Top 5 LLM Gateways for Securing Your AI Apps

TL;DR

• LLM gateways have become essential infrastructure for production AI applications, because every prompt and response passes through the one layer where LLM security controls can be enforced.
• This guide compares five leading solutions: Bifrost, LiteLLM, Azure API Management, Kong AI Gateway, and Cloudflare AI Gateway.
• Bifrost is an open-source enterprise AI gateway that adds 11 µs of overhead per request at 5,000 RPS, with advanced governance, security, and cost controls.
• Bifrost Enterprise runs three Bifrost-managed guardrail providers (Secrets Detection backed by Gitleaks, Custom Regex, and Prompt Guardrails) and integrates 11 external providers, including AWS Bedrock Guardrails and Azure AI Content Safety.
• Each platform offers distinct capabilities for unified LLM access, cost control, and security; they differ most in which LLM guardrails run natively and how PII is redacted.

LLM security is the set of controls that govern what enters a model in a prompt and what leaves it in a response, and an LLM gateway is the one layer every request passes through on the way. Bifrost is the best choice for enterprises running mission-critical AI workloads that require best-in-class performance, scalability, and reliability, and it applies LLM guardrails for prompt injection, PII, secrets, and content safety at that layer. This guide ranks five LLM gateways by the runtime security controls they apply to live AI traffic.

What is an LLM Gateway?

An LLM gateway sits between your AI applications and multiple LLM providers as a single control layer. Instead of managing separate integrations for OpenAI, Anthropic, AWS Bedrock, and others, a gateway provides a unified interface that normalizes API formats, handles authentication, implements failover logic, and provides observability. Without a gateway, teams face provider lock-in, manual failover management, and limited visibility into AI spending.

The same position makes the gateway a security boundary. Without one, each application carries its own PII filters and key handling; with one, a single set of rules inspects every prompt and response, which is why teams apply LLM guardrails at the gateway layer rather than in application code.

Figure 1: A gateway replaces per-application security code with one policy layer that sees every prompt and response.

LLM Security Risks a Gateway Can Control

A gateway controls the LLM security risks that show up in live traffic: prompt injection, sensitive data in prompts or responses, leaked credentials, harmful output, over-permissioned tool calls, and runaway usage. Risks rooted in training data or model weights, such as data poisoning, sit upstream of the gateway and need controls in the model pipeline instead.

The OWASP Top 10 for LLM Applications (2025) is the common reference for these risks. The table maps the runtime risks to the gateway control and Bifrost feature that address each.

How LLM Gateways Enforce LLM Guardrails

Securing an AI application means controlling what leaves in a prompt and what comes back in a response, not just authenticating the caller. The OWASP Top 10 for LLM Applications ranks prompt injection and sensitive-information disclosure as the top risks, and a gateway is the natural place to enforce controls against both, because every request passes through it. The controls that matter are guardrails on prompt and response content, PII redaction, secrets detection, audit logging, and data loss prevention.

Bifrost Enterprise enforces these at the gateway with guardrail rules and profiles. A rule is a CEL expression that decides which requests to check and whether to scan the input, the output, or both; a profile is the provider that performs the check. Rules target either LLM calls or MCP tool executions, so the same policy can inspect tool arguments before a tool runs and tool results before they return.

Figure 2: Input guardrails run before the provider sees the prompt, and output guardrails run before the caller sees the response.

Each profile applies one of three actions when it finds something: detect_only records the finding, block returns a guardrail intervention, and redact rewrites the detected text. Guardrail redaction then decides where the rewrite applies:

Streaming responses need separate handling, because a gateway cannot block text it has already sent. In Bifrost, detect-only and logs-only rules observe a stream without delaying it, runtime redaction checks buffered text segments before releasing them, and any matched rule that can block holds the complete stream until the guardrail decision is made. Bifrost logs store the redacted form of detected content, and signed audit logs record administrative changes to rules and profiles.

1. Bifrost - High-Performance Enterprise AI Gateway

Bifrost is an open-source AI gateway that combines 11 µs of routing overhead with native and external LLM guardrails: three Bifrost-managed providers, 11 external providers, three redaction modes, and guardrail rules for both LLM calls and MCP tool executions. Guardrails, redaction, and audit logs are part of Bifrost Enterprise.

