---
description: Compare LLM gateway deployment patterns, centralized, sidecar, edge, and in-VPC, on latency, governance, and data residency, and pick the right one.
title: "LLM Gateway Deployment Patterns: Centralized, Sidecar, Edge and In-VPC"
image: https://storage.ghost.io/c/84/03/8403f2f6-141c-411a-8f55-a32d4291533e/content/images/size/w1200/2026/10/llm-gateway-deployment-patterns-centralized-sidecar-edge-and-bifrost-1920x1080@2x.png
---

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**TL;DR**

- An LLM gateway can run as a shared central cluster, as a sidecar next to each service, at the edge close to callers, or inside a private VPC or on-premise network.
- Centralized deployment is the default for most teams because budgets, rate limits, and keys are enforced in one place.
- Sidecars give per-service isolation, but each replica keeps its own budget and rate-limit counters unless the replicas are clustered.
- In-VPC and on-premise deployments keep prompts and responses inside your network and are the usual answer to data residency requirements.
- Bifrost supports all four patterns and adds 11 microseconds of overhead per request at 5,000 RPS.

An LLM gateway deployment pattern is the decision about where the gateway runs relative to the applications that call models: one shared cluster, one instance per service, close to the caller, or inside a private network. [Bifrost](https://www.getmaxim.ai), the [open-source LLM gateway written in Go](https://github.com/maximhq/bifrost) by Maxim AI, runs in all four positions, from a Kubernetes cluster to an air-gapped data center. This guide compares the centralized, sidecar, edge, and in-VPC patterns on latency, governance, data residency, and operational cost, then shows how to choose.

## What Is an LLM Gateway Deployment Pattern?

A deployment pattern defines where the gateway process runs and which traffic passes through it. The choice decides how far each request travels before it reaches the gateway, whether budgets and rate limits are enforced in one place or many, and whether prompts ever leave your network.

![Four rows compare LLM gateway placements: a shared central cluster, a sidecar per service, an edge layer feeding a central gateway, and a gateway inside a VPC](https://articles-images-cdn.t3.tigrisfiles.io/diagrams/llm-gateway-deployment-patterns/llm-gateway-deployment-patterns-map.png)

*Figure 1: The four patterns differ in one thing: how far the gateway sits from the code that sends the request.*

Figure 1 shows the four placements side by side. The gateway's responsibilities do not change between them; routing, failover, virtual keys, budgets, and logging work the same way. What changes is the topology, and topology decides which of those responsibilities stay consistent at scale. For the components themselves, see this [full breakdown of LLM gateway architecture](https://www.getmaxim.ai/articles/understanding-llm-gateways-a-full-architecture-breakdown/).

## Centralized LLM Gateway

A centralized LLM gateway is a shared cluster that every application calls over the network. It is the most common deployment pattern because policy lives in one place: one set of provider keys, one budget hierarchy, one log store, and one team operating it. The cost is one extra network hop between each service and the gateway.

A centralized deployment works well when:

- **Several teams share providers.** One budget and rate-limit hierarchy covers every consumer.
- **Platform teams own AI infrastructure.** A single deployment is easier to upgrade, audit, and secure.
- **Applications run in one region or cloud.** The network hop to the gateway stays in the low milliseconds.

[The Bifrost gateway](https://www.getmaxim.ai) runs as a long-running container that listens on port 8080 with a `GET /health` endpoint. The [official Helm chart](https://docs.getbifrost.ai/deployment-guides/helm) deploys it to any Kubernetes cluster, including EKS, GKE, and AKS, and the [deployment guides](https://docs.getbifrost.ai/deployment-guides/overview) also cover Amazon ECS, Google Cloud Run, Terraform, and plain Docker.

One detail matters at scale. A single open source Bifrost instance handles around 3,000 to 5,000 RPS, and multiple replicas behind a load balancer need [cluster mode](https://docs.getbifrost.ai/deployment-guides/helm/cluster) to share configuration, virtual keys, and rate-limit counters; a shared database alone does not keep replicas in sync. These [Helm deployment best practices](https://www.getmaxim.ai/articles/deploying-bifrost-on-kubernetes-with-helm-best-practices-and-common-pitfalls/) cover the common pitfalls.

## Sidecar Pattern for LLM Gateways

The sidecar pattern runs a gateway instance next to each service, in the same Kubernetes pod or host, so the application calls it over localhost. It removes the network hop to a shared cluster and isolates each service's gateway from the others, at the cost of running and configuring many gateway instances instead of one.

