---
description: Compare the best AI gateways for the Claude Agent SDK on Anthropic API compatibility, Bedrock and Vertex failover, MCP tool governance, and agent budgets.
title: Best AI Gateways for the Claude Agent SDK in 2026
image: https://storage.ghost.io/c/84/03/8403f2f6-141c-411a-8f55-a32d4291533e/content/images/size/w1200/2026/10/best-ai-gateways-for-the-claude-agent-sdk-in-2026-bifrost-isometric.png
---

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**TL;DR**

- The Claude Agent SDK sends its model requests through a gateway when `ANTHROPIC_BASE_URL` is set in the SDK's `env` option, so the gateway must speak the Anthropic Messages API.
- A gateway for SDK agents needs four things: Anthropic-format streaming, multi-provider failover across the Anthropic API, Amazon Bedrock, and Google Vertex AI, per-agent budgets, and governed MCP tool access.
- Bifrost exposes an Anthropic-compatible `/anthropic` endpoint, scopes budgets and MCP tool allow-lists per virtual key, and adds 11 microseconds of overhead per request at 5,000 RPS.
- The Agent SDK's own `total_cost_usd` is a client-side estimate, so server-side cost records at the gateway are the reliable source for agent spend.
- LiteLLM, Kong AI Gateway, Vercel AI Gateway, and Cloudflare AI Gateway are the other options worth evaluating, depending on whether traffic must stay inside your network.

The Claude Agent SDK gives Python and TypeScript applications the same agent loop, built-in tools, and context management that power Claude Code, and in production every turn of that loop is an API call that needs credentials, budgets, failover, and logs. [Bifrost](https://www.getmaxim.ai/bifrost), the [open-source AI gateway built in Go](https://github.com/maximhq/bifrost) by Maxim AI, is the best choice for enterprises running mission-critical AI workloads that require best-in-class performance, scalability, and reliability, and it sits in front of that traffic with an Anthropic-compatible endpoint. This guide compares five AI gateways on the criteria that matter for agent loops: protocol compatibility, provider fallback, MCP governance, per-agent budgets, observability, and streaming.

## What Is the Claude Agent SDK?

The Claude Agent SDK is Anthropic's library for embedding Claude Code's agent loop, built-in tools, permissions, sessions, hooks, subagents, and MCP support inside your own Python or TypeScript application. The SDK runs the Claude Code binary as a child process, so every model request it makes follows the same networking rules as Claude Code itself.

That last detail decides how a gateway connects. Anthropic's [Agent SDK overview](https://code.claude.com/docs/en/agent-sdk/overview) describes the SDK as a library that runs the Claude Code binary, and the SDK's `env` option passes environment variables to that process. Setting `ANTHROPIC_BASE_URL` there points the whole agent loop at a gateway instead of the Anthropic API. Any gateway that already works with Claude Code works with the SDK, which is why the Bifrost [Claude Code integration](https://docs.getbifrost.ai/cli-agents/claude-code) is the reference configuration for SDK agents too.

Three facts from Anthropic's documentation shape the gateway requirements:

- **Protocol**: an Anthropic Messages-format gateway serves `/v1/messages` and, optionally, `/v1/messages/count_tokens`, and must forward the `anthropic-version` and `anthropic-beta` headers unchanged.
- **Streaming**: Claude Code reads streaming responses event by event, stalls if a gateway buffers the response, and treats a stream that ends before the final `message_delta` as a dropped connection.
- **Attribution headers**: requests carry `x-claude-code-session-id`, plus `x-claude-code-agent-id` on requests from subagents, which a gateway can consume to attribute cost to parallel agents.

## Why Route Claude Agent SDK Traffic Through an AI Gateway

An AI gateway gives SDK deployments one place to hold provider credentials, enforce spend limits, fail over between providers, govern MCP tools, and record every model call. Without one, each agent process carries its own API key and its own cost estimate, and nobody can see or stop a runaway loop centrally.

