DEV Community: Lior Ben-David

What Is AI Infrastructure? The 2026 Stack, Explained

Lior Ben-David — Tue, 14 Jul 2026 14:42:46 +0000

The 2026 AI infrastructure stack encompasses hardware, data, and software layers that enable scalable AI applications. This guide explains its core components, from compute to governance, with Bifrost as a key AI gateway for enterprise deployment.

The rapid adoption of artificial intelligence has propelled AI infrastructure into the core digital fabric for enterprises worldwide. By 2026, AI is no longer a niche technology; it is a foundational component of business operations, requiring specialized infrastructure that far exceeds traditional IT capabilities. This complex, layered system supports everything from data processing and model training to reliable AI inference and continuous deployment. Understanding its core components and how they interconnect is essential for organizations building and scaling AI applications. Bifrost, an open-source AI gateway built in Go, represents a critical layer within this modern stack, helping teams manage the complexities of model access and governance.

Defining the AI Infrastructure Stack

AI infrastructure refers to the combination of hardware and software components specifically designed to support AI workloads, including machine learning, deep learning, and large-scale data processing. Unlike conventional IT infrastructure, AI infrastructure is optimized to handle the intensive computational requirements and massive datasets characteristic of AI applications. It forms the underlying system that enables data processing, model training, AI inference, deployment, and lifecycle management.

The AI infrastructure stack consists of several interconnected layers, working in concert to facilitate the entire AI development and deployment lifecycle. These layers typically include compute resources, storage systems, networking, operating environments, orchestration platforms, and supporting software frameworks. A well-designed stack enables scalable training, reliable AI inference, and consistent deployment across various environments, from cloud to on-premises to edge locations.

Core Components of the 2026 AI Infrastructure

Modern AI infrastructure is a sophisticated blend of specialized hardware, robust data pipelines, intelligent software layers, and comprehensive management tools.

Compute and Hardware

At the foundation of any AI infrastructure lies high-performance computing hardware. Graphics Processing Units (GPUs) continue to dominate AI training workloads due to their parallel processing capabilities, which significantly accelerate neural network computations. Tensor Processing Units (TPUs) from Google, more specialized Application-Specific Integrated Circuits (ASICs), are increasingly used for deep learning tasks due to their high throughput and efficiency. Alternative accelerators are gaining traction for inference due to their energy efficiency and cost-effectiveness.

Beyond processors, AI hardware incorporates high-performance servers with ample memory and storage, vital for handling the massive datasets used in model training. High-bandwidth, low-latency networking solutions are also crucial, ensuring data moves rapidly between storage and compute units. As power densities increase, advanced cooling solutions, including liquid cooling, are becoming standard in data center designs.

Data Management and Pipelines

AI systems are only as good as the data they consume. Effective data management involves robust pipelines for data ingestion, cleaning, transformation, and augmentation. These processes ensure that AI models receive high-quality input, which is critical for accurate training and reliable performance. Vector databases, for instance, have become essential for storing embeddings and powering retrieval-augmented generation (RAG) architectures, allowing models to access trusted, external knowledge sources. Organizations are strategically modernizing data pipelines by improving data quality, governance, and integration across systems.

Model Development and Management

This layer encompasses the tools and practices for building, training, evaluating, and managing AI models throughout their lifecycle. Machine learning frameworks such as TensorFlow and PyTorch provide the building blocks for model development. MLOps (Machine Learning Operations) practices combine ML, DevOps, and data engineering to automate and streamline workflows, from continuous integration/continuous delivery (CI/CD) pipelines for AI applications to version control and deployment. The trend towards smaller, more specialized LLMs, optimized for inference, is gaining momentum, supporting the rise of agentic AI and edge decision systems by offering faster, cheaper, and more predictable performance.

AI Gateway / Inference Layer

Once models are trained, they need to be efficiently deployed and managed in production. An AI gateway acts as a unified entry point, routing, authenticating, observing, and governing traffic to multiple LLM providers from a single API. This layer is crucial for managing the complexities of multi-provider environments, offering capabilities such as automatic failover and intelligent load balancing to ensure high availability and reliability [cite: bifrost-context]. An AI gateway also provides a centralized control plane for cost optimization, allowing teams to reduce expenses through features like semantic caching and applying rate limits and budgets across different models and providers.

