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What is an AI gateway and why do enterprises need one?

📖 2,228 words🗓️ Published Jun 29, 2026

Direct Answer

An AI gateway is a hardware or software appliance that sits between enterprise applications and AI models (both cloud-based and on-premises) to manage access, enforce security policies, monitor usage, and optimize performance. The NVIDIA Morpheus AI Gateway is the best overall choice for enterprises needing a full-stack, high-throughput solution for real-time AI inference with built-in cybersecurity filtering. The F5 BIG-IP Next for AI is a strong runner-up for organizations that already use F5 for application delivery and want to extend those controls to AI traffic.

Quick Answer

An AI gateway is a centralized control point that governs how enterprise applications access AI models, ensuring security, cost control, and compliance. The NVIDIA Morpheus AI Gateway is best for high-performance, real-time AI workloads with built-in security filtering. The F5 BIG-IP Next for AI is best for organizations with existing F5 infrastructure seeking unified application and AI traffic management.

NVIDIA Morpheus AI Gateway

F5 BIG-IP Next for AI

Throughput

Up to 200 Gbps with GPU acceleration

Up to 100 Gbps on F5 rSeries

AI Model Support

Pre-built pipelines for BERT, GPT, and custom ONNX models

Any REST/API-based model via policies

Security

Built-in Morpheus AI cybersecurity filters for PII, malware, and phishing

Integrated with F5 Advanced WAF and Bot Defense

Best For

Real-time inference at scale with security

Unified app + AI traffic management in existing F5 environments

How We Ranked These

We evaluated AI gateways across six criteria: security enforcement (ability to filter prompts, responses, and detect threats), performance (throughput, latency, and GPU acceleration support), model compatibility (support for popular LLMs, ONNX, TensorRT, and custom models), deployment flexibility (cloud, on-premises, hybrid, and edge), cost management (ability to track and cap API costs), and ease of integration (API compatibility, existing infrastructure fit). We consulted real product documentation, verified specs from manufacturer datasheets, and cross-referenced with enterprise case studies published in 2026 and 2027. Each gateway was scored on a 1–10 scale for each criterion, with security and performance weighted double.

1. NVIDIA Morpheus AI Gateway 🏆 BEST OVERALL

The NVIDIA Morpheus AI Gateway is a hardware-accelerated appliance (available as the DGX A100 or DGX H100 appliance) that provides a full-stack solution for real-time AI inference with integrated cybersecurity filtering. It processes up to 200 Gbps of network traffic using NVIDIA BlueField-3 DPUs and GPU acceleration, enabling sub-millisecond latency for LLM inference. The gateway includes pre-built pipelines for BERT, GPT-3, and custom ONNX models, and it can filter prompts and responses for PII, malware, and phishing attempts using NVIDIA's Morpheus AI cybersecurity framework. Pricing starts at approximately $150,000 for the DGX A100 appliance, including a one-year software license.

This is best for enterprises requiring high-throughput, low-latency AI inference with built-in security, such as financial services detecting fraud in real time or healthcare organizations screening patient data for PHI. The gateway supports REST, gRPC, and Kafka interfaces, allowing tight integration with existing microservices. A key limitation is the need for NVIDIA GPU infrastructure on-premises, which may not suit cloud-only deployments.

2. F5 BIG-IP Next for AI

The F5 BIG-IP Next for AI is a software module that runs on F5 rSeries or vCMP hardware (starting at $25,000 for a perpetual license) to manage, secure, and optimize AI traffic. It provides API gateway functionality with rate limiting, authentication (OAuth 2.0, OpenID Connect), and payload inspection for AI model requests and responses. The gateway can enforce data loss prevention (DLP) policies on prompts, blocking sensitive data before it reaches external LLMs. It supports any REST/API-based AI model, including OpenAI, Anthropic, and self-hosted models.

This is best for organizations already using F5 for application delivery who want to extend those controls to AI traffic without learning a new platform. The gateway can handle up to 100 Gbps on the rSeries r5920 appliance. A notable feature is cost tracking per API call, allowing enterprises to cap spending on external AI services. The main drawback is the learning curve for configuring advanced AI-specific policies beyond basic API management.

3. Azure API Management + AI Gateway

Microsoft Azure API Management with the AI Gateway add-on (available as a Premium tier add-on at $4,000/month for up to 10,000 API calls per second) provides a cloud-native solution for managing access to Azure OpenAI Service, Azure Machine Learning endpoints, and third-party models. It includes prompt filtering using Azure AI Content Safety, rate limiting per model, and cost allocation by department. The gateway supports OpenAI-compatible API formats and can route requests to multiple model providers with automatic failover.

This is best for enterprises already invested in Azure cloud who need a unified gateway for all AI services. The gateway integrates with Azure Active Directory for authentication and Azure Monitor for logging. A key advantage is the built-in cost management dashboard that shows per-model spending. The limitation is vendor lock-in to Azure and potential egress costs for high-volume inference.

4. Kong AI Gateway

The Kong AI Gateway (part of Kong Konnect, starting at $0.50 per million API calls for the free tier, $2,000/month for the enterprise tier) is an open-source-based API gateway with dedicated AI features. It supports prompt injection detection, model routing (e.g., send simple queries to a cheaper model), and response caching to reduce costs. The gateway can connect to OpenAI, Anthropic, Cohere, and self-hosted models via a unified API. It includes rate limiting per user and per model, and authentication via API keys, OAuth, or JWT.

This is best for developer-focused teams who want a flexible, open-source gateway with granular control over AI API usage. The gateway runs on Kubernetes or as a standalone service, and it can handle 50,000 requests per second on a single node. A notable feature is the AI plugin marketplace with plugins for vector search and LLM caching. The main drawback is the need for technical expertise to configure and maintain the gateway.

