---
description: Compare five AI gateway options for automatic failover across Bedrock, Vertex AI, and Azure OpenAI on 429 handling, weighted keys, and cloud-native auth.
title: Top 5 AI Gateways for Automatic Failover Across Bedrock, Vertex AI, and Azure OpenAI
image: https://storage.ghost.io/c/84/03/8403f2f6-141c-411a-8f55-a32d4291533e/content/images/size/w1200/2026/10/top-5-ai-gateways-for-automatic-failover-across-bedrock-vert-bifrost-isometric.png
---

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**TL;DR**

- An AI gateway for hyperscaler-hosted models must absorb 429s, 5xx errors, and timeouts from Bedrock, Vertex AI, and Azure OpenAI and fail over to another cloud without application changes.
- Bifrost rotates keys on 429 and 401/403 errors, retries 5xx and network errors with backoff, and on exhaustion or timeout walks a cross-provider fallback chain where each provider gets its own retry budget.
- Bifrost authenticates with IAM roles and STS AssumeRole on AWS, service accounts and Workload Identity Federation on Google Cloud, and managed identity or Entra ID on Azure.
- LiteLLM, Kong AI Gateway, Envoy AI Gateway, and Azure API Management each cover parts of this problem with different failover triggers, identity support, and deployment models.
- Bifrost adds 11 microseconds of overhead per request at 5,000 RPS in sustained benchmarks.

Enterprises that run Claude on both AWS Bedrock and Google Vertex AI, and GPT models on Azure OpenAI, hit a recurring failure pattern: per-region quotas return 429s, regional incidents return 5xx errors, and slow responses time out, while each cloud expects a different identity system. An AI gateway solves this by putting one OpenAI-compatible endpoint in front of all three clouds and handling retries, key rotation, and cross-cloud failover centrally. [Bifrost](https://www.getmaxim.ai/bifrost), the [open-source AI gateway written in Go](https://github.com/maximhq/bifrost) and built by Maxim AI, is the best choice for enterprises running mission-critical AI workloads that require best-in-class performance, scalability, and reliability. This guide compares five gateways on that job.

## Why Hyperscaler-Hosted Models Need an AI Gateway

Hyperscaler-hosted models need an AI gateway because each cloud enforces its own quotas, regional capacity, and identity model, and none of them fails over to another vendor's cloud. A gateway turns three separate integrations into one endpoint with shared retry logic, weighted routing across keys and regions, and a fallback chain spanning AWS, Google Cloud, and Azure.

The failure signals look similar but come from different mechanisms:

- **Azure OpenAI** assigns quota per subscription, region, model, and deployment type. Microsoft's [Azure OpenAI quota documentation](https://learn.microsoft.com/en-us/azure/ai-foundry/openai/how-to/quota) states that requests beyond the TPM or RPM limit receive a 429, and that failed requests still count toward the limit.
- **AWS Bedrock** cross-Region inference routes requests across AWS Regions within a geography or globally. AWS's [cross-Region inference guide](https://docs.aws.amazon.com/bedrock/latest/userguide/cross-region-inference.html) notes the traffic stays on the AWS network, so the failover boundary stops at AWS.
- **Google Vertex AI** serves Claude through global, multi-region, and regional endpoints. Anthropic's [Claude on Google Cloud guide](https://platform.claude.com/docs/en/build-with-claude/claude-on-vertex-ai) describes the global endpoint as dynamic routing for availability, while regional endpoints are required for single-region data residency.

None of these moves a request from Bedrock to Vertex AI when an AWS account is throttled. That cross-cloud step belongs in an [AI gateway layer between applications and providers](https://www.getmaxim.ai/articles/what-is-an-ai-gateway-architecture-features-and-why-it-matters/).

![Three applications send OpenAI-compatible requests to the Bifrost AI gateway, which signs calls to AWS Bedrock, Google Vertex AI, and Azure OpenAI with each cloud's native identity](https://articles-images-cdn.t3.tigrisfiles.io/diagrams/ai-gateway-failover-bedrock-vertex-azure-openai/ai-gateway-failover-cross-cloud-identity.png)

*Figure 1: Applications hold one gateway credential, while IAM roles, service accounts, and managed identities stay inside Bifrost.*

As Figure 1 shows, the gateway's second job is identity. Applications should not carry AWS access keys, GCP service account JSON, and Azure client secrets; they authenticate to the gateway with one scoped key, such as a Bifrost [virtual key](https://docs.getbifrost.ai/features/governance/virtual-keys).