Bifrost AI Gateway

Platform Overview

Bifrost is a high-performance, open-source LLM gateway built by Maxim AI specifically for production-grade AI systems. Written in Go, Bifrost adds 11 µs of overhead per request at 5,000 RPS in sustained benchmarks. The gateway emphasizes zero-configuration deployment, with a gateway running in about 30 seconds and no configuration required.

Features

Core Capabilities:

• Unified Interface - Single OpenAI-compatible API across 25+ providers and 10,000+ models, including OpenAI, Anthropic, AWS Bedrock, Google Vertex, Azure, Cerebras, Cohere, Mistral, Ollama, and Groq
• Automatic Failover - Automatic failover between providers and models when a request fails
• Load Balancing - Weighted request distribution across multiple API keys and providers
• Semantic Caching - Response caching based on semantic similarity to reduce costs and latency

Advanced Features:

• Model Context Protocol (MCP) - Enable AI models to use external tools like filesystem, web search, and databases
• Multimodal Support - Text, images, audio, and streaming behind a unified interface
• Governance - Usage tracking, rate limiting, and hierarchical budget management
• Custom Plugins - Extensible middleware for analytics and monitoring

LLM Guardrails:

• Secrets Detection - Runs inside Bifrost with the 222 default Gitleaks v8.30.1 rules for cloud credentials, LLM provider keys, private keys, and tokens
• Custom Regex - In-process patterns, with a PII Detection template for emails, US phone and Social Security numbers, credit-card-like numbers, and IPv4 addresses
• Prompt Guardrails - An LLM-as-judge provider that blocks requests or responses violating a natural-language policy
• External providers - 11 integrations, including Microsoft Presidio, Azure AI Language PII, AWS Bedrock Guardrails, Azure Content Safety, Google Model Armor, and CrowdStrike AIDR

Enterprise Security:

• SSO Integration - OIDC login with Okta, Microsoft Entra, Google Workspace, Keycloak, and Zitadel
• Vault Support - Secure API key management with HashiCorp Vault, AWS Secrets Manager, or GCP Secret Manager
• Observability - Native Prometheus metrics and distributed tracing

Figure 3: Secrets Detection and Custom Regex need no external call, so teams can add credential and PII checks without another vendor account.

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.

2. LiteLLM - Open-Source Multi-Provider Gateway

LiteLLM is an open-source, Python-based gateway that secures LLM traffic mainly through third-party guardrail integrations configured in YAML. Teams choose the detection vendor for prompt injection and PII masking. Teams weighing a move off it can compare LiteLLM alternatives for production gateways.

LiteLLM

Platform Overview

LiteLLM is an open-source gateway supporting multiple LLM providers through a unified OpenAI-compatible interface. Available as both a Python SDK and proxy server, it offers extensive flexibility for different deployment scenarios.

Features

• Multi-Provider Support - OpenAI-compatible access to many LLM providers from one interface
• Guardrails - pre_call, during_call, and post_call modes, with integrations such as Presidio for PII masking and Lakera, Aporia, and AWS Bedrock Guardrails for content checks
• Agent Gateway (A2A) - Invoke and manage AI agents alongside LLM and MCP traffic
• MCP Support - Use Model Context Protocol servers directly via chat completions endpoint
• Cost Tracking - Budgets and rate limits on keys to monitor and cap spending
• Observability Integrations - Connects with Langfuse and other logging platforms

Best For

LiteLLM suits teams comfortable with YAML configuration who need maximum provider coverage and extensive third-party integrations. Its open-source nature makes it attractive for teams requiring full customization and transparency.

3. Azure API Management - AI Gateway for Azure Workloads

Azure API Management adds AI gateway policies to Microsoft's managed API management service, so LLM, MCP, and A2A traffic is governed alongside existing APIs. Its main LLM security control is a content safety policy that sends prompts and completions to Azure AI Content Safety and blocks flagged content with a 403 response. For teams on AWS instead, see how AWS Bedrock Guardrails work inside Bifrost.

