As AI systems graduate from demos to mission-critical production workloads, guardrails are no longer optional. Without strong safeguards, LLM-powered applications can hallucinate facts, expose sensitive data, break compliance rules, or generate content that harms brand trust.
AI gateways are the most effective place to enforce these guardrails. Since every prompt and response passes through the gateway, it becomes the natural control plane for policy enforcement, safety checks, cost limits, and governance.
This guide breaks down the top 5 AI gateways for implementing guardrails in production AI systems, evaluated on guardrail depth, performance overhead, governance capabilities, and developer experience.
Why AI Gateways Are the Ideal Guardrail Layer
Embedding guardrails directly inside application code often leads to fragmented and inconsistent enforcement. Each service or agent ends up reinventing safety logic, increasing maintenance costs and policy drift.
AI gateways solve this by centralizing enforcement at the infrastructure layer. Key benefits include:
- Uniform policy enforcement - Every model call, across teams and applications, is evaluated against the same guardrail rules
- Clear separation of responsibilities - Product teams focus on features while platform teams manage safety and compliance
- Immediate intervention - Unsafe outputs can be blocked or modified before reaching users, without code changes
- Audit-ready logging - All policy decisions and violations are captured centrally for compliance and investigations
With that foundation, here are the top AI gateways for guardrails in 2025.
1. Bifrost by Maxim AI
Bifrost is a high-performance, open-source AI gateway written in Go, purpose-built for production-scale AI workloads. It offers one of the most complete guardrail and governance stacks available today, while maintaining near-zero latency impact. Public benchmarks show ~11 microseconds of overhead at 5,000 RPS.
Guardrail strengths
- Real-time response blocking - Detects and blocks unsafe outputs instantly using configurable moderation and policy rules
- Infrastructure-level governance - Enforces rate limits, usage tracking, and fine-grained access controls on every request
- Cost and budget guardrails - Hierarchical budgets across orgs, teams, and customers prevent runaway spend
- MCP tool governance - Restricts which tools agents can invoke through Model Context Protocol controls
- Role-based access control - Granular permissions with SSO support for centralized identity management
What truly differentiates Bifrost is its tight integration with Maxim’s evaluation and observability platform. Guardrails enforced at the gateway layer feed directly into continuous evaluation pipelines, trace-level analysis, and production quality checks. This creates a closed feedback loop where real-world failures improve future policies.
Bifrost also supports a custom plugin system, allowing teams to inject organization-specific guardrail logic as middleware.
Performance
Designed for scale, Bifrost delivers industry-leading performance. Benchmarks show 54x faster P99 latency and 9x higher throughput than Python-based gateways on the same hardware.
Best for: Teams running production AI systems that require strict safety guarantees, deep governance, and minimal latency, especially when paired with evaluation and observability workflows.
2. LiteLLM
LiteLLM is a popular open-source gateway that standardizes access to 100+ LLM providers through OpenAI-compatible APIs. It includes basic guardrail functionality via its proxy mode.
Guardrail strengths
- Keyword and phrase blocking using configurable filters
- Regex-based rules for detecting PII or disallowed content
- Spend controls with virtual keys and project-level budgets
- Observability hooks for tools like Langfuse and MLflow
Trade-offs
Because LiteLLM is Python-based, it can struggle under high concurrency. Benchmarks show extreme P99 latency spikes at higher RPS levels, which limits its suitability for real-time guardrail enforcement in large-scale systems.
Best for: Smaller deployments or Python-centric teams that need basic guardrails and broad provider compatibility.
3. Kong AI Gateway
Kong AI Gateway builds on Kong’s established API management platform, extending enterprise-grade security and governance to AI traffic.
Guardrail strengths
- AI Prompt Guard plugin for regex and semantic similarity checks
- PII masking and redaction across multiple languages
- Gateway-level RAG orchestration to reduce hallucinations
- Prompt control and transformation with safety enforcement
Best for: Enterprises already using Kong that want to apply existing API governance policies to AI workloads without introducing a new gateway.
4. Cloudflare AI Gateway
Cloudflare AI Gateway delivers guardrails at the network edge, powered by Cloudflare Workers AI and Llama Guard models.
Guardrail strengths
- Built-in moderation for prompts and responses across common risk categories
- Configurable actions like block, flag, or allow per category
- Data loss prevention for PII, credentials, and jailbreak attempts
- Edge-based inference using Cloudflare’s global GPU footprint
Limitations
Streaming responses are not yet supported for guardrails, and the platform lacks advanced hierarchical governance or self-hosted deployment options.
Best for: Teams already on Cloudflare that want fast, globally distributed content moderation with minimal setup.
5. OpenRouter
OpenRouter focuses on unifying access to 200+ models with transparent pricing and intelligent routing. Its guardrail model emphasizes cost and provider selection rather than content safety.
Guardrail strengths
- Safety-aware model routing based on provider capabilities
- Cost controls with credits and spend visibility
- Usage analytics across models and vendors
- Automatic provider fallbacks for reliability
Limitations
OpenRouter does not offer real-time content moderation, PII detection, or policy enforcement at the gateway level.
Best for: Experimentation-heavy teams that care more about cost optimization and model choice than strict safety enforcement.
How to Choose the Right AI Gateway for Guardrails
When selecting a gateway, evaluate:
- Real-time enforcement vs post-hoc detection
- Latency impact at production scale
- Governance depth across orgs, teams, and users
- Integration with evaluation pipelines for continuous improvement
- Extensibility through plugins or custom middleware
Gateways that connect guardrails directly to evaluation workflows enable faster iteration and stronger long-term safety.
Final Thoughts
AI gateways have become the control plane for safe and compliant AI systems. While each option serves a different audience, Bifrost by Maxim AI stands out by combining real-time guardrails, deep governance, and industry-leading performance in an open-source package, with seamless integration into a full-stack AI quality platform.
No matter which gateway you adopt, enforcing guardrails at the infrastructure layer - not scattered across application code - is the most reliable way to scale AI safely.
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