![An agent application calls the Claude Agent SDK, which sends model and MCP traffic through an AI gateway to Anthropic, Bedrock, Vertex AI, and MCP servers](https://articles-images-cdn.t3.tigrisfiles.io/diagrams/best-ai-gateways-for-claude-agent-sdk/claude-agent-sdk-gateway-placement.png)

*Figure 1: One environment variable moves every model call in the agent loop onto the gateway, so provider credentials and policy live in one place.*

The case is stronger for agents than for chat applications, for four reasons:

- **Agent loops multiply calls.** One task can trigger dozens of model requests and tool calls, so per-request governance compounds into per-task cost control.
- **SDK cost figures are estimates.** Anthropic's [cost tracking guide](https://code.claude.com/docs/en/agent-sdk/cost-tracking) states that `total_cost_usd` is computed locally from a bundled price table and should not drive billing or financial decisions.
- **SDK budget caps are per session.** The `max_budget_usd` option stops one session, but it cannot enforce a monthly ceiling across every agent a team runs.
- **Credentials should not ship with agents.** Anthropic does not allow third-party products built on the Agent SDK to offer claude.ai login, so production agents authenticate with API keys, and a gateway keeps those keys server-side.

The same reasoning applies to [Claude Code governance with an AI gateway](https://www.getmaxim.ai/articles/choosing-an-ai-gateway-for-claude-code-complete-guide/), which covers the developer-laptop side of this cluster.

## Key Criteria for Choosing an AI Gateway for the Claude Agent SDK

The right gateway for SDK agents speaks the Anthropic Messages API natively, relays streaming events in order, fails over across Anthropic, Bedrock, and Vertex, scopes budgets per agent, governs MCP tools, and logs each step of the loop. The table below turns those needs into evaluation questions; the [LLM Gateway Buyer's Guide](https://www.getmaxim.ai/bifrost/resources/buyers-guide) covers the wider evaluation.

| Criterion | What to check | Why it matters for agent loops |
| --- | --- | --- |
| Anthropic Messages API compatibility | Serves `/v1/messages`, forwards `anthropic-beta` and `anthropic-version` | The SDK sends Anthropic-format requests with beta values that change each release |
| Streaming fidelity | Relays every SSE event in order, including `ping` and `message_stop` | Buffered or truncated streams stall or abort the agent |
| Multi-provider fallback | Ordered fallback to Bedrock and Vertex, retries per provider | Long agent tasks should survive a provider incident mid-run |
| Per-agent budgets | Budgets and rate limits per credential, with reset windows | Caps spend across sessions, not just inside one |
| MCP tool governance | Per-credential tool allow-lists, central MCP endpoint | Agents should reach only the tools their task requires |
| Agent loop observability | Logs tokens, cost, latency per call, OpenTelemetry export | Reconstructs what an agent did and what it cost |
| Deployment model | Self-hosted, in-VPC, or hosted only | Decides whether prompts and code leave your network |

Figure 2 shows how these criteria combine on a single agent turn: identity and budget checks run before any tokens are spent, and fallback applies only after the primary provider exhausts its retries.

![An agent turn passes virtual key and budget checks, then routes to the Anthropic API, with Bedrock and Vertex AI as ordered fallbacks](https://articles-images-cdn.t3.tigrisfiles.io/diagrams/best-ai-gateways-for-claude-agent-sdk/claude-agent-sdk-gateway-failover.png)

*Figure 2: Policy checks run before any tokens are spent, and fallbacks apply only after the primary provider exhausts its retries.*

## AI Gateway Capabilities Compared at a Glance

Bifrost, LiteLLM, Kong AI Gateway, Vercel AI Gateway, and Cloudflare AI Gateway all accept Anthropic-format traffic, but they differ on deployment model, MCP governance, and how budgets are scoped. The comparison below covers only capabilities published on each vendor's own documentation; "Not published" means the pages reviewed did not state it. For a broader field, see these [agent gateways for routing and governing AI agent traffic](https://www.getmaxim.ai/articles/top-5-agent-gateways-in-2026-routing-securing-and-governing-ai-agent-traffic/).

