrastructure sized for peak load is expensive to run at average load. Managing this tradeoff requires flexible scaling that training infrastructure does not need.

Why optimize for training breaks inference

Infrastructure decisions that make training more efficient introduce problems at inference time.

Batching is the primary training optimization. Training throughput improves by processing large batches of training examples, spreading the overhead of gradient computation across many examples simultaneously. 

At inference time, however, batching introduces latency. A request that arrives when a batch is not yet full must wait until enough requests have accumulated to fill the batch before it’s processed. The result is tail latency that increases with batch fill time. 

Checkpoint-heavy storage architectures are optimized for large sequential reads, such as reading a batch of training examples or writing a model checkpoint. Inference requires random-access reads of small records, such as a specific user's context, a specific embedding, or a specific feature value. Storage systems that are good at sequential throughput aren’t good at random-access workloads. 

GPU memory management strategies that focus on training throughput, such as filling GPU SRAM with large batch tensors to try to do many arithmetic functions at once, produce latency spikes at inference time when memory must be evicted to serve a new request. Training tolerates these spikes because throughput is the objective, but inference cannot.

The p99 problem in production AI

Average latency is the wrong metric for production AI infrastructure. A system that serves 95% of requests in 10 milliseconds and 5% of requests in 500 milliseconds has an average latency that looks acceptable, but a user experience that is not.

In distributed systems that combine results from multiple components, the probability that at least one component responds slowly on any given request increases with the number of components involved.⁷ Tail amplification means that inference pipelines that make multiple calls to data systems, model serving layers, or retrieval systems will see p99 latency that approaches the deeper tail (p99.9 or p99.99) of their individual components, not the p99. A component that looks fast on p99 but has a long p99.99 tail will produce aggregate p99 latency that reflects that worse tail.

What this means is that p99 and p999 latency,  not mean latency, should be the primary metrics for evaluating components in an inference pipeline. A data tier, serving layer, or retrieval system that looks fast on average but has unreliable tail latency will affect the p99 of every system that depends on it.

The infrastructure requirements of agentic AI

Static model serving of taking an input, running an inference, and returning an output, places predictable demands on infrastructure. But orchestrating AI agents, which must reason over multiple steps, call external tools, maintain state across turns, and execute parallel subtasks, places a different class of demands. Some current infrastructure isn’t ready to handle it yet. 

What makes agentic workloads architecturally different

An AI agent is not a more complex version of one inference call.  One inference call maps one input to one output. An agent maps one user intent to a sequence of actions, each of which may involve additional inference calls, tool invocations, data reads, external API calls, and state updates before producing a final result.

Agentic AI’s performance gain comes with an infrastructure cost: Agents typically use approximately 4 times more tokens than with chat interactions, and multi-agent systems use approximately 15 times more tokens than with single-turn chats. This means you need that much more serving infrastructure. 

Here’s how agentic AI generally works: the model receives a request, observes the current state of its environment, selects an action, executes it, and repeats until the task is complete. Each iteration of this loop involves an inference call, a state read, an action execution, and a state write. The data tier must perform all four of these operations within the latency time of one user-perceived interaction.

State management at inference time

Stateless inference of taking an input and returning an output doesn’t require much from the data tier, because the model needs no persistent context.

However, agentic inference is stateful. An agent must read its current state before deciding what action to take, update its state after taking the action, and read the updated state at the beginning of the next step. In a multi-step reasoning task, this cycle may repeat dozens of times before producing a final response.

This is not a storage problem in the traditional sense. Object storage and batch-oriented data systems aren’t good at the small record reads and writes at high frequency, within a strict per-request latency budget, that agentic AI uses.   The state management requirement at inference time is a low-latency, high-consistency operational database problem. The data tier must be able to serve state reads in microseconds, not milliseconds, or state access will have the most effect on latency.

Aerospike's sub-millisecond read performance and strong consistency model make it a natural fit for this job. When agent state must be read, updated, and read again within the latency budget of one reasoning step, the operational data tier cannot afford the latency variance of eventually consistent systems or the throughput limits of general-purpose caching layers.

Concurrency and orchestration at agent scale

The performance gains from agentic AI come largely from parallelism. Anthropic's multi-agent research system reduced research time to as little as 10% by having the lead agent run three to five subagents in parallel rather than executing subtasks one at a time.¹¹ 

However, running subagents in parallel creates fan-out problems. When five subagents run at the same time, each making multiple reads and writes to the shared data tier, it’s a lot more work compared with one agent. Systems that degrade under high concurrency,  whether through lock contention, connection pool exhaustion, or increased tail latency, don’t work well with agentic workloads.