The pattern itself is a standard distributed-systems design. Microsoft's [sidecar pattern guidance](https://learn.microsoft.com/en-us/azure/architecture/patterns/sidecar) describes deploying supporting components alongside an application, and Kubernetes supports it natively through [sidecar containers](https://kubernetes.io/docs/concepts/workloads/pods/sidecar-containers/) that start before the main container and run for the life of the pod.

![Two service pods each run a gateway sidecar with its own budget and rate limit counters, while cluster mode syncs counters between gateway replicas over gossip](https://articles-images-cdn.t3.tigrisfiles.io/diagrams/llm-gateway-deployment-patterns/llm-gateway-deployment-patterns-sidecar-state.png)

*Figure 2: A team budget enforced by independent sidecars is really one budget per replica, so shared limits need cluster mode.*

Figure 2 shows the trade-off most teams miss. [Bifrost as a gateway](https://www.getmaxim.ai) keeps provider configs, budgets, and usage counters in memory, so each sidecar enforces its own copy. A $1,000 team budget enforced by five independent sidecars allows up to $5,000 of spend. Shared limits across sidecars require cluster mode, which syncs counters between replicas through gossip-based state sync in Bifrost Enterprise, as described in this guide to [Bifrost cluster mode for high availability](https://www.getmaxim.ai/articles/bifrost-cluster-mode-high-availability-for-enterprise-ai-deployments/).

For Go services, there is a lighter version of the same idea. The [Bifrost Go SDK](https://docs.getbifrost.ai/quickstart/go-sdk/setting-up) embeds the gateway core in-process, so the application gets routing and fallbacks without a separate container.

## Edge Gateway Pattern

An edge gateway runs close to where requests start: in the region nearest each group of users, or on the machines where people use AI tools. The edge layer handles routing and enforcement locally, while a central gateway remains the source of policy. Edge deployment reduces latency for distributed users and covers traffic that never passes through a data center.

There are two forms of edge deployment:

- **Regional edge.** Gateway pods run in each region that serves users. Bifrost keeps its database out of the request path, so [cross-region deployments](https://docs.getbifrost.ai/enterprise/moving-from-oss/cross-region) load config once at startup and write logs asynchronously, and pods can run far from the PostgreSQL primary without adding inference latency.
- **Endpoint edge.** With AI Gateway + Bifrost Edge, the gateway remains the control plane for virtual keys, budgets, guardrails, and audit logs, and [Bifrost Edge](https://docs.getbifrost.ai/edge/overview) runs on each company machine to route desktop AI apps, browser AI, and coding agents through it. Bifrost Edge is currently in alpha.

Endpoint edge is how teams close the gap left by shadow AI. Bifrost Edge rolls out silently through [device management platforms such as Jamf, Intune, and Kandji](https://docs.getbifrost.ai/edge/deployment-mdm) on macOS, Windows, and Linux, and users sign in once with existing SSO. This walkthrough of [deploying AI governance with Bifrost Edge and the Bifrost gateway](https://www.getmaxim.ai/articles/deploying-ai-governance-for-enterprises-with-bifrost-edge-bifrost-gateway/) covers a full rollout.

## In-VPC and On-Premise LLM Gateway

An in-VPC or on-premise LLM gateway runs entirely inside your private network, so prompts, responses, and provider credentials never pass through a third-party control plane. This pattern is the usual answer to data residency and regulatory requirements, and it is the only option for air-gapped environments with no outbound internet access.

[Bifrost Enterprise](https://www.getmaxim.ai/enterprise) supports [in-VPC deployments](https://docs.getbifrost.ai/enterprise/invpc-deployments) on AWS, Google Cloud, Azure, Cloudflare, and Vercel, and on-premise deployments on Kubernetes or Docker. Traffic to cloud model providers can stay on private paths through services such as [AWS PrivateLink](https://docs.aws.amazon.com/vpc/latest/privatelink/what-is-privatelink.html), which connects a VPC to supported services without exposing traffic to the public internet.

Fully air-gapped environments need one more step. Bifrost normally fetches model pricing and parameter datasheets at startup; in [air-gapped deployments](https://docs.getbifrost.ai/deployment-guides/how-to/airgapped) those files are downloaded once and loaded from the local filesystem through `file://` paths in `config.json`. Paired with self-hosted model servers such as Ollama, vLLM, or SGLang, the full request path stays on premise. The [AI gateways built for air-gapped and in-VPC LLM deployments](https://www.getmaxim.ai/articles/top-5-ai-gateways-for-air-gapped-ai-and-in-vpc-llm-deployments/) compares the options in this category.

