DEV Community: Takeshi Mori

The Modern AI Infrastructure Stack: 8 Layers Every Team Needs

Takeshi Mori — Tue, 14 Jul 2026 14:46:26 +0000

As AI moves beyond experimentation, a robust infrastructure stack is essential for reliability, scalability, and governance. This article examines the eight critical layers comprising a modern AI infrastructure, highlighting how tools like Bifrost address key challenges for enterprise teams.

Deploying AI applications in production presents unique challenges that traditional software stacks were not designed to handle. From managing vast datasets and evolving models to ensuring real-time performance and stringent governance, a comprehensive, layered approach to AI infrastructure is no longer optional. It is the foundation upon which scalable, trustworthy AI systems are built. An increasing number of organizations are realizing that fragmented systems and inconsistent security policies hinder growth and lead to isolated, unscalable AI initiatives.

This guide explores the eight essential layers of a modern AI infrastructure stack, outlining their purpose and how they interoperate. Bifrost, an open-source AI gateway from Maxim AI, provides critical capabilities across several of these layers, particularly in orchestration, governance, and observability.

1. Compute & Infrastructure

The foundational layer of any AI stack provides the raw computing power, storage, and networking resources needed to build, train, and run models at scale. This includes specialized hardware like GPUs, distributed computing clusters, and scalable cloud or on-premise environments. Unlike traditional IT, AI workloads demand highly parallel processing and massive data movement, requiring infrastructure designed for such intensity.

Key components here encompass:

Hardware Accelerators: GPUs (NVIDIA, AMD), TPUs, and other specialized AI chips.
Cloud/On-premise Platforms: Providers like AWS, Azure, GCP, or private data centers offering elastic, scalable resources.
Containerization & Orchestration: Docker, Kubernetes, and other tools to manage and scale workloads across distributed environments.

This layer is critical for performance and cost efficiency, as AI workloads are compute-heavy and dynamic. Optimized inference serving, for example, relies on fast spin-up times and responsive auto-scaling, often leveraging advanced batching techniques for efficiency.

2. Data Management & Feature Engineering

Data is the lifeblood of AI. This layer focuses on ingesting, storing, processing, and transforming data to feed models. It also includes the crucial process of feature engineering, where raw data is converted into features that enhance model performance. Without high-quality, governed data, AI projects are predicted to fail.

Core components:

Data Ingestion Pipelines: Tools for collecting data from various sources (streaming, batch).
Data Lakes & Warehouses: Scalable storage solutions for raw and processed data.
Vector Databases: Specialized databases for storing and querying high-dimensional vector embeddings, crucial for RAG architectures.
Feature Stores: Centralized repositories for creating, storing, and serving features consistently across training and inference.
Data Quality & Governance: Tools and processes to ensure data accuracy, consistency, and compliance with regulations.

3. Model Development & MLOps

This layer encompasses the entire lifecycle of model creation, from initial experimentation and training to continuous integration and continuous deployment (CI/CD) for machine learning. MLOps practices automate model deployment, monitoring, and retraining to maintain performance in production environments.

Elements of this layer include:

Experiment Tracking: Tools to log model metrics, parameters, and artifacts.
Training Frameworks: TensorFlow, PyTorch, JAX, etc.
Version Control: Git, DVC, and specialized ML versioning tools for code, data, and models.
CI/CD for ML: Automated pipelines for building, testing, and deploying models.

4. Model Registry & Lifecycle Management

A model registry serves as a centralized catalog and system of record for all trained models, their versions, metadata, and lifecycle stages. It ensures reproducibility, auditability, and controlled promotion of models from development to production.

Key functions:

Model Versioning: Tracking changes in model architecture, training data, and performance metrics.
Metadata Management: Storing information about training data, configurations, and performance metrics.
Lifecycle Stages: Managing models through stages like "staging," "production," or "archived" with gated promotions.
Lineage Tracking: Connecting models to their upstream data sources and downstream consumers for full traceability.
Access Control: Defining permissions for who can view, edit, or promote models.

5. Model Serving & Inference

This layer focuses on deploying trained models into production environments and efficiently serving predictions (inference) to applications. It demands infrastructure optimized for low latency, high throughput, and resilience under varying loads.