## Key Criteria for Cross-Cloud Failover

A cross-cloud gateway should be judged on which errors trigger failover, how it balances load across keys and regions, how it authenticates to each cloud natively, how it maps one model name to different provider IDs, and where it can be deployed.

| Criterion | What to verify | Why it matters here |
| --- | --- | --- |
| Failover triggers | 429, 5xx, network errors, and timeouts handled explicitly | 429s signal quota, 5xx signal availability; they need different retry behavior |
| Retry budget per provider | Retries apply to each provider in the chain | A throttled Bedrock account should not spend the budget meant for Vertex AI |
| Weighted load balancing | Weights across keys, regions, and providers | Spreads TPM quota across Azure deployments or Bedrock regions |
| Native cloud identity | IAM roles, GCP service accounts and WIF, Azure managed identity | Removes long-lived static keys from application code |
| Model mapping | One model name mapped to Bedrock inference profiles, Vertex names, Azure deployment IDs | Lets the same Claude request fail over between Bedrock and Vertex AI |
| Deployment model | Self-hosted, in-VPC, Kubernetes, or managed | Regulated teams need the gateway inside their own network |

Model mapping and native identity separate a general gateway from one built for hyperscalers; the [LLM gateway buyer's guide](https://www.getmaxim.ai/bifrost/resources/buyers-guide) covers the broader evaluation checklist.

## Top 5 AI Gateways Compared at a Glance

All five gateways route to Bedrock, Vertex AI, and Azure OpenAI, but differ in failover triggers and cloud identity. Bifrost combines key rotation on 429s, per-provider retry budgets, and native identity for all three clouds in one open-source Go gateway.

| Gateway | Failover triggers | Load balancing | Bedrock auth | Vertex AI auth | Azure OpenAI auth | Deployment |
| --- | --- | --- | --- | --- | --- | --- |
| **Bifrost** | 429 and 401/402/403 (key rotation), 5xx, network errors, timeouts | Weighted keys, weighted providers per virtual key, adaptive (Enterprise) | Static keys, inherited IAM role, STS AssumeRole, API key | Service account JSON, ADC, GKE WIF | DefaultAzureCredential, Entra ID, API key | Self-hosted, Kubernetes, in-VPC, clustered |
| **LiteLLM** | Failed deployments in an order tier, including 429s and connection errors | Weighted shuffle, latency-based, usage-based, least-busy | boto3 chain, role assumption, web identity | Service account JSON or ADC | API key, Azure AD token, Entra ID, DefaultAzureCredential | Self-hosted Python proxy or SDK |
| **Kong AI Gateway** | Errors and timeouts by default; configurable criteria such as http\_429 | Weighted round-robin, lowest-latency, priority, semantic, usage-based | Access keys, assume role ARN | Service account JSON | Managed identity, client credentials | Kong Gateway plugin |
| **Envoy AI Gateway** | Retry policy on status codes and connect failures; priority-ordered backends | Envoy Gateway traffic policies | OIDC with AWS STS | ADC, service account keys, WIF | Entra ID tokens or API key | Kubernetes |
| **Azure API Management** | Circuit breaker on status-code ranges with Retry-After; priority groups | Round-robin, weighted, priority, session-aware | Not published | Not published | Managed identity | Azure managed service |

"Not published" means the documentation reviewed did not describe that capability. Bifrost's [enterprise scalability](https://www.getmaxim.ai/bifrost/resources/enterprise-scalability) page covers how the top entry behaves under production load.

## 1. Bifrost

The [Bifrost AI gateway](https://www.getmaxim.ai/bifrost) is high-performance and open source, and unifies 25+ providers and 10,000+ models behind one OpenAI-compatible API, including AWS Bedrock, Google Vertex AI, and Azure OpenAI. It classifies every upstream failure, rotates or retries accordingly, and then fails over across clouds with a fresh retry budget per provider.

**Best for:** Bifrost is built for enterprises running mission-critical AI workloads that require best-in-class performance, scalability, and reliability. It serves as a centralized AI gateway to route, govern, and secure all AI traffic across models and environments with ultra low latency. Bifrost unifies LLM gateway, MCP gateway, and Agents gateway capabilities into a single platform. Designed for regulated industries and strict enterprise requirements, it supports air-gapped deployments, VPC isolation, and on-prem infrastructure. It provides full control over data, access, and execution, along with robust security, policy enforcement, and governance capabilities.