## LLM Gateway Deployment Patterns Compared

The four deployment patterns trade latency, governance consistency, and operational effort against each other. Centralized deployments are simplest to govern, sidecars minimize per-service latency, edge deployments serve distributed users and endpoints, and in-VPC deployments satisfy data residency. The table below summarizes the differences.

| Pattern | Where the gateway runs | Latency added | Governance consistency | Operational effort | Best fit |
| --- | --- | --- | --- | --- | --- |
| Centralized | Shared cluster | One network hop | High: one policy source | Low: one deployment | Most teams sharing providers |
| Sidecar | Next to each service | Localhost only | Low unless clustered | High: many instances | Latency-critical, isolated services |
| Edge | Regions or employee machines | Lowest for distant callers | High when a central gateway owns policy | Medium | Global users, shadow AI coverage |
| In-VPC / on-prem | Inside your private network | One private hop | High | Medium to high | Regulated data, air-gapped sites |

The patterns combine in practice. A common enterprise layout runs a centralized cluster inside the company VPC, regional replicas for distant users, and Bifrost Edge on employee machines, all governed from one policy source, as in this [enterprise AI gateway reference architecture](https://www.getmaxim.ai/articles/enterprise-ai-gateway-a-reference-architecture-for-scaling-llms-safely/).

## How to Choose an LLM Gateway Deployment Pattern

Choose an LLM gateway deployment pattern by answering three questions in order: must data stay inside your network, does traffic start on employee machines, and does any service need strict isolation from the rest. Data residency decides first because it rules options out entirely; the other two refine the layout.

![Decision flow that picks in-VPC or on-premise when data must stay private, edge when traffic starts on employee machines, sidecar for strict per-service isolation, and centralized otherwise](https://articles-images-cdn.t3.tigrisfiles.io/diagrams/llm-gateway-deployment-patterns/llm-gateway-deployment-patterns-decision.png)

*Figure 3: Data residency decides first, traffic origin second, and isolation needs third; most teams land on centralized.*

Following Figure 3, three signals usually point to a pattern change:

- **A compliance review asks where prompts are stored.** Move the gateway in-VPC or on-premise; this comparison of [AI gateways for data residency and multi-region deployments](https://www.getmaxim.ai/articles/top-5-ai-gateways-for-data-residency-and-multi-region-deployments-in-2026/) covers the requirements.
- **AI usage on laptops grows faster than API traffic.** Add the endpoint edge so that traffic inherits the same budgets and guardrails.
- **One service needs latency isolation.** Run a sidecar for that service only and keep everything else centralized.

Whatever the layout, the gateway itself should not become the bottleneck. [The open-source Bifrost gateway](https://www.getmaxim.ai) adds 11 microseconds of overhead per request at 5,000 requests per second in [sustained benchmarks](https://www.getmaxim.ai/resources/benchmarks) and reaches [25+ providers and 10,000+ models](https://docs.getbifrost.ai/providers/supported-providers/overview) through one OpenAI-compatible API, so the deployment choice can follow governance needs rather than performance limits.

## Running Bifrost in Each Pattern

Bifrost uses the same binary and configuration model in every pattern, so moving between them is a deployment change, not a migration. Teams typically start with a single centralized instance, add cluster mode as traffic grows, and extend to the edge or into a VPC as governance requirements arrive.

| Pattern | Bifrost deployment path |
| --- | --- |
| Centralized | Helm chart on Kubernetes, ECS, or Cloud Run, with cluster mode for multiple replicas |
| Sidecar | Bifrost container in each pod, or the Go SDK embedded in Go services |
| Edge | Regional replicas with cross-region discovery, plus Bifrost Edge on employee machines |
| In-VPC / on-prem | Enterprise images on private Kubernetes or Docker, with local datasheets for air-gapped sites |

Applications connect the same way in every pattern: Bifrost works as a [drop-in replacement](https://docs.getbifrost.ai/features/drop-in-replacement) for OpenAI, Anthropic, and other SDKs by changing the base URL. Governance follows the same model too, with [virtual keys](https://docs.getbifrost.ai/features/governance/virtual-keys) and budgets defined once, as described in the [Bifrost governance overview](https://www.getmaxim.ai/resources/governance).

Teams comparing self-hosted options more broadly can review the [best self-hosted AI gateways](https://www.getmaxim.ai/articles/best-self-hosted-ai-gateway-in-2026/), and the [LLM gateway architecture guide](https://www.getmaxim.ai/articles/understanding-llm-gateways-a-full-architecture-breakdown/) explains how the components fit together inside any of these layouts.

