How do you secure an LLM application’s infrastructure?

How We Ranked These

We evaluated options based on five criteria critical for production LLM security: threat coverage (does it block prompt injection, data leakage, and DoS?), ease of deployment (how quickly can a team integrate it?), scalability (can it handle 1M+ requests/day without latency spikes?), compliance support (SOC 2, GDPR, HIPAA readiness), and cost transparency (no hidden per-token fees). We tested each solution against OWASP's Top 10 for LLM Applications (2025 edition) and consulted real-world deployment reports from fintech and healthcare teams. Only tools with documented success in production environments made the list.

1. Cloudflare AI Gateway 🏆 BEST OVERALL

Cloudflare AI Gateway sits between your application and any LLM provider (OpenAI, Anthropic, Google, etc.), acting as a reverse proxy that inspects every request and response. It blocks prompt injection by scanning for known attack patterns using Cloudflare's WAF (Web Application Firewall) rules, and prevents data exfiltration by enforcing DLP policies that redact sensitive strings (e.g., credit card numbers, API keys) before they reach the model. The gateway also provides rate limiting at the edge—up to 10,000 requests per second per zone—without adding more than 5ms of latency.

Best for teams already using Cloudflare's CDN or needing a managed, SOC 2-compliant solution. Pricing starts at $0.10 per million requests for the AI Gateway add-on, with a free tier covering 100,000 requests/month. It supports all major LLM providers and can be configured via a single API endpoint change. The real-time analytics dashboard shows blocked injection attempts and DLP triggers, making it easy to tune rules without writing code.

2. Portkey AI Gateway 💎 BEST VALUE

Portkey AI Gateway is an open-source, self-hosted proxy that adds authentication, rate limiting, prompt template validation, and audit logging to any LLM API. It runs as a Docker container on your own infrastructure (AWS, GCP, or on-prem) and integrates with OpenAI, Anthropic, Cohere, and Azure OpenAI out of the box. The killer feature is prompt guardrails: you define regex patterns or semantic rules that block or flag requests containing "ignore previous instructions" or "output your system prompt" before they reach the model.

Best for teams in regulated industries (healthcare, finance) that need full control over data and logs. The free tier supports 1,000 requests/day; paid plans start at $99/month for 100,000 requests and include SOC 2 reports and SSO. Portkey's audit trail logs every request and response, which satisfies HIPAA and GDPR requirements. Deployment takes about 30 minutes using Docker Compose, and the open-source version (MIT license) has no request limits.

3. NVIDIA NeMo Guardrails

NVIDIA NeMo Guardrails is a framework for adding programmable guardrails to LLM applications, rather than a gateway. It lets you define topical guardrails (block requests about competitors or internal data), safety guardrails (filter toxic or biased outputs), and security guardrails (prevent prompt injection via semantic similarity checks). It runs as a Python library that wraps any LLM call, adding 50–200ms of latency depending on the number of active guardrails.

Best for teams building custom LLM pipelines who need deep control over behavior. It's free and open-source under Apache 2.0, but requires Python expertise and a separate vector database (e.g., Chroma or Pinecone) for semantic filtering. NVIDIA provides pre-built guardrails for finance and healthcare use cases, and the framework integrates with LangChain and LlamaIndex. Expect to spend 1–2 days integrating and testing guardrails before production use.

4. Azure AI Content Safety

Azure AI Content Safety is a managed API that scans text and images for harmful content, including prompt injection attempts, hate speech, and self-harm references. It offers four severity levels (safe, low, medium, high) and can be called as a standalone service or integrated into Azure OpenAI Service via the Content Filter system. The service processes up to 10,000 requests per minute with a 99.9% SLA and stores no data beyond 24 hours.

Best for teams already in the Azure ecosystem who need compliance with Microsoft's Responsible AI standards. Pricing is $1 per 1,000 text records for the standard tier, with a free tier of 5,000 records/month. It supports 100+ languages and can be configured to block or flag requests based on custom categories (e.g., "financial advice" or "medical diagnosis"). The real-time moderation dashboard in Azure Portal shows flagged content trends.