This is where underpowered data tiers fail. A system that handles single-agent state access acceptably may become the weak link in a multi-agent deployment, not because individual read latency has increased, but because it can’t handle the number of concurrent reads it has to do. 

The memory layer in agentic systems

Current infrastructure discussions think of agent memory as one thing. Actually, it has four roles:

1. Short-term context is the active working memory of an agent's current reasoning session, or the sequence of observations, thoughts, and actions accumulated within one task. The model doesn’t need external storage for this, but its growth has implications for processing cost and latency.

2. Long-term memory is persistent information about users, preferences, facts, or learned patterns that should survive across sessions. This is what vector stores and embedding retrieval systems are good at, which must support fast similarity search at low latency to be useful in an agent's reasoning loop.

3. Session state is the structured record of a specific interaction, such as the task goal, the actions taken, and the current position in the plan. This puts the heaviest load on the operational data tier: frequent small reads and writes with strong consistency requirements within a bounded time window.

4. Retrieval is the RAG layer, or fetching relevant documents, facts, or records from a knowledge base in response to a specific query. Retrieval infrastructure must support vector similarity search, keyword search, or hybrid approaches, quickly enough to be useful. 

Without a memory architecture that spans all four of these layers, agents forget everything between conversations, so they don’t maintain state. Building that architecture means you need to decide what goes where, at what latency, and with what consistency guarantees.

Hybrid, sovereign, and edge deployment architecture

Whether AI infrastructure runs in public clouds, on-premises data centers, at network edge nodes, or in combinations of all three is a workload and compliance question determined by latency requirements, data residency obligations, and economics.

Cloud vs. on-premises vs. hybrid

Public cloud infrastructure offers capacity that grows and shrinks easily, access to the latest accelerator hardware, and managed services that perform administration and maintenance. It is the default choice for teams that do not have large upfront capital budgets or that need to scale processing capacity rapidly without building physical infrastructure.

IDC reports that AI infrastructure currently deployed in cloud or shared environments accounts for 84.1% of total AI spend in 2025. AI infrastructure spending in cloud environments grew 99.3% year-over-year in Q4 2024 alone, reaching $67.0 billion for the quarter.¹² The trend toward cloud-first AI deployment reflects both the availability of managed GPU capacity and the amount of work required to manage accelerator clusters.

On-premises deployment is appropriate when data residency requirements prohibit cloud processing, when inference latency requirements cannot tolerate cloud round-trip delays, or when your system is big enough that economies of scale mean it’s cheaper to own and run your own.  The build-versus-buy decision for AI infrastructure requires a total cost of ownership (TCO) analysis that includes not just hardware acquisition but energy, cooling, networking, operations, and the opportunity cost of capital.

Hybrid architectures use cloud for flexible training capacity and on-premises or edge deployment for inference serving, addressing both the cost efficiency of cloud processing for batch workloads and the latency requirements of real-time serving.

Sovereign AI infrastructure

Sovereign AI refers to a nation's or organization's capability to produce and operate artificial intelligence using infrastructure it controls, within data and regulatory boundaries it defines. Sovereign AI concerns where models are trained, where data is stored, and how inference is served. 

Data sovereignty requirements also need to be considered as part of the infrastructure. If applicable regulations prohibit training data from leaving a jurisdiction, the training cluster must be located within that jurisdiction. If inference requests contain regulated personal data, the inference serving layer must run within the regulatory boundary. These constraints require physical infrastructure deployments that comply with residency requirements.

Edge AI and the inference latency floor

Cloud-based inference introduces a minimum latency requirement determined by the round-trip time between the user and the nearest cloud data center. For applications requiring sub-10 millisecond response times, such as autonomous systems, industrial process control, real-time fraud detection, and certain consumer applications, cloud round-trip times just aren’t fast enough.

Research on telco-based AI inference finds that central cloud adds more than 50 milliseconds one-way latency for many users, regional data centers respond in under 20 milliseconds, and RAN-edge deployments, or computing resources near mobile phone towers and radio network equipment, reduce latency and respond under five milliseconds.¹³ Applications with strict latency requirements may need to consider moving inference to the edge. 

However, edge deployment has its own infrastructure limitations. Edge nodes typically have less compute capacity than cloud data centers, requiring model compression techniques such as quantization, distillation, and pruning to make models small enough to serve at the edge and still be accurate enough. The operational data tier must also follow the workload to the edge, which creates issues about data synchronization, consistency across distributed edge nodes, and the challenge of running systems in remote locations where there are no dedicated IT or operations staff physically present. 