### How Bifrost handles 429s, 5xx errors, and timeouts

[Retries and fallbacks](https://docs.getbifrost.ai/features/fallbacks) are nested in Bifrost: retries run inside one provider, and fallbacks move to the next provider once that provider's `max_retries` budget is spent.

![A failed provider call is classified by status: 429 rotates keys, auth errors mark keys dead, 5xx errors retry the same key, and timeouts move to the fallback](https://articles-images-cdn.t3.tigrisfiles.io/diagrams/ai-gateway-failover-bedrock-vertex-azure-openai/ai-gateway-failover-error-classification.png)

*Figure 2: Each failure class gets its own handling inside a provider; once that provider is exhausted, the request moves down the fallback chain.*

Figure 2 shows the classification applied to each failed attempt:

- **429 rate limits** rotate to another key in the pool and still apply backoff, because quota is often shared at the account level.
- **401, 402, and 403 errors** mark the key dead for that request and rotate without backoff. If every key is dead, Bifrost returns `502 upstream_credentials_exhausted`.
- **5xx errors and network failures** reuse the same key with exponential backoff and jitter.
- **Timeouts**, bounded per provider by `default_request_timeout_in_seconds` in the [provider network configuration](https://docs.getbifrost.ai/deployment-guides/config-json/providers), end that provider's attempt with a 504 and hand the request to the next fallback.
- **400, 404, and 422 validation errors** are not retried.

Azure OpenAI streams can report an error inside an HTTP 200 response; Bifrost buffers startup events so that error still reaches retry and fallback logic.

### Cross-cloud fallback chains

A request carries a `fallbacks` array of `provider/model` strings. Each fallback runs as a fresh request with plugins re-applied, and `extra_fields.provider` records which cloud served it. Every retry and fallback transition is written to the request's routing log trail. The [retries, fallbacks, and circuit breakers guide](https://www.getmaxim.ai/articles/retries-fallbacks-and-circuit-breakers-in-llm-apps-a-production-guide/) covers these patterns in depth.

### Weighted load balancing across keys, regions, and clouds

Bifrost uses [weighted key selection](https://docs.getbifrost.ai/features/keys-management), so a provider can hold several keys, each pinned to a region and weighted by its quota; Azure keys map model names to deployment IDs and Bedrock keys map them to inference profiles. One level up, [governance-based routing](https://docs.getbifrost.ai/providers/provider-routing) lets a virtual key spread one model across providers by weight, and unselected providers become fallbacks automatically. Bifrost Enterprise adds [adaptive load balancing](https://docs.getbifrost.ai/enterprise/adaptive-load-balancing), recomputing route weights every 5 seconds from error rate and latency.

### Native identity for AWS, Google Cloud, and Azure

- [\*\*AWS Bedrock](https://docs.getbifrost.ai/providers/supported-providers/bedrock):\*\* SigV4 signing with explicit credentials, the inherited credential chain (IRSA, ECS task role, EC2 instance profile), STS AssumeRole with `role_arn` and `external_id`, or a Bedrock API key.
- [\*\*Google Vertex AI](https://docs.getbifrost.ai/providers/supported-providers/vertex):\*\* service account JSON, Application Default Credentials, or GKE Workload Identity Federation. API keys work for Gemini only, so Claude on Vertex AI uses a service account or ADC.
- [\*\*Azure OpenAI](https://docs.getbifrost.ai/providers/supported-providers/azure):\*\* `DefaultAzureCredential` for managed identity and AKS workload identity, an Entra ID service principal, or an API key.

Enterprise deployments can keep remaining secrets in AWS Secrets Manager, GCP Secret Manager, or HashiCorp Vault through [secret management](https://docs.getbifrost.ai/enterprise/secret-management).

### Performance and deployment

Bifrost adds 11 microseconds of overhead per request at 5,000 RPS with a 100% success rate in sustained [benchmarks](https://www.getmaxim.ai/bifrost/resources/benchmarks). Because the database stays off the request path, [cross-region deployments](https://docs.getbifrost.ai/enterprise/moving-from-oss/cross-region) can run replicas in several clouds at once.

Regulated teams can run the gateway privately with [in-VPC deployments](https://docs.getbifrost.ai/enterprise/invpc-deployments) and [clustering](https://docs.getbifrost.ai/enterprise/clustering) for high availability.

## 2. LiteLLM

LiteLLM is an open-source Python SDK and proxy that routes across many providers, including Bedrock, Vertex AI, and Azure OpenAI. Its router groups deployments under one model name, retries failures, and cools down failing deployments.