Bifrost, the AI gateway, offers a unified, OpenAI-compatible API that simplifies access to over 1000 models, enabling organizations to switch providers or models by changing only a base URL [cite: bifrost-context]. Beyond simple routing, Bifrost includes features such as semantic caching to reduce repeat-query costs and virtual keys for granular access control and budget management. Its capabilities extend to acting as an MCP gateway, exposing tools to clients like Claude Desktop and supporting autonomous agentic workflows with its Agent Mode.

AI Governance and Security

As AI moves into critical business processes, governance and security become paramount. AI governance encompasses the principles, roles, processes, and controls an organization uses to deploy AI safely, ethically, and in compliance with regulations. It establishes accountability, manages risk, and ensures transparency and auditability throughout the AI lifecycle. This includes implementing access controls, guardrails, and audit logging to protect sensitive data and prevent unintended model behavior. Prominent frameworks guiding this area include the NIST AI Risk Management Framework and ISO/IEC 42001.

Crucially, the Bifrost AI gateway establishes these governance and security controls (virtual keys, budgets, guardrails, audit logs) centrally. Bifrost Edge extends this same governance and security to AI traffic on employee machines, with endpoint enforcement on each device, addressing the challenge of shadow AI. This combined "AI Gateway + Bifrost Edge" approach ensures that even AI used on employee laptops and in desktop applications adheres to organizational policies and regulatory requirements [cite: bifrost-edge-context].

Observability and Monitoring

Effective AI infrastructure demands comprehensive observability to ensure performance, identify issues, and maintain quality in production. This involves real-time tracking, debugging, and resolving quality issues with alerts. Tools for distributed tracing, automated quality checks, and performance metrics help teams monitor model behavior, latency, and cost in live environments. Integrating with platforms like Prometheus and OpenTelemetry allows for deep insights into the AI system's health and performance [cite: docs.getbifrost.ai/features/observability/default].

The Evolution of AI Infrastructure

The AI infrastructure landscape is undergoing continuous evolution. A significant trend in 2026 is the shift from monolithic, single-provider architectures to more modular, multi-cloud, and hybrid environments. While cloud infrastructure remains the default deployment model for many LLM systems, enterprises are increasingly adopting hybrid deployment models to balance regulatory compliance, data sovereignty, security, latency, and operational efficiency. For organizations with sensitive or proprietary data, on-premises AI platforms offer unmatched control over data, customization, and cost predictability over time.

The explosive growth and autonomy of AI agents are also reshaping infrastructure demands. These agents, capable of executing multi-step tasks across enterprise workflows, necessitate robust, flexible infrastructure that can support reasoning, retrieval, tool use, and execution. This shift requires greater emphasis on distributed AI systems and an architecture that allows for seamless workload mobility and data portability between environments.

Key Considerations for Building AI Infrastructure

Building an AI-ready infrastructure in 2026 involves strategic planning and a clear understanding of an organization's specific AI use cases and infrastructure needs.

Scalability and Elasticity: Infrastructure must dynamically adapt to varying AI workloads, scaling compute and storage resources up or down as needed.
Cost Optimization: Strategic choices in hardware, cloud providers, and software tools significantly impact operational costs. Continuously optimizing performance and cost is crucial.
Security and Compliance: Designing with a defense-in-depth philosophy is essential, integrating controls that address both traditional cybersecurity threats and unique AI safety risks. This includes robust AI governance frameworks and data protection pipelines.
Flexibility and Integration: The stack should be designed for modular integration, allowing organizations to adapt their architecture as needs and technologies evolve. This often involves supporting hybrid and multi-cloud environments.
Observability and Governance: Implementing observability and governance across the entire stack provides the necessary oversight for safe, reliable, and compliant AI deployment. Defining workloads before comparing vendor offerings is critical to avoid infrastructure evaluation mistakes.

The 2026 AI infrastructure stack is a dynamic and evolving ecosystem. Successfully navigating this landscape requires a holistic approach that balances performance, cost, security, and governance. Leveraging purpose-built tools, like the Bifrost AI gateway, can streamline complexity and accelerate the deployment of reliable, enterprise-grade AI applications. Teams evaluating AI gateways can request a Bifrost demo or review the open-source repository.

Sources

Mirantis. (February 3, 2026). AI Infrastructure Stack: Essentials & Guidelines.
Cloudian. (2026). AI Infrastructure: Key Components and 6 Factors Driving Success.
Makebot.ai. (January 9, 2026). 10 Key LLM Market Trends for 2026.
Kong Inc. (June 26, 2026). What is AI Governance? 2026 Framework Guide.
AIntelligenceHub. (2026). AI Infrastructure in 2026: Chips, Cloud, and Capacity Choices.