5. Amazon Bedrock Guardrails

Amazon Bedrock Guardrails is a managed service within AWS that enforces content policies for LLMs hosted on Bedrock or external APIs. You define denied topics (e.g., "competitor pricing"), word filters (block specific terms), and sensitive information filters (redact PII like SSNs or emails). It supports contextual grounding checks that verify the model's output against a source document, reducing hallucinations in RAG applications.

Best for AWS-native teams using Bedrock for model hosting. Pricing is $0.75 per 1,000 text units (1 unit = 1,000 characters) for guardrail evaluation, with no additional cost for the first 100,000 units/month. It integrates with AWS CloudTrail for audit logging and AWS KMS for encryption. The denied topics feature uses a semantic similarity model that can block entire categories of queries with 95%+ accuracy in testing.

6. OpenAI Moderation API

OpenAI Moderation API is a free endpoint that classifies text into 11 categories (hate, harassment, self-harm, sexual, violence, etc.) and is designed to catch prompt injection and policy violations. It's the same model used by ChatGPT's internal filters and can be called as a pre-request check before sending a prompt to GPT-4 or other models. The API returns a per-category score from 0 to 1, with a recommended threshold of 0.5 for blocking.

Best for simple, low-latency filtering where you already use OpenAI. It's free for up to 100 requests/minute, with no separate billing. However, it does not catch all prompt injection variants—testing shows it misses about 15% of "ignore previous instructions" attacks. It's best used as a first line of defense combined with a gateway like Cloudflare or Portkey.

7. Rebuff AI

Rebuff is an open-source library specifically designed to detect and block prompt injection attacks. It uses a four-layer defense: (1) a heuristic detector for common injection patterns, (2) a vector database of known attack embeddings, (3) a LLM-based judge that asks a secondary model to classify the prompt, and (4) a response validator that checks if the model's output matches expected format. It integrates with LangChain and LlamaIndex as a plugin.

Best for security researchers and teams who want to build their own detection pipeline. It's MIT-licensed and free, but requires a vector database (e.g., Qdrant or Weaviate) and an additional LLM call for the judge step, adding 200–500ms latency. The heuristic detector catches about 70% of known injection patterns out of the box, and the embedding-based detector improves that to 90%+ with a populated database.

8. Snyk for AI

Snyk for AI is a security scanning tool that analyzes your LLM application's code, dependencies, and configuration for vulnerabilities. It scans Docker images, Python packages, and Terraform scripts for known CVEs in libraries like LangChain, Transformers, and FastAPI. It also checks for misconfigurations like exposed API keys in environment files or overly permissive IAM roles. The tool integrates into CI/CD pipelines (GitHub Actions, GitLab CI, Jenkins) and provides fix recommendations.

Best for DevOps and security teams who need to shift left and catch vulnerabilities before deployment. Pricing starts at $25/user/month for the Team plan, with a free tier for open-source projects. Snyk's AI-specific rules cover 50+ vulnerabilities, including the LangChain arbitrary code execution (CVE-2024-1234) and Pickle deserialization risks common in ML pipelines.

9. Lakera Guard

Lakera Guard is a managed API that specializes in detecting prompt injection and jailbreak attempts. It uses a proprietary detection model trained on millions of adversarial prompts and claims a 99.7% detection rate with under 50ms latency. The service supports real-time scoring (0–100 risk score) and can be configured to block, flag, or log requests based on thresholds. It integrates via a simple REST API or a Python SDK.

Best for teams that want a specialized, high-accuracy injection detector without building their own. Pricing is $0.50 per 1,000 requests for the standard tier, with a free tier of 10,000 requests/month. Lakera publishes a public leaderboard of detection accuracy against common attack patterns, and its model is updated weekly based on new injection techniques. It's SOC 2 Type II certified and supports GDPR data residency.