LiteLLM's routing documentation describes fallbacks from `order=1` to `order=2` deployments on failures including connection errors and 429s, with cooldowns triggered immediately on 429s and on 401, 404, and 408 responses. A `RetryPolicy` sets retries per error type, including timeouts and rate limits. Strategies include weighted simple shuffle (the documented production default), latency-based, usage-based, and least-busy routing; the docs caution against usage-based routing in production because of Redis overhead.

For identity, LiteLLM uses boto3 for Bedrock with parameters such as `aws_role_name` and `aws_web_identity_token`, service account JSON or ADC for Vertex AI, and API keys, Entra ID, or `DefaultAzureCredential` for Azure.

**Best for:** Python-centric teams that want many routing strategies and are comfortable operating a Python service under production load. Teams evaluating a move can compare a [high-performance LiteLLM alternative](https://www.getmaxim.ai/bifrost/resources/litellm-alternative) built in Go.

## 3. Kong AI Gateway

Kong AI Gateway extends Kong Gateway with AI plugins, chiefly AI Proxy Advanced, which load balances across model targets on Azure OpenAI, Amazon Bedrock, Vertex AI, and other providers.

Kong documents weighted round-robin, lowest-latency (peak EWMA), priority groups that fall back only when higher groups are unavailable, semantic routing, and usage-based balancing. Failover follows `failover_criteria`: by default, retries occur on connection errors and timeouts, and teams can add criteria such as `http_429` and `http_500`.

For identity, the plugin reference includes `aws_assume_role_arn` for Bedrock, `gcp_use_service_account` for Vertex AI, and `azure_use_managed_identity` for Azure OpenAI.

**Best for:** teams already standardized on Kong that want AI routing as plugins next to existing API policies. The [AI gateway load balancing guide](https://www.getmaxim.ai/articles/load-balancing-in-ai-gateway-a-comprehensive-guide/) explains how these algorithms compare in practice.

## 4. Envoy AI Gateway

Envoy AI Gateway is an open-source, Kubernetes-native gateway built on Envoy Gateway; its project site now presents it as Agent Router, an Agentic AI Foundation project with the same code and maintainers.

Provider fallback is a prioritized list of `backendRefs` in an `AIGatewayRoute`, with retries defined in a `BackendTrafficPolicy` (retry counts, timeouts, backoff, and triggers such as connect failures and specific status codes). Documented triggers are network errors, 5xx responses, and health check failures.

Upstream authentication is a strength: the docs describe OIDC with AWS STS for Bedrock, short-lived Entra ID tokens for Azure OpenAI, and workload federation with Google STS for Vertex AI, including Anthropic models on Vertex AI.

**Best for:** Kubernetes platform teams that want gateway behavior as CRDs and will compose resilience from Envoy traffic policies. Teams wanting the same target with a purpose-built AI gateway can run Bifrost on [Kubernetes](https://docs.getbifrost.ai/deployment-guides/k8s).

## 5. Azure API Management

Azure API Management (APIM) adds AI gateway capabilities to Microsoft's managed API platform, governing Azure OpenAI and Microsoft Foundry traffic alongside existing APIs.

APIM backend pools support round-robin, weighted, priority-based, and session-aware load balancing with up to 30 backends per pool. Circuit breaker rules trip on failures within a status-code range and can honor `Retry-After`, and Microsoft recommends them for Azure OpenAI 429s. Microsoft notes that balancing and tripping are approximate across gateway instances, and the circuit breaker is unavailable in the Consumption tier.

APIM accepts models following the OpenAI, Anthropic Messages, or Vertex AI API schemas, including models on Amazon Bedrock, and authenticates to Azure AI services with managed identities. The reviewed documentation did not describe IAM role or GCP service account handling for Bedrock and Vertex AI backends.

**Best for:** Azure-centric enterprises focused on spreading load across Azure OpenAI deployments and PTU capacity. Teams with significant Bedrock or Vertex AI traffic can review [Azure AI gateway alternatives for multi-cloud LLM traffic](https://www.getmaxim.ai/articles/top-5-azure-ai-gateway-alternatives-for-multi-cloud-llm-traffic-in-2026/).

## Running Claude on Bedrock and Vertex AI Behind One Gateway

The most common cross-cloud pattern is the same Claude model on AWS Bedrock and Google Vertex AI, with Azure OpenAI as a different-model fallback. In Bifrost, a virtual key spreads Claude traffic across both clouds by weight, regional keys spread each cloud's quota, and a request-level fallback reaches Azure if both fail.