Platform Overview

Azure API Management is Microsoft's managed service for publishing and governing APIs, and its AI gateway capabilities apply the same policy model to model endpoints in Microsoft Foundry and other providers.

Features

• Content Safety Policy - The llm-content-safety policy checks requests or responses against Azure AI Content Safety harm categories and custom blocklists
• Prompt Shields - An optional shield-prompt setting checks content for user prompt attacks
• Token Limits - The llm-token-limit policy enforces tokens-per-minute limits or token quotas per subscription key, IP address, or custom key
• Semantic Caching - Reuses completions for prompts with similar vector embeddings
• Load Balancing and Circuit Breaking - Round-robin, weighted, priority-based, and session-aware distribution across AI backends
• MCP and A2A - Expose REST APIs as MCP servers, govern existing MCP servers, and import A2A agent APIs

Best For

Azure API Management fits organizations already running their APIs and model deployments on Azure that want LLM security policies in the same control plane. PII redaction and secrets detection are not part of the published content safety policy, so teams with those requirements pair it with another control.

4. Kong AI Gateway - Enterprise API Management

Kong AI Gateway secures LLM traffic with a set of AI plugins on Kong's API gateway: prompt guards for jailbreak and injection attempts, a sanitizer that redacts PII before it reaches the provider, a semantic response guard, and connectors to cloud trust-and-safety services. A broader view of these controls is in this guide to enterprise AI guardrails for PII, injection, and toxicity.

Kong AI Gateway

Platform Overview

Kong AI Gateway extends Kong's API management platform to AI workloads. It provides governance for LLM, MCP, and agent traffic through AI entities and policies attached to a single endpoint.

Features

• Universal LLM API - Route across major LLM providers, including Azure AI and Amazon Bedrock, through one consistent API
• MCP & A2A Support - Govern Model Context Protocol servers and Agent-to-Agent traffic from the same gateway
• Prompt Guards - AI Prompt Guard and AI Semantic Prompt Guard catch jailbreak and prompt-injection attempts, including ones phrased in natural language
• PII Sanitization - AI Sanitizer redacts PII before it reaches the upstream LLM provider
• Advanced Guardrails - AWS Guardrails, Azure Content Safety, and GCP Model Armor plugins, plus AI Semantic Response Guard for responses
• Semantic Caching - Serve repeated or near-duplicate prompts from cache
• Prompt Compression - Shrink prompts before they reach the upstream provider to reduce token costs

Best For

Kong targets enterprise organizations requiring comprehensive governance and integration with existing API management infrastructure. Teams already using Kong Gateway can manage AI traffic in the same control plane.

5. Cloudflare AI Gateway - Global Infrastructure Platform

Cloudflare AI Gateway is a managed gateway on Cloudflare's network that adds two LLM security features to caching, rate limiting, and routing: Guardrails, which flag or block harmful content in prompts and responses, and Data Loss Prevention, which scans both directions for sensitive data. The two approaches compare closely with the top AI guardrails platforms for prompt injection.

Cloudflare AI Gateway

Platform Overview

Cloudflare AI Gateway uses Cloudflare's global network to provide AI application control with analytics, logging, and unified billing.

Features

• Unified Billing - Pay for and authenticate inference requests through Cloudflare billing
• Guardrails - Evaluate prompts and responses against safety categories and choose to flag or block
• Caching & Rate Limiting - Reduce costs and control usage at scale
• Dynamic Routing - Route requests based on conditions, quotas, and fallbacks
• Data Loss Prevention - Integrated DLP to scan prompts and responses for sensitive data; with response scanning on, streamed responses are buffered in full before release
• Free Tier - Available on all Cloudflare plans

Best For

Cloudflare suits teams already using Cloudflare services who want direct integration with their existing infrastructure. The unified billing simplifies multi-provider cost management, and request-only DLP scanning avoids buffering streamed responses on latency-sensitive routes.