| Capability | Bifrost | LiteLLM | Kong AI Gateway | Vercel AI Gateway | Cloudflare AI Gateway |
| --- | --- | --- | --- | --- | --- |
| Anthropic Messages endpoint | Yes (`/anthropic`) | Yes (proxy) | Yes (`llm_format: anthropic`) | Yes | Yes (Anthropic provider path) |
| Documented Agent SDK or Claude Code setup | Claude Code guide | Agent SDK guide | Claude Code how-to | Agent SDK and Claude Code guide | Not published |
| Deployment | Self-hosted, in-VPC, on-prem | Self-hosted | Self-hosted data plane with Konnect | Hosted | Hosted |
| Provider fallback | Retries plus ordered fallback chain | Fallbacks | Not published | Model fallbacks | Retry and model fallback |
| Per-credential budgets | Virtual keys, team and customer hierarchy | Budgets and rate limits | Not published | Budgets | Spend limits (beta) |
| MCP gateway with per-key tool filtering | Yes | MCP gateway with key and team access | MCP support in AI Gateway | Not published | Not published |
| Observability | Built-in logs, OpenTelemetry, Prometheus | Logging and spend tracking | File log plugin in how-to | Vercel Observability | Analytics and logging |

## 1. Bifrost

**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](https://www.getmaxim.ai/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.

[The Bifrost AI gateway](https://www.getmaxim.ai/bifrost) is open source and exposes an Anthropic-compatible endpoint at `/anthropic`, so an SDK agent needs only a base URL and a virtual key to route through it. Bifrost governs both paths an agent uses, model inference and MCP tool calls, under one virtual key, and adds [11 microseconds of overhead per request at 5,000 RPS](https://www.getmaxim.ai/bifrost/resources/benchmarks) in sustained benchmarks.

![A Claude Agent SDK agent sends inference to the Bifrost Anthropic endpoint and tool calls to the MCP endpoint, both governed by one virtual key and logged](https://articles-images-cdn.t3.tigrisfiles.io/diagrams/best-ai-gateways-for-claude-agent-sdk/claude-agent-sdk-bifrost-architecture.png)

*Figure 3: A single virtual key scopes the model budget and the MCP tool allow-list for one agent, and both paths land in the same logs.*

### Connecting the Claude Agent SDK to Bifrost

Bifrost acts as a [drop-in replacement for the Anthropic API](https://docs.getbifrost.ai/integrations/anthropic-sdk/overview), and the recommended credential is a Bifrost virtual key passed as `ANTHROPIC_AUTH_TOKEN`, which the CLI sends as a bearer token. With that set, the agent needs no Anthropic credentials at all:

```python
from claude_agent_sdk import query, ClaudeAgentOptions

options = ClaudeAgentOptions(
    model="claude-sonnet-4-6",
    env={
        "ANTHROPIC_BASE_URL": "http://localhost:8080/anthropic",
        "ANTHROPIC_AUTH_TOKEN": "sk-bf-your-virtual-key",
    },
)

async for message in query(prompt="Triage the failing tests", options=options):
    print(message)
```

Model names can carry a provider prefix, such as `bedrock/global.anthropic.claude-sonnet-4-6` or `vertex/claude-sonnet-4-6`, to pin an agent to Claude on a specific cloud. Routing rules can also rewrite an alias like `sonnet-model` to any configured target at request time through [model aliasing](https://docs.getbifrost.ai/providers/aliasing-models). The [Claude Code on Bedrock runbook](https://docs.getbifrost.ai/runbooks/claude-code-bedrock) and the [AWS Bedrock resource page](https://www.getmaxim.ai/bifrost/resources/aws-bedrock) walk through deployment-name mappings for Bedrock-hosted Claude models.

### Failover across Anthropic, Bedrock, and Vertex

Bifrost separates retries from fallbacks. [Retries and fallbacks](https://docs.getbifrost.ai/features/fallbacks) work in two layers: retries rotate API keys on `429` or auth failures and back off exponentially on `5xx` errors, then the fallback chain moves to the next provider, and each fallback gets its own full retry budget. On a virtual key with weighted provider configs, the remaining providers become fallbacks sorted by weight.