![A request with a virtual key passes budget and rate-limit filters, then weighted selection sends it to AWS Bedrock while Vertex AI becomes the fallback, each with region-weighted keys](https://articles-images-cdn.t3.tigrisfiles.io/diagrams/ai-gateway-failover-bedrock-vertex-azure-openai/ai-gateway-failover-weighted-virtual-key.png)

*Figure 3: Provider weights pick the primary cloud, key weights pick the region, and the unselected provider is appended as the fallback automatically.*

Bedrock gets two regional keys that assume an IAM role, Vertex AI gets an ADC key on the global endpoint, and an alias maps a shared model name to the Bedrock inference profile:

```json
{
  "providers": {
    "bedrock": {
      "keys": [
        { "name": "bedrock-us-east-1", "models": ["*"], "weight": 0.5,
          "aliases": { "claude-sonnet": "us.anthropic.<claude-model-id>" },
          "bedrock_key_config": { "region": "us-east-1", "role_arn": "env.AWS_ROLE_ARN" } },
        { "name": "bedrock-us-west-2", "models": ["*"], "weight": 0.5,
          "aliases": { "claude-sonnet": "us.anthropic.<claude-model-id>" },
          "bedrock_key_config": { "region": "us-west-2", "role_arn": "env.AWS_ROLE_ARN" } }
      ],
      "network_config": { "max_retries": 3, "default_request_timeout_in_seconds": 60 }
    },
    "vertex": {
      "keys": [
        { "name": "vertex-global-adc", "value": "", "models": ["*"], "weight": 1.0,
          "vertex_key_config": { "project_id": "env.VERTEX_PROJECT_ID", "region": "global", "auth_credentials": "" } }
      ]
    }
  }
}
```

The virtual key splits Claude traffic 60/40 between the clouds, with a monthly budget on Bedrock. When Bedrock's budget is exhausted, governance filters it out; when Bedrock returns 429s or 5xx errors past its retry budget, the auto-generated fallback sends the request to Vertex AI.

```json
{
  "provider_configs": [
    { "provider": "bedrock", "allowed_models": ["claude-sonnet"], "key_ids": ["*"], "weight": 0.6,
      "budgets": [{ "max_limit": 5000.0, "reset_duration": "1M" }] },
    { "provider": "vertex", "allowed_models": ["claude-sonnet"], "key_ids": ["*"], "weight": 0.4 }
  ]
}
```

To add Azure OpenAI as a last resort, the application passes `"fallbacks": ["azure/gpt-4o"]`, with the Azure key mapping `gpt-4o` to its deployment ID and authenticating through managed identity. The [guide to Bedrock, Vertex, Gemini, and Anthropic models through Bifrost](https://www.getmaxim.ai/articles/using-bedrock-vertex-gemini-and-anthropic-ai-models-through-bifrost/) walks through each provider, and the [governance resource page](https://www.getmaxim.ai/bifrost/resources/governance) covers budgets and rate limits. Coding-agent teams can follow [running Claude Code on Bedrock or Vertex through an enterprise AI gateway](https://www.getmaxim.ai/articles/running-claude-code-on-bedrock-vertex-or-your-own-models-with-an-enterprise-ai-gateway/).

## How to Choose the Right AI Gateway for Multi-Cloud Failover

The right choice depends on where your traffic runs and which platform you already operate. Teams with real traffic on two or more hyperscalers need native identity for every cloud and per-provider retry budgets; teams living mostly in one cloud can accept a gateway that is strongest there.

| If your situation is | Start with | Reason |
| --- | --- | --- |
| Claude on Bedrock and Vertex AI, GPT on Azure, regulated environment | Bifrost | Native identity for all three clouds, key rotation on 429s, self-hosted in your VPC |
| Python-heavy team trying many routing strategies | LiteLLM | Wide strategy menu in a Python proxy |
| Existing Kong Gateway estate | Kong AI Gateway | AI routing as plugins next to current API policies |
| Kubernetes team standardized on Envoy | Envoy AI Gateway | CRD-driven routing with short-lived upstream credentials |
| Azure-first organization with PTU capacity | Azure API Management | Managed backend pools and circuit breakers for Azure OpenAI |

Before production, test real failure modes: exhaust a Bedrock quota, revoke an Azure credential, and force a Vertex AI timeout, then confirm the logs show the expected path. Bifrost exposes those paths through [request logs](https://docs.getbifrost.ai/features/observability/default) and [Prometheus metrics](https://docs.getbifrost.ai/features/observability/prometheus).

