Top 5 AI Gateways to Secure Your AI Apps in 2026

Compare the top 5 AI gateways to secure your AI apps: prompt injection defense, PII redaction, governance, audit logs, and policy enforcement at the gateway layer.

AI gateways have become the security control plane for production LLM applications. As models gain access to internal tools, customer data, and external APIs, the threats outlined in the OWASP Top 10 for LLM Applications, prompt injection, sensitive information disclosure, improper output handling, and excessive agency, can no longer be mitigated inside individual application code. Teams need centralized enforcement where every request and response is validated against policy before it reaches a model or a user. This guide compares the top 5 AI gateways to secure your AI apps on runtime guardrails, governance, audit evidence, and credential isolation. Bifrost, the open-source AI gateway by Maxim AI, anchors this list as the highest-performance option that consolidates security, routing, and MCP governance in a single Go binary.

Why AI Gateways Are a Security Layer, Not Just a Proxy

LLM security risks differ fundamentally from traditional web app risks. Prompts are executable instructions, outputs can leak training data or PII, and agentic workflows give models the ability to call tools that change real-world state. Prompt injection holds the top position in the OWASP LLM Top 10 because it exploits the fundamental design of LLMs and has no foolproof prevention.

Securing AI apps at the gateway layer gives platform and security teams four properties application-level controls cannot:

• Centralized enforcement: One policy, one set of guardrails, applied to every request across every team and every model.
• Defense in depth: Multiple guardrail providers layered on inputs and outputs before requests reach the LLM and before responses reach end users.
• Audit evidence: Immutable logs of every blocked, redacted, or allowed request, mapped to SOC 2, HIPAA, and EU AI Act requirements.
• Credential isolation: Application code never sees provider API keys, and consumer-level virtual keys can be revoked without redeploys.

The five AI gateways below are ranked by how completely they deliver on those properties.

Key Criteria for Evaluating AI Gateways for Security

When the gateway is the security boundary, these are the criteria that matter:

• Runtime guardrails: Native, inline content safety on inputs and outputs, with multi-provider integration (AWS Bedrock Guardrails, Azure AI Content Safety, specialized vendors).
• Prompt injection and jailbreak defense: Detection of direct, indirect, and multimodal injection attacks before they reach the model.
• PII detection and redaction: Automatic identification of sensitive identifiers in prompts and responses, with configurable block or redact actions.
• Governance primitives: Virtual keys, per-team budgets, rate limits, and role-based access control to constrain blast radius.
• MCP and tool security: For agentic workloads, the gateway must govern which tools each consumer can call, with OAuth and per-key tool filtering.
• Audit logs and compliance evidence: Immutable, exportable trails sufficient for SOC 2, HIPAA, ISO 27001, and EU AI Act conformity.
• Secret management and deployment flexibility: Native vault integration and in-VPC or on-premises options for regulated workloads.

The gateways below are ranked against these criteria, starting with the most complete option.

1. Bifrost: Open-Source AI Gateway with Enterprise Guardrails

Bifrost is a high-performance, open-source AI gateway built in Go that unifies access to 20+ LLM providers through a single OpenAI-compatible API. It treats security as a first-class gateway capability and adds only 11 microseconds of overhead per request at 5,000 RPS in sustained benchmarks. Performance methodology is documented on the Bifrost benchmarks page.

Bifrost's security architecture has four pillars: inline guardrails, governance via virtual keys, MCP gateway controls for agentic workflows, and immutable audit logs.

Inline guardrails on every request and response. Bifrost integrates natively with AWS Bedrock Guardrails, Azure AI Content Safety, Patronus AI, and GraySwan as guardrail backends, with the ability to layer multiple providers for defense-in-depth. Policies are defined once in the gateway and enforced on both inputs (prompt injection, PII entering the provider, prompt-level policy violations) and outputs (hallucinations, PII leakage, toxic generations, indirect injection fallout). A CEL-based rule engine lets teams define custom policies with conditions on message role, model type, content length, and keyword presence. The full set of capabilities is on the Bifrost guardrails resource page.