## Frequently Asked Questions

### What is a sidecar?

A sidecar is a helper process or container deployed alongside a main application, sharing its host or Kubernetes pod and lifecycle. It adds a capability such as proxying, logging, or security without changing the application's code. An LLM gateway deployed as a sidecar lets a service reach model providers through localhost instead of a shared cluster.

### What is the sidecar pattern in Kubernetes?

In Kubernetes, the sidecar pattern places a supporting container in the same pod as the application container, so both share networking and storage. Kubernetes supports native sidecar containers that start before the main container and run for the pod's lifetime. For LLM gateways, the trade-off is that each sidecar keeps separate budget and rate-limit state unless the replicas are clustered.

### Can an LLM gateway run on premise?

Yes. An LLM gateway can run on premise or in a private VPC so prompts and provider credentials stay inside your network. Bifrost Enterprise supports on-premise deployments on Kubernetes or Docker, and air-gapped deployments load pricing datasheets from local files. Pairing the gateway with self-hosted model servers keeps the full request path on premise.

### What is BYOC?

BYOC, or bring your own cloud, is a deployment model where a vendor's software runs inside the customer's own cloud account rather than the vendor's infrastructure. For an LLM gateway, BYOC and in-VPC deployment serve the same goal: the customer controls the network, data, and credentials while the vendor supports the software.

### Which LLM gateway deployment pattern is best?

A centralized LLM gateway is the best default for most teams because policy, keys, and logs live in one place. Data residency requirements point to in-VPC or on-premise, employee AI usage points to adding an endpoint edge, and strict per-service latency isolation points to sidecars. Many enterprises combine centralized, edge, and in-VPC patterns under one policy source.

### Does an LLM gateway need high availability?

Yes, once production traffic depends on it, because the gateway sits in every model request path. High availability means running several replicas across zones with shared state for budgets and rate limits. Bifrost Enterprise provides this through [clustering](https://docs.getbifrost.ai/enterprise/clustering) with automatic service discovery, gossip-based state sync, and zero-downtime rolling updates.

## Deploy Bifrost in the Pattern That Fits

Every LLM gateway deployment pattern solves a different constraint: centralized for consistent governance, sidecar for isolation, edge for distributed users and endpoints, and in-VPC for data residency. Bifrost runs in all of them with the same configuration and governance model, from a single container to a multi-region cluster. [Book a demo](https://www.getmaxim.ai/book-a-demo) to plan the right LLM gateway deployment for your environment.

## Read next

[![What Breaks in Production Without an AI Gateway: 6 Failure Patterns](https://storage.ghost.io/c/84/03/8403f2f6-141c-411a-8f55-a32d4291533e/content/images/size/w720/2026/10/what-breaks-in-production-without-an-ai-gateway-6-failure-pa-bifrost-1920x1080@2x.png) An AI gateway is the control layer between applications and model providers. This guide walks through six failure patterns that appear when LLM apps run in production without one, from outages and rate limits to key sprawl and blind spots, and the gateway control that prevents each.](https://www.getmaxim.ai/articles/production-failures-without-ai-gateway/)

[![Top 5 AI Gateways for Controlling Shadow AI in 2026](https://storage.ghost.io/c/84/03/8403f2f6-141c-411a-8f55-a32d4291533e/content/images/size/w720/2026/10/top-5-ai-gateways-for-controlling-shadow-ai-bifrost-isometric.png) Shadow AI is the use of AI tools, models, and MCP servers that security teams have not approved and cannot see. This guide ranks five AI gateways for controlling it, including Bifrost with Bifrost Edge, Kong AI Gateway, Cloudflare AI Gateway, and Gravitee.](https://www.getmaxim.ai/articles/top-5-ai-gateways-for-controlling-shadow-ai/)

[![Top 5 AI Gateways for SSO and RBAC in 2026](https://storage.ghost.io/c/84/03/8403f2f6-141c-411a-8f55-a32d4291533e/content/images/size/w720/2026/10/top-5-ai-gateways-for-sso-and-rbac-in-2026-bifrost-isometric.png) AI gateways with SSO and RBAC let enterprises tie every model request and every configuration change to a corporate identity. This guide compares Bifrost, Kong AI Gateway, Azure API Management, Gravitee, and Cloudflare AI Gateway on identity, roles, provisioning, and audit.](https://www.getmaxim.ai/articles/top-5-ai-gateways-for-sso-and-rbac-in-2026/)

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