For the scoring side of routing, see [what adaptive load balancing is](https://www.getmaxim.ai/articles/what-is-adaptive-load-balancing/), and for the general category, this [AI gateway architecture and features overview](https://www.getmaxim.ai/articles/what-is-an-ai-gateway-architecture-features-and-why-it-matters/).

## Frequently Asked Questions

### What does failover mean?

Failover means automatically sending a request to a backup system when the primary fails. In a gateway, failover moves a request from one provider or model to another after errors such as 429 rate limits, 5xx responses, or timeouts. In Bifrost, failover runs after the primary provider's retries are exhausted, and each fallback provider gets its own full retry budget.

### Which is better, load balancing or failover?

Neither replaces the other; production systems need both. Load balancing spreads healthy traffic so no single quota is exhausted, while failover handles requests that still fail. Bifrost combines weighted keys and weighted provider routing with retries and fallback chains, so balancing reduces how often 429s occur and failover absorbs the rest.

### Can an AI gateway fail over from Claude on Bedrock to Claude on Vertex AI?

Yes, if the gateway maps one model name to both providers' identifiers and authenticates to both clouds. Bifrost maps a shared alias to a Bedrock inference profile, calls Claude on Vertex AI with service account or ADC credentials, and lists Vertex AI as a fallback. The application sends the same model name throughout, and the response records which cloud served it.

### Does Bedrock cross-region inference replace an AI gateway?

No. Bedrock cross-Region inference routes requests across AWS Regions, which helps with regional capacity on AWS. It does not route to Vertex AI or Azure OpenAI, and it does not centralize identity, budgets, or logs across clouds. Many teams use both: cross-Region inference profiles as the Bedrock target, and an AI gateway for cross-cloud fallback and governance.

### How does an AI gateway handle Azure OpenAI rate limits?

A gateway handles Azure OpenAI rate limits by spreading traffic across deployments or regions and reacting to 429s without hammering the throttled key. Bifrost rotates to another weighted Azure key on a 429, applies backoff because quota can be shared at the account level, and falls back to another provider once retries run out. Backoff matters because failed requests still count toward Azure quotas.

### Is there an open source AI gateway for Bedrock, Vertex AI, and Azure OpenAI?

Yes. Bifrost is an open-source AI gateway written in Go that supports AWS Bedrock, Google Vertex AI, and Azure OpenAI with native authentication for each, automatic fallbacks, and weighted load balancing. LiteLLM and Envoy AI Gateway are also open source. Related comparisons include these [AI gateway platforms with automatic failover](https://www.getmaxim.ai/articles/top-ai-gateway-platforms-with-automatic-failover-in-2026/).

## Try Bifrost for Cross-Cloud Failover

Bifrost gives enterprises one AI gateway in front of AWS Bedrock, Google Vertex AI, and Azure OpenAI, with key rotation on 429s, retries on 5xx errors, timeout-bounded cross-cloud fallback chains, and native identity for every cloud. It runs inside your own infrastructure through [Bifrost Enterprise](https://www.getmaxim.ai/bifrost/enterprise). To see automatic failover across your own cloud accounts, [book a demo with the Bifrost team](https://getmaxim.ai/bifrost/book-a-demo).

## Read next

[![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/)

[![Semantic Caching: The Top 5 AI Gateways in 2026](https://storage.ghost.io/c/84/03/8403f2f6-141c-411a-8f55-a32d4291533e/content/images/size/w720/2026/10/semantic-caching-the-top-5-ai-gateways-in-2026-bifrost-isometric.png) Semantic caching serves a stored LLM response when a new prompt means the same thing as an earlier one. This guide compares Bifrost, Kong AI Gateway, Azure API Management, and Cloudflare AI Gateway on match modes, vector stores, thresholds, TTLs, and cache scoping.](https://www.getmaxim.ai/articles/semantic-caching-the-top-5-ai-gateways-in-2026/)

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    "description": "An AI gateway gives applications one endpoint while it signs, retries, and reroutes calls to Bedrock, Vertex AI, and Azure OpenAI. This guide compares Bifrost, LiteLLM, Kong AI Gateway, Envoy AI Gateway, and Azure API Management on cross-cloud failover.",
    "mainEntityOfPage": "https://www.getmaxim.ai/articles/top-5-ai-gateways-for-automatic-failover-across-bedrock-vertex-ai-and-azure-openai/"
}
```