What AI infrastructure costs

The ROI narrative around AI infrastructure often focuses on projected efficiency gains and improved revenue. A more rigorous analysis starts with a complete picture of cost,  including costs that do not appear in initial procurement budgets.

Capital costs vs. operational costs

The capital cost of AI infrastructure is what procurement conversations focus on: the cost of GPU servers, networking hardware, storage systems, and data center capacity. These costs are substantial. Global AI infrastructure spending reached $976 billion in 2025, with Gartner projecting $1.43 trillion in 2026 and $1.89 trillion in 2027, with year-over-year increases of 47% and 32%, and spending on AI-optimized servers to triple over the next five years to become the largest subsegment.¹⁴ 

But running the equipment costs even more than buying it, primarily due to energy consumption. NVIDIA Blackwell-generation systems require liquid cooling infrastructure, and power density requirements create energy bills that grow with how much they’re used. 

The relevant financial metric for AI infrastructure evaluation is three-to-five-year TCO, not how much it costs to buy the equipment. A system with lower upfront cost but is more complex to run, uses more energy, or fails more often will typically have worse TCO than a system that doesn’t have those problems. 

The cost of infrastructure failure

Infrastructure failures in AI systems are not limited to outages. The more common and more costly failure is degraded performance, such as inference latency that misses SLAs, consistency failures that produce incorrect model outputs, and throughput constraints that force request queuing.

Not surprisingly, engineering teams with more frequent delivery failures spend more time recovering and fixing these problems than on feature development. Applied to AI infrastructure, this means engineering time spent diagnosing increasing latency, implementing workarounds for consistency failures, and managing scaling incidents is time that is not spent improving model quality or expanding application capabilities.

Gartner predicts that by 2028, 40% of organizations deploying AI will implement dedicated AI observability tools to monitor model performance, a reflection of growing recognition that infrastructure failure in AI systems is often subtle, showing up as decreasing model accuracy or user experience problems rather than system errors.¹⁵ Without an observability infrastructure that spans both the model layer and the underlying data tier, these failures are difficult to detect and nearly impossible to attribute correctly.

The added cost of workarounds

When infrastructure isn’t enough for what it’s supposed to do, the immediate response is typically not to replace the infrastructure but to build workarounds with application-level compensating logic. Retry mechanisms handle transient failures. Circuit breakers prevent cascade failures under load. Staggered cache refresh strategies reduce the probability of stampedes. Read-repair code handles consistency problems in distributed systems.

Each workaround solves an immediate problem while creating technical debt.  The workaround code must be understood by every engineer who works on the system. It must be tested when the underlying infrastructure changes. It must evolve when the problem it addresses changes. And because it masks the underlying infrastructure problem, it discourages the investment that would eliminate the need for the workaround.

McKinsey's State of AI 2025 survey finds that AI high performers are nearly three times more likely than other organizations to have rigorously redesigned their workflows as a consequence of organizational AI adoption. This pattern also applies to infrastructure: high-performing teams tend to address structural problems at the layer they live in, rather than compensating around them.¹⁶ Designing the operational data tier correctly at the outset, rather than patching a general-purpose system with workarounds, is more productive over the lifetime of the system.

Build vs. buy vs. managed service

Organizations evaluating AI infrastructure face three structural options for each layer of the stack, each with its own infrastructure investment.

• Building infrastructure means owning hardware, running software, and managing the operational lifecycle. It gives you the most control and, at sufficient scale, is the most economical.  However, it requires engineering capability and capital investment, and it gives you a single point of failure.

• Buying from cloud providers means using GPU processing, storage, and managed services on an operational budget. It is more flexible and less work. However, you’re then dependent on a vendor for cloud pricing and availability, and costs at scale add up.

• Adopting purpose-built managed services for specific layers, particularly the operational data tier, combines the economics of managed operations with the performance characteristics of specialized systems. Purpose-built systems designed for AI-scale workloads deliver the latency, throughput, and consistency that general-purpose managed services cannot, while not being as much work as self-managed infrastructure.

Evaluate each infrastructure layer independently against the workload it serves, the SLAs it meets,  and the TCO over time. No one procurement model is correct for all layers or all organizations.

How to evaluate AI infrastructure for production 

Production readiness evaluation requires a framework that tests what infrastructure provides under real conditions, and what it looks like when it doesn’t meet those commitments. 

The five infrastructure guarantees that matter in production

1. Latency at the tail. Evaluate infrastructure against p99 and p999 latency under load, not average latency under ideal conditions. A system that cannot guarantee its tail is not production-ready for latency-sensitive AI workloads.

2. Throughput under burst. AI application traffic does not follow a bell curve distribution. Evaluate the throughput capacity of each infrastructure layer, not at its mean operating point, but at three to five times that level. Systems provisioned for average load will fail during high traffic events.