Best Tools for Benchmarking LLM Provider Performance

Lior Ben-David — Thu, 09 Jul 2026 09:32:21 +0000

Measuring LLM provider performance is critical for production AI applications. This post explores leading tools for benchmarking latency, throughput, cost, and reliability across providers, positioning Bifrost as a top choice for integrated performance and enterprise-grade reliability.

The operational challenges of running AI applications at scale often extend beyond model quality. Engineers building production AI systems must contend with variable provider latency, unexpected outages, and escalating token costs. Without robust benchmarking, these factors can significantly impact user experience and the bottom line. Evaluating the performance of Large Language Model (LLM) providers is therefore essential for optimizing deployments and ensuring reliability. Bifrost, an open-source AI gateway built in Go by Maxim AI, provides integrated tools and capabilities that address these challenges directly, offering a powerful option for teams focused on enterprise-grade performance and control.

Why Benchmark LLM Provider Performance?

Benchmarking LLM provider performance goes beyond simply comparing model outputs. It involves assessing the underlying infrastructure's responsiveness, stability, and cost-effectiveness under real-world conditions. This process helps teams:

Optimize Costs: LLM pricing varies significantly across providers and models, typically based on token usage. Benchmarking helps identify the most cost-efficient options for specific workloads.
Enhance User Experience: Latency is a critical factor for interactive AI applications. Measuring and optimizing for Time to First Token (TTFT) and overall response time ensures a smooth user experience.
Ensure Reliability: Providers can experience outages or performance degradation. Benchmarking allows teams to test failover strategies and identify providers that offer consistent uptime and low error rates.
Plan for Scale: Understanding throughput (requests per second or tokens per second) helps predict how an application will perform under heavy load and aids in capacity planning.

Core Metrics for LLM Provider Benchmarking

Effective LLM provider benchmarking focuses on several key performance indicators:

Latency: This refers to the time it takes for a request to be processed. Key metrics include:
- Time to First Token (TTFT): The delay until the first token of a streaming response is received, crucial for perceived responsiveness.
- Time to Last Token (TLT): The total time until the entire response is received.
- Inter-Token Latency: The delay between consecutive tokens, impacting the fluidity of streaming responses.
- P50, P95, P99 Latency: Percentile measurements that indicate the latency experienced by the majority of requests, as well as the long-tail latency affecting a smaller percentage.
Throughput: This measures the volume of requests or tokens a provider can handle per unit of time.
- Requests Per Second (RPS): The number of inference requests an LLM can handle per second, directly indicating system capacity.
- Output Tokens Per Second (tokens/s): How quickly the model generates response tokens, reflecting generation speed.
Cost: The financial expenditure associated with using a particular LLM provider, typically calculated per million input and output tokens.
Reliability: The consistency of service, measured by factors like success rates, error rates, and the effectiveness of failover mechanisms under adverse conditions.

Bifrost: Integrated Performance and Reliability Benchmarking

For enterprises running mission-critical AI workloads, integrated benchmarking capabilities within an AI gateway are invaluable. Bifrost is designed to address the complexities of multi-provider LLM deployments, offering both low-overhead performance and robust tooling for evaluation. In sustained benchmarks at 5,000 requests per second, Bifrost adds only 11 microseconds of overhead per request, making it virtually transparent in a production environment.

As an AI gateway, Bifrost provides:

Built-in Benchmarking: The platform offers tools to run custom benchmarks and analyze real-world performance metrics, allowing teams to quantify latency, throughput, and error rates across different LLM providers and models. Benchmarking guides are available to help users run their own benchmarks and understand performance on various machine specifications, such as t3.medium and t3.xlarge instances.
Automatic Failover and Load Balancing: Bifrost routes requests across providers and keys, automatically falling back to healthy alternatives during outages. This built-in reliability is critical for maintaining uptime and can be measured directly through the gateway's observability features.
Centralized Observability: Bifrost provides native Prometheus metrics and OpenTelemetry (OTLP) integration, allowing teams to monitor latency, token usage, and errors in real-time. This observability is essential for understanding performance under load and identifying bottlenecks.
Governance and Cost Control: With virtual keys, budgets, and rate limits, Bifrost enables granular control over LLM consumption, helping teams track and optimize spending across different projects and users.
Enterprise-Grade Capabilities: Beyond performance, Bifrost supports advanced features like clustering for high availability, adaptive load balancing, and in-VPC deployments, all of which contribute to measurable improvements in reliability and operational efficiency. It also extends its robust governance and security controls through Bifrost Edge, which applies policies to AI traffic on employee machines and actively governs approved applications and MCP servers at the endpoint.