LLM Security Tools Compared

The five LLM security tools split along two lines: where the gateway runs, and whether PII, secrets, and injection checks are native or delegated to another service. For the enforcement details behind each row, see LLM gateway security for prompt injection, PII, and audit.

Choosing the Best LLM Gateway for LLM Security

The best LLM gateway for LLM security is the one that enforces your required checks inside the boundary your data is allowed to cross. Start from the OWASP threats above, then filter by deployment model and by whether streaming responses must stay incremental. Dedicated LLM security tools for enterprise AI applications can complement any of the five.

The right gateway depends on your specific requirements, but all five platforms solve the fundamental challenge of unified LLM access while offering distinct advantages for production AI applications. Bifrost unifies LLM, MCP, and agent gateway capabilities in a single platform built for enterprises running mission-critical AI workloads, delivering ultra-low latency routing, governance, and security across every model and environment.

• Prompts must stay inside your network: choose a self-hosted gateway such as Bifrost with in-VPC deployment, or LiteLLM.
• Credentials leak into prompts: use a gateway with native secrets detection, so API keys are blocked or redacted before a provider or a log stores them.
• Your platform is already Azure, Kong, or Cloudflare: the matching gateway keeps AI policies in the control plane you already operate.
• Agents call tools: apply guardrails to MCP tool arguments and results, not only to model prompts, as covered in this guide on how to implement LLM guardrails in the Bifrost AI gateway.

Get started with Bifrost.

Frequently Asked Questions

What is an LLM gateway?

An LLM gateway is a middleware layer between your applications and one or more model providers. It exposes a single API, normalizes provider formats, handles authentication and failover, and enforces governance and security controls on every request. For securing AI apps specifically, the gateway is where guardrails, PII redaction, and audit logging are applied consistently across all providers.

How does an LLM gateway protect against prompt injection?

The gateway inspects prompt content before it reaches a model and response content before it returns to the caller, applying policy rules that block or flag injection attempts. Because every request passes through one layer, the defense is consistent across providers. Bifrost applies this through Prompt Guardrails and external providers such as AWS Bedrock Guardrails, Google Model Armor, and Check Point's AI Agent Security.

Can an LLM gateway redact PII before it reaches a provider?

Yes. A gateway with PII controls scans prompts for sensitive data and redacts or blocks it before the request leaves your environment, and scans responses on the way back. Enforcing this at the gateway means one policy covers every application and provider, rather than reimplementing redaction in each service. In Bifrost, the runtime redaction mode rewrites a detected email address to [EMAIL] before the provider receives the prompt.

What security controls should a secure LLM gateway have?

At minimum: guardrails on prompt and response content, PII redaction, secrets detection, prompt-injection defense, signed audit logs, and secure secrets management for provider keys. RBAC, SSO, and in-VPC or air-gapped deployment matter for regulated workloads. The controls should be enforced at the gateway so they apply uniformly, not per application.

What are LLM vulnerabilities?

LLM vulnerabilities are weaknesses that let an attacker or a careless input change what a model does or reveals. The OWASP Top 10 for LLM Applications lists prompt injection, sensitive information disclosure, supply chain risks, data and model poisoning, improper output handling, excessive agency, system prompt leakage, vector and embedding weaknesses, misinformation, and unbounded consumption. A gateway addresses the runtime ones.

What are examples of AI guardrails?

Common AI guardrails include prompt-injection detection, PII detection and redaction, secrets detection for leaked API keys, content safety filters for hate or violence, topic restrictions enforced by an LLM judge, and output checks on tool results. Bifrost runs secrets detection and regex checks in process and routes deeper checks to providers such as Presidio or Azure Content Safety. Running them in one place is why guardrails belong in the gateway rather than the app.

What is the most secure way to deploy an LLM gateway?

For regulated or data-sensitive workloads, self-host the gateway inside your own VPC or an air-gapped environment so no prompt data leaves your boundary. Bifrost is open source under Apache 2.0 and supports in-VPC, on-premises, and air-gapped deployment, which keeps data, keys, and enforcement decisions entirely within your infrastructure.

Want to see how Bifrost can enforce LLM security across your AI infrastructure? Schedule a demo to learn more about Bifrost.