3. Consistency under concurrent writes. For any infrastructure layer that must serve state, context, or features to inference systems, test the consistency model using the concurrent writes that agentic workloads generate. A system that provides eventual consistency may be acceptable for some caching use cases, but it is not acceptable for session state, feature serving, or any workload where stale reads produce incorrect model outputs.

4. Availability while maintaining performance. Test what happens to latency and throughput during node failures, rolling updates, and capacity scale-in events. Systems that maintain availability with poorer performance when something happens result in unpredictable SLAs.

5. Operational observability. The Google SRE Book's four “golden” signals of latency, traffic, errors, and saturation are the minimum production metrics.¹⁷ Infrastructure that cannot measure these well enough to support root-cause analysis will make debugging production problems expensive.

The problem audit

For each infrastructure layer, production readiness evaluation should identify three factors: what failure looks like, how it shows up, and how quickly it can be detected and resolved.

• Processing failures such as GPU errors, node crashes, and cluster network partitions typically show up as job failures or serving errors and are the most visible class of failure. Mature orchestration systems generally handle them.

• Data tier failures are harder to spot. Increasing latency in the operational data tier may not show up as an error, but as increased inference latency, which may be attributed incorrectly to the model serving layer or to network congestion. Identifying the data tier as the root cause requires observability instrumentation at the data access layer, not just at the application.

• Consistency failures are the hardest to detect. A model receiving stale feature data, incorrect session state, or outdated embeddings will produce outputs that are wrong in ways that look like model accuracy problems rather than infrastructure failures. Detecting consistency failures requires end-to-end testing with consistency-checking instrumentation, not just latency and throughput monitoring.

What breaks first as workloads scale

As AI workloads grow, infrastructure layers reach their limits in a predictable sequence.

• Processing capacity is typically the first constraint organizations encounter, because GPU procurement timelines are long and demand for accelerator hardware remains high. Teams that do not plan GPU capacity six to 12 months ahead run into serving capacity constraints that cannot be resolved quickly.

• The operational data tier is frequently the second constraint, because it is often not defined as well as the processing layer. Teams that spend money on GPU infrastructure and managed serving frameworks, but deploy the data tier on general-purpose caching systems not designed for AI inference,  encounter data tier limitations when traffic grows.

• Networking becomes a constraint during training scale, when sharing training updates between machines requires more network bandwidth than a standard data center network can provide. For inference workloads, networking constraints typically mean the system accepts incoming data only up to a certain speed or volume during traffic spikes.

Understanding this sequence lets you plan for it. Spend money on the constraint you will run into first based on how much it affects the system's ability to meet its SLAs, not based on its cost or how much people talk about it during procurement. 

AI infrastructure and Aerospike

Problems that undermine AI deployments at scale are typically not problems with processing, but with the data tier. Cache stampedes, consistency drift, tail latency amplification, and the workarounds that accumulate around them aren’t obvious in GPU utilization charts or networking dashboards, but show up as model outputs that aren’t as accurate, p99 latency that’s not as predictable, and engineering teams spending their time on fixing problems rather than designing new systems. 

Aerospike is built for this. Its architecture is designed around the assumption that workloads are inherently volatile, that people will use the system in different ways, that traffic will spike, and that multiple agents will compete for the same state simultaneously. 

Unlike general-purpose caching systems or object stores adapted for inference uses, Aerospike delivers consistent behavior even when people use the system differently, predictable performance under large-scale request fan-out, and correctness even in distributed, always-on systems. 

For teams building production AI systems, the data tier is the one that determines whether other infrastructure investments do what they’re supposed to. When the operational data tier cannot meet the latency and consistency demands of real-time inference, you can’t just throw processing at the problem. The constraint is architectural, and it requires an architectural solution.

Aerospike's role in AI infrastructure spans all four memory functions that production systems require: 

1. Session state and context stores that serve sub-millisecond reads under concurrent load

2. Feature stores that reduce divergence between training and serving

3. Embedding stores that support low-latency vector retrieval for RAG pipelines

4. Long-term memory stores that persist across agentic sessions without sacrificing consistency

Each of these is a separate requirement. Aerospike addresses them within one platform, so you don’t have to put together and maintain separate systems for each one. 

The AI infrastructure stack is entering a period of significant change. Static model serving is being replaced by agentic orchestration. Single-agent workflows are giving way to multi-agent systems that need ten times more concurrent data access.  Infrastructure decisions teams make now, especially the operational data tier, determine whether those systems scale reliably or accumulate the workarounds and technical debt that limit future teams.