Components include:

Inference Servers: Software like NVIDIA Triton Inference Server or TensorFlow Serving to manage model loading, execution, and scaling.
Load Balancing & Autoscaling: Distributing requests across multiple model instances and dynamically adjusting resources based on demand.
Batching Engines: Optimizing throughput by grouping individual requests for more efficient processing, especially with GPUs.
API Endpoints: Exposing models through RESTful APIs or gRPC for integration with applications.

6. AI Gateway & Orchestration

The AI Gateway acts as a specialized middleware layer that centralizes and manages interactions between applications and AI models, particularly Large Language Models (LLMs). It's a unified entry point that orchestrates the flow of data, instructions, and policies, providing a single control plane for managing multiple models and providers.

Bifrost offers comprehensive capabilities in this layer:

Unified API: A single OpenAI-compatible interface for over 1000+ models from various providers, allowing for a drop-in replacement in existing applications.
Intelligent Routing & Failover: Automatically directs requests to appropriate models based on criteria like cost, latency, or content, and reroutes traffic during provider outages or rate limits. Bifrost provides automatic fallbacks across providers, ensuring application reliability.
Load Balancing & Cost Optimization: Distributes requests and manages API keys to optimize usage and control costs.
Semantic Caching: Reduces latency and costs by serving cached responses for semantically similar queries. Bifrost's semantic caching capability intelligently reuses responses to decrease provider calls.
MCP Gateway: Bifrost functions as an MCP gateway, standardizing how AI models connect to external tools and context sources for agentic workflows. It supports Agent Mode for autonomous tool execution and Code Mode for token-efficient orchestration.

7. AI Governance, Security, & Compliance

As AI systems move into critical workflows, robust governance, security, and compliance become paramount. This layer establishes the policies, procedures, and ethical considerations to oversee the development, deployment, and maintenance of AI systems, ensuring they operate within legal and ethical boundaries.

Key aspects include:

Access Control & Authentication: Centralized management of who can access which models and data. Bifrost uses virtual keys for granular control over permissions, budgets, and rate limits.
Guardrails & Content Safety: Detecting and blocking malicious activity, sensitive data, or policy violations in prompts and responses. Bifrost offers guardrails for content safety, including native secrets detection and custom regex patterns.
Audit Trails & Explainability: Maintaining immutable records of AI interactions for regulatory compliance (e.g., SOC 2, GDPR, HIPAA, ISO 27001).
Data Access Control (DAC): Governing how models access sensitive data.
Endpoint AI Governance with Bifrost Edge: Beyond gateway-level controls, Bifrost Edge extends governance and security to AI traffic on employee machines. It helps end shadow AI by routing all AI app and MCP server usage through the Bifrost gateway, ensuring endpoint enforcement of existing virtual keys, budgets, and guardrails with MDM deployment for fleet-wide rollout. This capability ensures compliance reaches every device, even for desktop apps and browser AI.

8. Evaluation, Observability, & Application Integration

This top layer focuses on continuously monitoring AI systems in production, evaluating their performance, and integrating AI capabilities seamlessly into end-user applications. AI observability goes beyond traditional monitoring by assessing output quality and model behavior, not just infrastructure metrics.

Key capabilities:

AI Observability: Tracking real-time performance, cost, and usage, with distributed tracing to understand multi-step agentic workflows. Bifrost provides built-in observability with Prometheus and OpenTelemetry integrations.
Evaluation Frameworks: Systematically measuring model quality (accuracy, fairness, safety) using automated and human-in-the-loop methods across development and production. Maxim AI's platform provides simulation and evaluation for testing agents across scenarios and offers production observability with automated quality checks.
Application Integration Patterns: Defining how AI models and agents connect to external data sources, tools, and services via APIs, webhooks, or specialized protocols like MCP.
User Interface/Application Layer: Embedding AI capabilities into software applications, products, and services to deliver actionable insights and drive decision-making.

Conclusion

A modern AI infrastructure stack is a multifaceted system designed to support the entire AI lifecycle, from raw compute to end-user applications. Each of the eight layers plays a distinct but interconnected role in enabling scalable, reliable, and governed AI. For enterprise teams navigating the complexities of AI deployment, understanding these layers and selecting tools like Bifrost that offer robust capabilities across orchestration, governance, and security is paramount to building trustworthy, production-grade AI systems. Teams can request a Bifrost demo or review its open-source repository to explore how it addresses these critical infrastructure needs.