Governance through virtual keys. Each consumer (an application, a team, a customer) receives a virtual key that scopes access permissions, budgets, rate limits, and which MCP tools that consumer can invoke. Compromised virtual keys can be revoked instantly without redeploying applications, and per-key cost controls prevent runaway spend. The Bifrost governance resource page covers the complete model.

MCP gateway with per-key tool filtering. For agentic workloads, the MCP gateway governs which Model Context Protocol tools each virtual key can invoke, supports OAuth 2.0 with PKCE and automatic token refresh, and centralizes tool registration so security teams have one inventory of every tool an AI agent can reach. Code Mode reduces token costs and the attack surface by having models orchestrate multiple tools in a Starlark sandbox instead of round-tripping each call. The Bifrost MCP gateway access control and governance post details how this maps to enterprise compliance.

Audit logs, vault support, and in-VPC deployment. Every guardrail invocation, virtual key action, and tool call lands in immutable audit logs suitable for SOC 2, HIPAA, GDPR, and ISO 27001 evidence. Provider keys flow through HashiCorp Vault, AWS Secrets Manager, Google Secret Manager, or Azure Key Vault. Regulated organizations can deploy Bifrost in-VPC or on-premises so prompts, responses, and audit trails never leave customer infrastructure.

Best for: Teams that need defense-in-depth security, gateway-level guardrails across 20+ providers, MCP governance for agents, and a single audit-ready control plane. Bifrost is the only option on this list that combines all of these without sacrificing latency.

2. LiteLLM: Open-Source Proxy with Basic Security Controls

LiteLLM is a widely adopted open-source LLM proxy that provides a unified, OpenAI-compatible interface across many providers. Its security posture covers the basics: API key management, per-team budgets, rate limiting, virtual keys, basic spend tracking, and request logging. The Python ecosystem makes it easy to plug in custom validators or call external safety APIs from request hooks.

The trade-offs are visible at production scale. The proxy is Python-based, which introduces latency overhead under sustained load. Native runtime guardrail integrations are less comprehensive, with most safety logic implemented as hooks rather than first-class gateway primitives. Audit logging is basic and typically requires external SIEM integration to satisfy formal compliance evidence. Teams comparing the two can review the Bifrost LiteLLM alternatives page for a full feature breakdown.

Best for: Early-stage teams prototyping LLM applications in Python who need provider abstraction and basic governance, without strict performance or audit requirements yet.

3. Kong AI Gateway: API Management Platform Extended for LLMs

Kong AI Gateway extends Kong's established API management platform with LLM-specific routing, semantic security, and caching plugins. For enterprises that have already standardized on Kong for traditional API traffic, it brings familiar policy primitives to AI workloads, including authentication, authorization, plugin-based extensibility, and rate limiting.

Security capabilities include token analytics, request and response transformation, and prompt-level policy enforcement through plugins. Teams already using Kong can apply consistent policies across traditional APIs and LLM traffic, reducing operational fragmentation. The constraint is that AI is layered onto a general-purpose API gateway rather than designed as a native abstraction. Multi-provider guardrails, MCP gateway controls, and LLM-specific cost attribution often require custom plugins or external systems.

Best for: Large enterprises that already run Kong Gateway across their API infrastructure and want to extend that governance model to LLM traffic.

4. Cloudflare AI Gateway: Edge-Based Routing with Network Security

Cloudflare AI Gateway integrates AI routing into Cloudflare's edge network, combining caching, rate limiting, and security features with model access. It requires no infrastructure setup and is accessible directly through the Cloudflare dashboard.

Core security features include request caching to reduce duplicate inference cost, rate limiting per route, usage analytics, and basic logging. Integration with Cloudflare's existing WAF, bot management, and DDoS protection means AI traffic inherits the network-level security posture teams already run for their web applications. The trade-off is depth: application-layer guardrails like PII detection, prompt injection defense, and multi-provider safety policies are not native and must be handled either upstream in application code or via integrations.