Session affinity matters for agents specifically. Bifrost adopts the `x-claude-code-session-id` header the CLI already sends, so a session stays on the provider and key that served it and keeps hitting the same prompt cache, and Claude Code subagents share their parent's session ID. [Session affinity](https://docs.getbifrost.ai/providers/session-affinity) reorders the routing chain without ever reviving a provider that budget, rate limits, or health checks excluded.

### Per-agent budgets with virtual keys

[Virtual keys](https://docs.getbifrost.ai/features/governance/virtual-keys) are the primary governance entity in Bifrost: each carries allowed providers and models, budgets with reset durations from one minute to one year, and token and request rate limits. Issuing one virtual key per agent, or per tenant of a multi-tenant agent product, turns the SDK's session-level `max_budget_usd` into an organization-level ceiling. Budgets roll up through a customer, team, and virtual key hierarchy described in [budgets and limits](https://docs.getbifrost.ai/features/governance/budget-and-limits), and the [governance resource page](https://www.getmaxim.ai/bifrost/resources/governance) covers how teams structure it.

### MCP tool governance

Bifrost works as an [MCP gateway](https://www.getmaxim.ai/bifrost/resources/mcp-gateway) that aggregates connected MCP servers behind a single `/mcp` endpoint, which an Agent SDK agent can register as an HTTP MCP server with its virtual key in the `Authorization` header. Key governance properties:

- **Deny by default**: a virtual key with no MCP configuration exposes no MCP tools, except from clients marked Allow by Default, through [per-key MCP tool filtering](https://docs.getbifrost.ai/features/governance/mcp-tools).
- **Curated endpoints**: [Virtual MCPs](https://docs.getbifrost.ai/mcp/virtual-mcps) bundle selected tools from several servers at `/mcp/<slug>`, reachable only through attached virtual keys.
- **No automatic execution by default**: tool calls are suggestions until explicitly executed, unless Agent Mode is configured for specific tools.
- **Lower token use**: [Code Mode](https://docs.getbifrost.ai/mcp/code-mode) exposes tools as a small set of meta tools the model uses to discover and call underlying tools.

When an agent uses both the inference endpoint and `/mcp`, disabling auto tool injection keeps the two paths separate. The [MCP gateway cost governance write-up](https://www.getmaxim.ai/bifrost/blog/bifrost-mcp-gateway-access-control-cost-governance-and-92-lower-token-costs-at-scale) covers the token economics in more depth.

### Observability of agent loops

Bifrost's [built-in observability](https://docs.getbifrost.ai/features/observability/default) captures inputs, outputs, tokens, cost, and latency for every request asynchronously, with no added request latency. Configured logging headers copy request headers into log metadata, so capturing `x-claude-code-agent-id` attributes cost to individual subagents. The [OpenTelemetry plugin](https://docs.getbifrost.ai/features/observability/otel) exports traces using GenAI semantic conventions to collectors such as Grafana, Datadog, or Honeycomb, alongside the Agent SDK's own OTel export.

### Streaming and enterprise deployment

Bifrost relays Anthropic streaming in the native event order (`message_start` through `message_stop`, including `input_json_delta` for tool arguments and `thinking_delta` for reasoning) and manages Anthropic beta headers per provider. For production, enterprise teams add [clustering for high availability](https://docs.getbifrost.ai/enterprise/clustering), [in-VPC deployments](https://docs.getbifrost.ai/enterprise/invpc-deployments), and guardrails through [Bifrost Enterprise](https://www.getmaxim.ai/bifrost/enterprise). Bifrost reaches 25+ providers and 10,000+ models through one OpenAI-compatible API.

## 2. LiteLLM

**Best for:** Python-centric platform teams that want an open-source proxy with a documented Agent SDK setup and a large provider catalog.

LiteLLM publishes an Agent SDK guide that points `ANTHROPIC_BASE_URL` at the LiteLLM proxy and passes a LiteLLM key as `ANTHROPIC_API_KEY`, with models defined in a `config.yaml` file. The guide lists multi-provider routing, cost tracking, rate limiting and budgets, load balancing, and fallbacks as the reasons to pair the two.