Sources

AI Governance Framework: Core Principles & Global Standards.
What Is an AI Gateway? | IBM.
What Is an AI Stack? Building a Modern Tech Infrastructure in 2026 - Bronson.AI.
Enterprise AI Architecture: Key Components & Best Practices 2026 - Leanware.
On Evaluating Performance of LLM Inference Serving Systems - arXiv.

10 Questions to Ask Before Choosing an LLM Gateway

Takeshi Mori — Thu, 09 Jul 2026 09:36:06 +0000

When selecting an AI gateway for production LLM workloads, evaluating options against key criteria is essential for reliability, cost, and compliance. Bifrost, an open-source AI gateway, centralizes routing, governance, and security for multi-provider deployments.

As organizations move beyond proofs-of-concept to deploying Large Language Models (LLMs) in production, managing direct integrations with model providers becomes increasingly complex. Different APIs, varying rate limits, inconsistent billing, and the need for robust governance quickly create operational overhead. This challenge has driven many engineering teams to adopt an LLM gateway as a centralized control layer. An LLM gateway acts as an intermediary that standardizes access, enhances security, optimizes performance, and streamlines operations across diverse LLM ecosystems.

For teams tasked with selecting this critical piece of infrastructure, the decision involves more than just API compatibility. It requires a structured evaluation of a gateway's capabilities, its fit within existing workflows, and its future-readiness. Bifrost, an open-source AI gateway from Maxim AI, provides a unified entry point to hundreds of models, offering features designed for enterprise-grade performance, governance, and security.

Here are 10 questions to ask when evaluating LLM gateways:

1. What is the gateway's performance overhead and how is it measured?

Latency is a critical factor for interactive AI applications and agentic workflows that involve many LLM calls per task. Even microsecond-level overhead can accumulate, impacting user experience and application responsiveness. Understanding a gateway's baseline performance requires looking beyond simple throughput numbers. Teams should inquire about p95 and p99 latency under realistic concurrency, tail-latency behavior when policies are evaluated, and throughput ceilings per node.

The Bifrost AI gateway is engineered for minimal overhead, adding only 11 microseconds per request at 5,000 requests per second in sustained benchmarks. This near-transparent overhead ensures that the gateway itself does not introduce bottlenecks in high-throughput production pipelines. Performance benchmarks are available to review the methodology and results.

2. Does it offer a truly unified API across all providers and models?

The core value of an LLM gateway is to abstract away provider-specific API differences, allowing applications to interact with many models through a single, consistent interface. This simplifies development, reduces integration effort, and prevents vendor lock-in. A strong unified API supports all major providers and allows for easy onboarding of new or custom models without requiring code changes in the application layer.

Bifrost provides an OpenAI-compatible API that unifies access to 1000+ models from over 20 providers, including OpenAI, Anthropic, AWS Bedrock, and Google Vertex AI. It functions as a drop-in replacement for existing SDKs, often requiring only a change to the base URL.

3. How does it ensure reliability, high availability, and automatic failover?

Production AI applications demand continuous uptime. Provider outages, rate limits, and network issues can severely impact service availability. A robust LLM gateway should include mechanisms such as automatic failover, intelligent load balancing, and health monitoring to ensure requests are always routed to an available and performing model. These features are fundamental for maintaining service level agreements (SLAs).

Bifrost includes automatic fallbacks and intelligent load balancing capabilities that ensure requests keep flowing even during provider outages or degraded performance. Its clustering functionality supports high availability and zero-downtime deployments, making every instance equal in a peer-to-peer architecture.

4. What governance and access control features are available?

Managing who can access which models, setting spending limits, and enforcing usage policies are critical for cost control and compliance, especially in large organizations. An effective gateway offers granular access control, virtual keys, budgets, and rate limits that can be applied at user, team, or project levels.

Bifrost offers comprehensive governance features, with virtual keys as the primary entity for managing access permissions, budgets, and rate limits. These controls enable hierarchical cost management and precise allocation of resources across an organization. Its enterprise version extends this with role-based access control (RBAC) and integration with identity providers like Okta and Microsoft Entra.

5. What security controls and guardrails does it provide?

LLM gateways process sensitive information, making security a paramount concern. The gateway should act as a central enforcement point for security policies, including input validation, output filtering, sensitive data detection, and prompt injection prevention. Compliance with standards like SOC 2, HIPAA, and GDPR often depends on these gateway-level controls.

