LLM Gateway

Top 5 AI Gateways to Implement Guardrails in Your AI Applications

AI applications face critical safety challenges that can expose organizations to security vulnerabilities, compliance violations, and reputational damage. Prompt injection attacks, data leakage, toxic content generation, and hallucinations pose substantial risks when deploying AI systems at scale. AI gateways with built-in guardrails provide a systematic approach to implementing safety controls across your AI infrastructure.

An AI gateway acts as a control plane between your applications and foundation models, enabling you to enforce policies, validate content, and monitor behavior without modifying individual applications. This architecture provides centralized governance while reducing implementation overhead.

Why Guardrails Matter for Production AI Systems

Organizations deploying AI applications encounter several categories of risk that require systematic mitigation:

Security vulnerabilities: Prompt injection attacks can manipulate model behavior to bypass safety controls or extract sensitive information from system prompts
Data privacy violations: AI applications may inadvertently expose personally identifiable information (PII) including social security numbers, financial data, or health records through model responses
Content safety issues: Models can generate harmful content including hate speech, violence, or misinformation without appropriate filtering mechanisms
Compliance requirements: Regulated industries face specific requirements for data handling, audit trails, and content moderation that require enforcement at the infrastructure level

Implementing guardrails at the gateway layer provides consistent protection across all AI interactions while enabling teams to iterate on applications without rebuilding safety controls for each use case.

1. Bifrost by Maxim AI

Bifrost is an open source LLM gateway built in Go that provides enterprise-grade guardrails functionality. The core gateway is available as open source and free for all users, while advanced guardrails capabilities are available in the enterprise version.

Key Guardrails Features

Bifrost integrates with three leading guardrail providers to deliver comprehensive protection:

AWS Bedrock Guardrails: Content filtering across hate speech, violence, sexual content, and misconduct with configurable severity thresholds. PII detection and redaction for 50+ entity types including financial information, health records, and personal identifiers. Contextual grounding checks to verify response accuracy against source documents.
Azure Content Safety: Multi-modal content moderation with four-level severity classification. Prompt Shield technology for detecting jailbreak attempts and indirect attacks. Groundedness detection to identify hallucinations and factual inaccuracies.
Patronus AI: Specialized LLM security evaluations including hallucination detection, toxicity screening, and prompt injection defense. Custom policy templates that organizations can adapt to specific use cases.

Implementation Approach

Bifrost implements guardrails through a flexible configuration system that allows validation at both input and output stages. Organizations can attach guardrails to specific API calls using custom headers or configure default guardrails that apply to all requests. The system supports both synchronous and asynchronous validation modes depending on latency requirements.

Response handling includes three action types: block (prevent request/response from proceeding), redact (remove sensitive content), and log (record violations without blocking). This granular control enables teams to tune guardrail behavior based on application requirements and risk tolerance.

Enterprise Deployment

The Bifrost enterprise version includes production-grade features for large-scale deployments including clustering for high availability, adaptive load balancing across multiple providers, vault integration for secure credential management, and comprehensive audit logging for compliance requirements.

Organizations can request a free 14-day enterprise trial to evaluate advanced guardrails capabilities.

2. Portkey

Portkey provides an AI gateway with over 60 built-in guardrails that organizations can apply to LLM requests and responses. The platform offers both managed cloud services and open source gateway options.

Guardrails Capabilities

The Portkey guardrails framework includes:

Input validation: Screening for prompt injection attempts, malicious content, and off-topic queries before they reach foundation models
Output validation: Filtering model responses for harmful content, PII leakage, and policy violations
Integration ecosystem: Native integrations with specialized guardrail providers including Palo Alto Networks AIRS for security threat detection and custom webhook support for proprietary validation logic

Configuration and Routing

Portkey enables routing decisions based on guardrail results. Organizations can configure policies that automatically retry failed requests with different models, deny risky requests entirely, or route requests to specialized models based on content classification.

The platform provides real-time visibility into guardrail performance including pass/fail rates, latency impact, and violation patterns across different content categories.

3. LiteLLM

LiteLLM offers an open source LLM gateway with extensive guardrails support through integrations with third-party providers. The platform focuses on flexibility and customization for teams that require fine-grained control over validation logic.

Guardrails Architecture

LiteLLM supports guardrail execution at three stages:

Pre-call validation: Inspect and validate input prompts before forwarding to foundation models
During-call validation: Run validation in parallel with model inference to minimize latency impact
Post-call validation: Analyze model responses before returning to applications

Organizations can configure multiple guardrails to run sequentially at each stage, enabling layered defense approaches.