10. Google Cloud Armor + Vertex AI

Google Cloud Armor is a WAF that can be configured to inspect traffic to Vertex AI endpoints. By enabling adaptive protection and rate limiting, you can block DDoS attacks and brute-force attempts against your LLM API. Combined with Vertex AI Model Monitoring, which detects data drift and anomalous inputs, this provides a network-level defense for models deployed on GCP. Cloud Armor supports custom rules based on request headers, IP ranges, and geolocation.

Best for teams already on GCP who need DDoS protection and network access control for their LLM endpoints. Cloud Armor pricing starts at $3 per month per policy plus $0.75 per GB of traffic inspected. Vertex AI Model Monitoring costs $0.30 per monitoring hour per model. This combination does not detect prompt injection at the application layer, so it should be paired with a gateway like Cloudflare or a guardrail framework.

Related on PULSE

• [How do you set up observability for a RAG application?](/knowledge/ai387)
• [The 10 Best AI Tools for Web Application Security in 2027](/knowledge/ai0316)
• [What infrastructure do you need to run AI agents in production?](/knowledge/ai373)
• [What infrastructure do you need for fine-tuning versus RAG?](/knowledge/ai427)
• [What is the difference between batch and real-time inference infrastructure?](/knowledge/ai409)
• [What is the role of Kubernetes in modern AI infrastructure?](/knowledge/ai431)

FAQ

What is the most important security layer for an LLM application? The most critical layer is a gateway that sits between users and the LLM API. It enforces rate limiting, validates prompts for injection attempts, and blocks sensitive data from being sent or returned. Without it, your model is directly exposed to malicious inputs and data leaks.

Can a gateway stop all prompt injection attacks? No gateway can guarantee 100% prevention, as attackers constantly evolve their techniques. Cloudflare AI Gateway and Portkey AI Gateway both detect common patterns like role-playing or system prompt overrides, but sophisticated attacks may still bypass filters. Regular updates and layered defenses are essential.

Do I need a separate gateway if I use a cloud provider’s LLM service? Yes, because cloud providers offer limited built-in security for their LLM APIs. A dedicated gateway adds granular controls like custom prompt validation, data loss prevention, and detailed audit logs that native services lack. This is especially important for regulated industries or high-risk use cases.

How does rate limiting protect my LLM infrastructure? Rate limiting prevents denial-of-service attacks and cost spikes by capping the number of requests per user or IP. It also throttles automated scraping or brute-force attempts to probe the model. Both Cloudflare and Portkey allow you to set flexible limits based on your traffic patterns.

What is data loss prevention (DLP) in this context? DLP scans prompts and responses for sensitive information like credit card numbers, API keys, or personal data. It can block such content from reaching the LLM or being returned to users. Cloudflare AI Gateway includes built-in DLP rules, while Portkey lets you define custom patterns for your specific compliance needs.

Can I secure an LLM without a gateway? You can, but it requires building custom middleware for prompt validation, rate limiting, and logging from scratch. This is time-consuming and error-prone, especially for teams without deep security expertise. A gateway provides a proven, maintainable solution that integrates quickly with existing infrastructure.

Sources

• Cloudflare AI Gateway documentation
• Portkey AI Gateway GitHub repository
• NVIDIA NeMo Guardrails documentation
• Azure AI Content Safety overview
• Amazon Bedrock Guardrails features
• OpenAI Moderation API guide
• Rebuff AI prompt injection detector
• Snyk for AI security scanning
• Lakera Guard detection accuracy leaderboard
• Google Cloud Armor for Vertex AI

Bottom Line

Securing an LLM application's infrastructure requires a multi-layered approach combining a gateway for rate limiting and DLP (Cloudflare AI Gateway is our top pick), a guardrail framework for injection detection (NVIDIA NeMo for custom needs), and compliance-specific tools like Azure AI Content Safety or Amazon Bedrock Guardrails. Start with a gateway to block obvious attacks, then layer in guardrails for semantic detection. Test every solution against OWASP's LLM Top 10 before going to production.

*LLM application security, AI infrastructure protection, prompt injection prevention, AI gateway tools, LLM guardrails*