LiteLLM also runs an MCP gateway with permission management that grants MCP server access to keys and teams. Enterprise features listed on its pages include SSO, audit logs, multi-team management, and guardrails. Teams comparing performance and governance depth can review the [LiteLLM alternative overview](https://www.getmaxim.ai/bifrost/resources/litellm-alternative).

## 3. Kong AI Gateway

**Best for:** Organizations already standardized on Kong Gateway that want agent traffic managed with the same plugins and control plane as their APIs.

Kong documents routing Claude Code traffic through its AI Proxy plugin with `llm_format: anthropic`, which tells the gateway to validate requests and responses in Claude's native API format. The how-to targets Kong Gateway Enterprise with a Konnect control plane, version 3.13 or later, and uses the file log plugin to inspect traffic. Because the Agent SDK runs the same CLI, the same route serves SDK agents.

Kong positions its AI Gateway across LLM traffic, MCP, and agent-to-agent coordination. Teams weighing Kong's plugin model against a purpose-built AI gateway can read [Kong AI Gateway alternatives](https://www.getmaxim.ai/articles/best-kong-ai-gateway-alternatives-in-2026/).

## 4. Vercel AI Gateway

**Best for:** Teams already deploying on Vercel that want a hosted gateway with no infrastructure to operate.

Vercel AI Gateway serves Anthropic Messages API endpoints, including `/v1/messages` with streaming, tool calling, and extended thinking, plus token counting and batches. Vercel documents a dedicated Claude Code compatibility endpoint and an Agent SDK example that sets `ANTHROPIC_BASE_URL` and `ANTHROPIC_AUTH_TOKEN` in the SDK's `env` option. Traffic and spend appear in the Vercel dashboard, and its docs cover budgets, model fallbacks, and bring-your-own-key.

Vercel [AI Gateway](https://www.getmaxim.ai/articles/top-5-llm-gateways-in-2026-a-production-ready-comparison/) is a hosted service, so prompts, tool results, and code context in agent requests transit Vercel's infrastructure. Teams with data residency or in-VPC requirements can compare [Vercel AI Gateway alternatives](https://www.getmaxim.ai/articles/best-vercel-ai-gateway-alternatives-in-2026/).

## 5. Cloudflare AI Gateway

**Best for:** Teams on Cloudflare that want edge caching, analytics, and spend limits in front of Anthropic traffic.

Cloudflare AI Gateway exposes an Anthropic provider path that the Anthropic SDK can use as its base URL, with requests posted to `/v1/messages` under the gateway's account and gateway IDs. Its feature set includes caching, rate limiting, request retry and model fallback, dynamic routing, guardrails, analytics, and logging. Spend limits, currently in beta, can be scoped by model, provider, or custom metadata such as user or team.

The pages reviewed did not include an Agent SDK or Claude Code guide or an MCP gateway, so teams should test streaming and beta-header forwarding against their agent workloads. [Cloudflare AI Gateway alternatives](https://www.getmaxim.ai/articles/best-cloudflare-ai-gateway-alternative-in-2026/) covers self-hosted options for the same use case.

## How to Choose a Gateway for Claude Agent SDK Agents

Choosing a gateway for SDK agents starts with the network boundary: if agent prompts, tool results, and source code must stay inside your infrastructure, only self-hosted gateways qualify. Existing platform commitments, MCP governance needs, and how strictly per-agent spend must be enforced then narrow the field.

![A decision flow checks whether agent traffic must stay in your network and whether Kong is already in use, then points to a gateway type](https://articles-images-cdn.t3.tigrisfiles.io/diagrams/best-ai-gateways-for-claude-agent-sdk/claude-agent-sdk-gateway-selection.png)

*Figure 4: Network boundary is the first filter; existing platform investments decide the rest.*

Three challenges come up repeatedly once agents reach production:

- **Header drift**: the CLI's `anthropic-beta` values change with releases, and a gateway that allowlists individual values breaks new features. Bifrost ships a per-provider beta-header matrix and lets operators add `anthropic-beta` to the allowed client headers.
- **Cost attribution across subagents**: parallel subagents share one session, so per-agent cost needs the agent ID header captured at the gateway, as covered in [monitoring Claude Code token usage](https://www.getmaxim.ai/articles/best-ai-gateway-to-monitor-claude-code-token-usage/).
- **Tool sprawl**: agents that load every MCP server inflate context and widen the blast radius; per-key allow-lists and [MCP gateways for Claude Code](https://www.getmaxim.ai/articles/best-mcp-gateways-for-claude-code-in-2026/) address both.