Bifrost provides robust security features, including guardrails for content safety, secrets detection, and custom regex pattern matching to prevent sensitive data leakage or prompt injection. It also supports data access control (DAC) and offers immutable audit logs that are essential for regulatory compliance. Beyond routing, Bifrost applies governance and security controls (virtual keys, budgets, guardrails, audit logs) centrally, and Bifrost Edge extends that same governance and security to AI traffic on employee machines, with endpoint enforcement on each device. This allows for centralized policy management even for AI applications running on desktops and in browsers.

6. How does it help optimize costs?

LLM costs can escalate quickly, especially with agentic workflows that generate numerous API calls. An effective gateway offers features like intelligent model routing, semantic caching, and token budgeting to reduce spending without sacrificing quality. Real-time visibility into usage and costs is also essential for data-driven optimization.

Bifrost helps teams optimize LLM costs through several mechanisms. Its semantic caching reduces repeat-query costs by caching responses based on semantic similarity. Routing rules can direct requests to the most cost-effective models for specific tasks, and its governance features enable precise budget and rate limits.

7. What observability and debugging capabilities are built in?

When issues arise—whether performance degradation, errors, or unexpected costs—teams need tools to quickly identify the root cause. A good LLM gateway provides real-time monitoring, detailed logs, and metrics for request volume, latency, error rates, token usage, and model selection. Integration with existing observability stacks is also a key consideration.

Bifrost includes built-in real-time request monitoring with native Prometheus metrics and OpenTelemetry (OTLP) integration for distributed tracing. This enables teams to track performance, usage patterns, and compliance metrics effectively.

8. Does it support Model Context Protocol (MCP) for agentic workflows?

As AI agents become more prevalent, the ability to connect them securely and efficiently to external tools and internal systems via the Model Context Protocol (MCP) is critical. An MCP gateway centralizes the discovery, security, and traffic management for these agent-to-tool communications, enforcing policies and providing audit trails for agent actions.

Bifrost natively functions as an MCP gateway, allowing AI agents to discover and execute external tools in a governed manner. It supports Agent Mode for autonomous tool execution and Code Mode for optimized token usage when agents orchestrate multiple tools. Furthermore, it provides MCP tool filtering per virtual key, ensuring granular control over agent capabilities.

9. How does it address "shadow AI" and endpoint governance?

Employees often use AI tools on their personal devices or through unmanaged services, creating "shadow AI" that bypasses traditional IT oversight. This poses significant data leakage and compliance risks. An effective gateway strategy should extend governance to the endpoint, ensuring all AI traffic, regardless of its origin (desktop apps, browsers, coding agents), adheres to organizational policies.

Bifrost addresses the challenge of shadow AI by extending its gateway-level governance to the endpoint through Bifrost Edge. This alpha-stage capability allows administrators to govern which AI applications and MCP servers are permitted on company devices, enforcing the same virtual keys, budgets, and guardrails that protect gateway traffic. Edge runs on macOS, Windows, and Linux and can be deployed fleet-wide via MDM platforms such as Jamf and Microsoft Intune.

10. What are its deployment options and enterprise readiness?

The flexibility of deployment—whether self-hosted in a VPC, on-premises, or as a managed service—is a key consideration for data residency, security, and compliance in regulated industries. Enterprise readiness also involves features like clustering for high availability, robust RBAC, and integrations with existing enterprise identity systems.

Bifrost is an open-source gateway that offers flexible deployment options, including self-hosting in a VPC or on-premises environments, which is crucial for organizations with strict data residency requirements. Its enterprise version provides advanced features for clustering, adaptive load balancing, and user provisioning with OIDC, ensuring it can meet the demands of large-scale production deployments.

Choosing the right LLM gateway is a strategic decision that impacts the reliability, cost, security, and future readiness of AI applications. By systematically evaluating options against these questions, organizations can select a gateway that not only meets current needs but also scales with their evolving AI strategy. Teams can request a Bifrost demo or review its open-source repository to explore its capabilities.

Sources

Truefoundry. "What Is LLM Proxy?". June 12, 2026.
Portkey. "How to choose an AI gateway in 2025". September 5, 2025.
Ghosh, B. "LLM Traffic Control: Gateway or Router or Proxy". Medium. December 1, 2024.
Cequence.ai. "How LLM Gateways Work, 5 Key Features & How to Choose". July 2, 2026.
Tech Jacks Solutions. "Securing LLM Gateways: Threats, Hardening & Compliance (2026)". July 3, 2026.