Best for: Teams already invested in the Cloudflare ecosystem who want basic AI gateway features tightly coupled with their existing network security stack.

5. AWS Bedrock: Managed Model Access with Native Guardrails

AWS Bedrock is less a traditional gateway and more a managed model access layer within AWS, but it warrants inclusion because Bedrock Guardrails is one of the most mature managed content safety services available. It provides configurable filters for hate speech, violence, sexual content, and misconduct; denied topics defined in natural language; PII detection and redaction; and contextual grounding checks. The ApplyGuardrail API allows teams to validate content independently of model inference.

For AWS-native organizations, Bedrock provides foundation model access, IAM-based access control, CloudTrail audit logs, and KMS-backed encryption out of the box. HIPAA-eligible, SOC-certified, and FedRAMP-authorized configurations are available depending on region.

The constraint is that Bedrock is a model platform, not a multi-provider gateway. Routing across non-AWS providers requires a separate gateway layer, and MCP governance and unified audit logs spanning multiple model platforms are not in scope. Many enterprises pair Bedrock Guardrails with a multi-provider gateway like Bifrost, which natively integrates Bedrock Guardrail ARNs and applies them globally to every request, regardless of which downstream provider serves the model.

Best for: AWS-centric organizations using Bedrock as their primary inference platform, often paired with a multi-provider gateway for non-AWS traffic.

How These AI Gateways Map to OWASP LLM Top 10 Mitigations

Mapping gateway capabilities to OWASP risk categories shows where each platform delivers natively:

• LLM01 Prompt Injection: Bifrost (Azure Prompt Shield, Bedrock prompt attack prevention, GraySwan, CEL rules), AWS Bedrock (native), Kong (via plugins), LiteLLM (via hooks).
• LLM02 Sensitive Information Disclosure: Bifrost (Bedrock PII plus Patronus output validation), AWS Bedrock (native), others via integrations.
• LLM05 Improper Output Handling: Bifrost (output rules with redact or block), AWS Bedrock (output guardrails).
• LLM06 Excessive Agency: Bifrost (per-virtual-key MCP tool filtering, OAuth, audit logs); others require custom code.
• LLM08 Vector and Embedding Weaknesses: Bifrost (guardrails applied to RAG responses to catch indirect injection payloads).

The runtime telemetry from inline guardrails also produces conformity evidence aligned with the NIST AI Risk Management Framework and EU AI Act obligations for high-risk AI systems.

Choosing the Right AI Gateway to Secure Your AI Apps

The right gateway depends on the threat model and operating environment:

• Defense-in-depth across multiple providers and agentic workloads: Bifrost. Native multi-provider guardrails, MCP governance, virtual keys, audit logs, and vault support in a single open-source platform.
• Single-team Python prototypes moving toward early production: LiteLLM, with a migration path as scale and audit demands grow.
• Kong-standardized enterprises: Kong AI Gateway, with LLM features layered on a general API gateway.
• Cloudflare-centric stacks needing edge gateway features: Cloudflare AI Gateway.
• AWS-only inference workloads: AWS Bedrock paired with a multi-provider gateway for non-AWS traffic.

For most enterprise teams, the strongest pattern is layering: Bifrost as the multi-provider control plane, with Bedrock Guardrails or Azure Content Safety as guardrail backends, and provider keys held in a vault. This gives a single audit-ready control plane that satisfies OWASP, SOC 2, HIPAA, and EU AI Act obligations without slowing developer iteration.

Secure Your AI Apps with Bifrost

Among the top 5 AI gateways to secure your AI apps, Bifrost is the only option that consolidates inline guardrails, virtual-key governance, MCP tool controls, immutable audit logs, and vault-backed credential management into a single open-source binary, with sub-microsecond overhead at production scale. Teams can deploy Bifrost in-VPC, point existing OpenAI, Anthropic, or Bedrock SDKs at it with a one-line base URL change, and inherit gateway-level security on day one. To see how Bifrost can secure your AI apps across every provider and every agent, book a demo.