For teams moving from Claude Code pilots to SDK-based agents, the [guide to running Claude Code on Bedrock, Vertex, or your own models](https://www.getmaxim.ai/articles/running-claude-code-on-bedrock-vertex-or-your-own-models-with-an-enterprise-ai-gateway/) and the [Claude Code resource page](https://www.getmaxim.ai/bifrost/resources/claude-code) describe the same configuration patterns from the developer side.

## Frequently Asked Questions

### What is the Claude Agent SDK?

The Claude Agent SDK is Anthropic's Python and TypeScript library for building agents with the same agent loop, built-in tools, and context management that power Claude Code. It runs the Claude Code binary as a child process and adds hooks, subagents, MCP connections, permissions, and sessions. It is the successor to the earlier Claude Code SDK packages.

### What's the difference between Claude Code and Claude Agent SDK?

Claude Code is the terminal interface built for interactive development, while the Claude Agent SDK embeds the same agent in your own application, running in a process you operate. Both use the same binary and environment variables, so a gateway configured for Claude Code, including the Bifrost virtual key setup, works for SDK agents through the SDK's `env` option.

### Is there a Python SDK for Claude agent?

Yes. The SDK ships as the `claude-agent-sdk` package for Python and `@anthropic-ai/claude-agent-sdk` for TypeScript. Both bundle a native Claude Code binary on supported platforms. In Python, the `env` option merges over the inherited environment; in TypeScript it replaces it, so include `process.env` when setting `ANTHROPIC_BASE_URL` for a gateway.

### Is the Claude Agent SDK free?

The SDK packages are published openly on npm and PyPI, and their use is governed by Anthropic's Commercial Terms of Service. Model usage is billed per token to whichever account supplies the credential, such as an Anthropic Console account or a Bedrock or Vertex account. Routing through a gateway with a gateway credential bills the organization's provider account behind the gateway.

### How do I point the Claude Agent SDK at an AI gateway?

Set `ANTHROPIC_BASE_URL` to the gateway's Anthropic-compatible endpoint and a gateway credential in the SDK's `env` option. For Bifrost, the base URL is the `/anthropic` endpoint and the credential is a virtual key passed as `ANTHROPIC_AUTH_TOKEN`. The Agent SDK passes these to the Claude Code process, so every request in the agent loop routes through the gateway.

### Can the Claude Agent SDK use Amazon Bedrock or Google Vertex AI?

Yes. The SDK supports Bedrock and Vertex directly through `CLAUDE_CODE_USE_BEDROCK` and `CLAUDE_CODE_USE_VERTEX`, but that ties each agent to one provider. Behind an Anthropic-format gateway such as Bifrost, the agent keeps one configuration while the gateway pins models with prefixes like `bedrock/` or `vertex/` and fails over between [Bedrock](https://docs.getbifrost.ai/providers/supported-providers/bedrock) and other providers.

## Try Bifrost with the Claude Agent SDK

Running the Claude Agent SDK in production means treating every agent turn as governed, observable infrastructure traffic. Bifrost gives these agents an Anthropic-compatible endpoint, ordered failover across Anthropic, Bedrock, and Vertex, per-agent budgets with [virtual keys and hierarchical spend controls](https://www.getmaxim.ai/articles/llm-budget-management-virtual-keys-and-hierarchical-spend-controls/), and governed MCP tool access with [auditable tool calls](https://www.getmaxim.ai/articles/mcp-gateway-observability-audit-every-ai-tool-call/).

For the broader rollout across coding agents, see the [complete guide to choosing a Claude Code gateway](https://www.getmaxim.ai/articles/choosing-an-ai-gateway-for-claude-code-complete-guide/). To see Bifrost handle your agent workloads, [book a demo](https://getmaxim.ai/bifrost/book-a-demo) with the Bifrost team.

## 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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