AI Gateway vs Sidecar vs SDK: Deployment Comparison
Architecture Comparison

AI Gateway vs Sidecar vs SDK: Choosing a Runtime Enforcement Architecture

Gateway, sidecar, and SDK models enforce AI agent policy at different points in the request path. Gateways centralize enforcement at a network chokepoint, sidecars co-locate enforcement with each workload, and SDKs embed enforcement inside application code. The right choice depends on how much control you need over tool-call traffic, how distributed your infrastructure is, and who owns enforcement operationally.

Three Models, Three Enforcement Points

Each architecture answers the same question, where should a policy decision be checked, at a different distance from the workload.

Gateway

A centralized network chokepoint for tool calls, shared across all agents.

Sidecar

A co-located proxy attached to each individual workload instance.

SDK

Enforcement embedded directly in application code, compiled into the agent.

Where Enforcement Happens

  1. 1

    Gateway

    Sits between agents and downstream tools or models, inspecting and authorizing every call before it leaves the network boundary. All agents share the same enforcement point.

  2. 2

    Sidecar

    Runs as a companion process to a single agent or service instance, intercepting calls at the workload level. Each instance has its own enforcement copy.

  3. 3

    SDK

    Runs inside the agent's own process, invoked directly by application code before a tool call or model request is made. Enforcement is compiled into the application.

Evaluation Criteria for Choosing an Architecture

Use these questions to determine which enforcement model, or combination of models, fits your infrastructure and operating structure.

  • Confirm whether all agent-to-tool traffic can be routed through a single network point, or whether workloads communicate outside a central path.
  • Assess your team's ability to operate and patch per-instance sidecars across your orchestration environment.
  • Determine whether your engineering teams can consistently integrate and maintain an SDK across every agent and application.
  • Identify your latency tolerance and whether an additional network hop, local proxy, or in-process check fits your production constraints.
  • Clarify who owns enforcement operationally: a central platform team, embedded application teams, or both.
  • Map your audit and compliance evidence requirements to where logs would actually be generated under each model.

Defining the Three Architectures

Enterprise teams deploying AI agents generally choose between three enforcement architectures, and the distinction matters because it determines where policy decisions actually happen. A gateway architecture places enforcement at a shared network point that all agent traffic passes through before reaching tools, models, or external services. A sidecar architecture places enforcement in a process that runs alongside each workload, typically in the same pod or host, intercepting traffic locally rather than centrally. An SDK architecture places enforcement inside the application itself, as a library that the agent code calls directly when making tool or model requests. Each model answers the same question, where should policy be checked, with a different tradeoff between centralization and proximity to the workload.

Identity, Least Privilege, and Audit Across Models

Enforcing least privilege and maintaining an audit trail depend on where policy decisions are made and how consistently that point sees traffic. A gateway can apply consistent identity checks and permission scoping across every agent that routes through it, and because logs originate from one place, audit evidence is easier to centralize and review. The tradeoff is that a gateway only sees what passes through it, so any tool call made outside that path is invisible to the enforcement layer. A sidecar keeps enforcement close to the workload, which can capture local calls a gateway would miss, but audit and identity data is now distributed across many instances, requiring aggregation before it is useful for compliance review. An SDK ties enforcement directly to the calling code, which can enable fine-grained permission checks tied to the specific function or agent making the request, but it depends entirely on developers calling the SDK correctly and consistently, and audit logging quality is only as good as what each integration actually emits. In multi-tool or multi-model environments, this difference compounds, since agents rarely follow a single, uniform code path.

Common Questions

Can gateway, sidecar, and SDK approaches be combined?

Enterprises can layer these models, for example using a gateway for broad network-level policy and an SDK for fine-grained, code-level checks within specific agents. Combining models increases coverage but also increases the number of enforcement points to maintain and keep in sync.

Does the Model Context Protocol require a specific enforcement architec