Built-in and Custom Guardrails

Recent versions of LiteLLM include built-in guardrails that execute without external API dependencies:

Keyword blocking for sensitive terms
Pattern detection using regular expressions for emails, social security numbers, and API keys
Custom regex patterns for organization-specific content policies

The platform also supports integration with external providers including AWS Bedrock Guardrails, Guardrails AI, Aporia, and Lakera for more sophisticated validation logic.

Per-Key Guardrail Control

LiteLLM enables teams to configure different guardrail policies for individual API keys or projects. This capability supports multi-tenant deployments where different applications or customers require distinct safety policies.

4. AWS Bedrock Guardrails

AWS Bedrock Guardrails provides a standalone API for content validation that works with any foundation model, whether hosted on AWS or elsewhere. The service offers comprehensive safety features backed by AWS infrastructure.

Core Safety Features

Bedrock Guardrails implements six configurable policy types:

Content filters: Detection and blocking of harmful categories including hate speech, insults, sexual content, violence, and misconduct. Organizations can set filter strength independently for each category.
Denied topics: Natural language definitions of topics that models should not discuss. The service uses these descriptions to detect and block related content in both prompts and responses.
Word filters: Custom lists of prohibited terms with support for both exact matching and pattern-based detection.
PII redaction: Identification and masking of personal information across multiple entity types with configurable handling for different data categories.
Contextual grounding: Verification that model responses align with provided source documents, blocking responses that contradict reference information.
Automated Reasoning checks: Mathematical validation of model outputs against formal policies, providing auditable verification for regulated industries.

ApplyGuardrail API

The ApplyGuardrail API enables content validation independent of model inference. Organizations can use this endpoint to validate user inputs before processing, check content from external sources, or implement multi-stage validation workflows that separate content screening from model invocation.

5. Azure Content Safety

Azure AI Content Safety provides content moderation capabilities through Microsoft's cognitive services platform. The service supports text and image analysis with real-time detection of harmful content.

Detection Capabilities

Azure Content Safety analyzes content across multiple risk categories:

Harmful content detection: Severity-based classification for hate speech, sexual content, violence, and self-harm with four levels ranging from safe to high risk
Prompt Shield: Advanced detection of jailbreak attempts and indirect attacks that attempt to manipulate model behavior
Groundedness detection: Verification that generated content aligns with provided source material
Protected material detection: Identification of copyrighted content in model outputs

Custom Categories

Organizations can define custom content categories using natural language descriptions and sample content. The service uses these definitions to detect organization-specific policy violations beyond standard content categories.

Choosing the Right Gateway for Your Use Case

Selecting an AI gateway depends on several factors specific to your deployment requirements:

Performance requirements: Gateways built in compiled languages like Go (Bifrost) or Rust (Helicone) typically provide lower latency overhead compared to Python-based solutions
Provider ecosystem: Evaluate whether the gateway supports the foundation model providers and guardrail services your organization uses
Deployment model: Consider whether you need managed cloud services, self-hosted options, or hybrid approaches that support both
Integration complexity: Assess how easily the gateway integrates with existing infrastructure including authentication systems, logging platforms, and monitoring tools
Cost structure: Evaluate both gateway costs and potential savings from features like caching, load balancing, and intelligent routing

For organizations requiring comprehensive guardrails capabilities with high performance, Bifrost provides an open source foundation with enterprise-grade security features. Teams can start with the free open source version and upgrade to enterprise capabilities as requirements expand.

Implementing Guardrails at Scale

Successful guardrails implementation requires more than selecting a gateway. Organizations should:

Define clear safety policies that align with regulatory requirements and organizational values
Establish baselines for acceptable content across different application contexts
Configure appropriate actions for different violation types based on risk severity
Monitor guardrail performance to identify false positives that may impact user experience
Create feedback loops that incorporate guardrail violations into model improvement workflows

Maxim AI's platform provides comprehensive observability for AI applications, enabling teams to track guardrail performance alongside other quality metrics across the AI lifecycle.

Ready to implement enterprise-grade guardrails for your AI applications? Start with Bifrost to get comprehensive protection with minimal implementation overhead, or schedule a demo to learn how Maxim AI's full platform can help you build, evaluate, and monitor AI systems with confidence.