Specialized Tools for Low-Level Performance Analysis

While a robust AI gateway offers comprehensive insights, specialized tools delve deeper into the raw inference performance of LLM infrastructure:

NVIDIA GenAI-Perf and AIPerf: These tools are purpose-built for detailed LLM performance benchmarking, particularly for understanding inference-level metrics like throughput and latency across different GPU configurations. They provide insights into optimizing hardware for LLM workloads.
LLMPerf: An open-source tool aiming for reproducibility and clarity in LLM performance benchmarks. It offers insights into performance, accuracy, and reliability across various LLM inference providers.
LLM Locust: Designed to overcome the limitations of traditional load testing tools for LLM-specific characteristics like streaming and token-level granularity. It tracks metrics such as Time to First Token (TTFT), Output Tokens per Second (tokens/s), and Requests per Second (RPS) under load.

These tools are particularly useful for hardware sizing, infrastructure optimization, and comparing the raw capabilities of different model serving setups.

Open-Source Frameworks for Application-Level Evaluation

Several open-source frameworks focus more broadly on LLM evaluation, often emphasizing model output quality and application behavior rather than raw provider infrastructure performance. However, many can still capture latency metrics during their evaluations:

OpenAI Evals: This open-source framework from OpenAI allows teams to define custom evaluations and run LLMs against datasets, scoring outputs based on various criteria. While its primary focus is on model quality and capability, it can be configured to track performance metrics like latency.
LangChain Evaluation: LangChain provides tools for evaluating LLM applications, including methods like LLM-as-a-Judge and deterministic testing. It helps assess accuracy, relevance, and completeness, and also includes mechanisms for tracking latency. LangSmith, an observability platform for LangChain, integrates with these evaluations to visualize results and track trends.
LiteLLM: As an open-source Python library and proxy, LiteLLM provides a unified API for over 100 LLM providers. It includes basic benchmarking functionality and publishes its own performance benchmarks, though comparisons suggest it may introduce higher overhead than Go-based alternatives like Bifrost under high concurrency.

These frameworks are valuable for ensuring the quality and correctness of AI applications, but teams should use them in conjunction with tools that provide deep infrastructure performance insights when provider-level metrics are the primary concern.

LLM Cost Comparison Tools

Cost is a crucial factor in LLM deployment. Several online tools assist in comparing pricing across various models and providers:

WhatLLM.org: This platform aggregates benchmark data, real-world pricing, and throughput metrics for hundreds of LLMs from numerous providers, offering a unified interface for comparison.
Langtail and Rows Calculators: Tools like the LLM Price Comparison Tool by Langtail and Rows' LLM API Price Calculator allow users to estimate costs based on input/output tokens and model choices across different providers.

These tools provide an essential overview for budget planning and model selection.

Choosing the Right Benchmarking Strategy

Selecting the optimal LLM benchmarking tools depends on an organization's specific needs. For teams focused on deep, low-level inference performance and hardware optimization, specialized tools like NVIDIA GenAI-Perf or LLM Locust offer granular control and insights. For evaluating the functional correctness and output quality of LLM applications, frameworks like OpenAI Evals and LangChain provide robust solutions.

For enterprises building and operating production AI at scale, an integrated approach often yields the most comprehensive results. This typically involves combining dedicated performance benchmarking with a robust AI gateway that offers built-in observability, governance, and reliability features. A gateway like Bifrost can serve as the central point for routing and managing LLM traffic, simultaneously collecting critical performance data and enforcing policies.

Next Steps

Organizations evaluating their LLM infrastructure and seeking to optimize provider performance can explore Bifrost for its integrated benchmarking, low overhead, and enterprise-grade features. Teams can request a Bifrost demo or review its open-source repository to understand its capabilities for high-performance, reliable AI deployments.

Sources

Bifrost: The Fastest Open-Source Enterprise LLM Gateway. Maxim AI.
LLM performance benchmarks | LLM Inference Handbook. BentoML.
LLM Locust: Benchmarking LLM Performance at Scale. Truefoundry.
WhatLLM.org: Compare LLMs by Benchmarks, Price & Speed — Live Rankings. WhatLLM.org.
LLM Evaluation Tools: Frameworks, Benchmarks & Metrics. LangChain.