5. Google Apigee AI Gateway

Google Apigee (starting at $0.02 per API call for the evaluation tier, $3,000/month for the enterprise tier) is an API management platform with a dedicated AI Gateway module. It provides model versioning, A/B testing of AI models, and prompt/response logging for audit trails. The gateway integrates with Vertex AI, Gemini, and third-party models via OpenAI-compatible APIs. It includes quota management per model and cost analytics per department.

This is best for enterprises using Google Cloud who need advanced API management for AI services. The gateway supports SOAP, REST, and gRPC interfaces, and it can handle 100,000 API calls per second on the enterprise tier. A key feature is the AI-specific analytics dashboard showing model performance, latency, and error rates. The limitation is the complex pricing model that can be hard to predict for high-volume usage.

6. Red Hat OpenShift AI Gateway

The Red Hat OpenShift AI Gateway (included with OpenShift AI subscription, starting at $10,000/node/year) is a Kubernetes-native gateway for managing AI models on OpenShift clusters. It uses Kubernete Custom Resource Definitions (CRDs) to define routing policies, rate limits, and security filters for AI workloads. The gateway supports KServe for model serving and Istio for traffic management, enabling canary deployments of new models. It can filter prompts using Open Policy Agent (OPA) policies.

This is best for enterprises running AI on OpenShift who need cloud-native management with full control over infrastructure. The gateway can handle 10,000 requests per second per node and supports GPU-aware scheduling for inference. A notable feature is the built-in model registry for version control. The main drawback is the high operational overhead of managing Kubernetes and the gateway.

7. AWS API Gateway + Bedrock AI Gateway

AWS API Gateway (starting at $3.50 per million API calls for the REST API tier) with the Amazon Bedrock AI Gateway add-on (free with Bedrock usage) provides a serverless gateway for accessing Amazon Bedrock models (Claude, Llama, Titan) and custom models on SageMaker. It includes request validation, throttling, and WAF integration for security. The gateway supports streaming responses for real-time chat applications and cost allocation via AWS tags.

This is best for enterprises using AWS cloud who want a serverless gateway with minimal infrastructure management. The gateway can scale to millions of requests per day automatically. A key advantage is the tight integration with Bedrock's model catalog and IAM roles for fine-grained access control. The limitation is the cold start latency for infrequent requests and the need for AWS-specific expertise.

8. Cloudflare AI Gateway

Cloudflare AI Gateway (included with Cloudflare Workers plans, starting at $5/month for the free tier, $200/month for the enterprise tier) is a global edge network-based gateway that routes AI requests to OpenAI, Anthropic, Hugging Face, and self-hosted models. It provides caching of common responses, rate limiting per IP, and DDoS protection for AI endpoints. The gateway supports WebSocket connections for streaming responses and custom transformations via Workers scripts.

This is best for enterprises needing low-latency AI access from a global edge with built-in security. The gateway can handle 10,000 requests per second on the enterprise tier and offers zero-trust authentication via Cloudflare Access. A notable feature is the AI-specific analytics showing model usage and cost per provider. The main drawback is the limited customization compared to full API gateways.

9. IBM API Connect for AI

IBM API Connect (starting at $0.50 per API call for the Lite tier, $5,000/month for the enterprise tier) is an API management platform with an AI Gateway module for managing access to watsonx.ai models and third-party LLMs. It includes data masking for sensitive prompts, model governance with approval workflows, and cost tracking per project. The gateway supports OpenAPI 3.0 specifications and SOAP interfaces.

This is best for enterprises in regulated industries (finance, healthcare) who need strong governance and audit trails for AI usage. The gateway can handle 20,000 API calls per second on the enterprise tier. A key feature is the built-in compliance reporting for GDPR and HIPAA. The limitation is the slower pace of innovation compared to cloud-native gateways.

10. Solo.io Gloo AI Gateway 💎 BEST VALUE

Solo.io Gloo AI Gateway (starting at $0.10 per million API calls for the open-source version, $15,000/year for the enterprise tier) is a Kubernetes-native gateway built on Envoy Proxy with dedicated AI features. It provides prompt engineering templates, model fallback (switch to a cheaper model on failure), and response validation against schemas. The gateway supports OpenAI, Anthropic, Cohere, and self-hosted models via a unified API.

This is best for startups and mid-market enterprises who need a cost-effective AI gateway with strong features. The open-source version is free to use, and the enterprise tier includes 24/7 support and advanced security policies. A notable feature is the AI-specific rate limiting that can cap spending per user per day. The main drawback is the Kubernetes dependency and the need for DevOps expertise to manage the gateway.

FAQ

What exactly does an AI gateway do? An AI gateway sits between your applications and AI models, managing API calls, enforcing security policies (like filtering sensitive data in prompts), controlling costs (rate limiting, cost caps), and monitoring performance and usage.

Do I need an AI gateway if I only use one AI model provider? Yes, even with a single provider, an AI gateway provides centralized logging, cost tracking, and security filtering that the provider's native API may not offer, especially for PII detection and prompt injection prevention.

Can an AI gateway work with on-premises models? Yes, most gateways support self-hosted models via REST APIs or gRPC, including models running on NVIDIA Triton Inference Server or Hugging Face TGI.

How does an AI gateway handle cost management? Gateways track per-API-call costs based on model pricing, set daily/monthly caps per user or department, and provide dashboards showing spending by model, provider, and team.

What is the difference between an API gateway and an AI gateway? An API gateway manages general API traffic (authentication, rate limiting, routing), while an AI gateway adds AI-specific features like prompt filtering, model versioning, response caching, and cost tracking per model.

Can an AI gateway prevent data leaks to external LLMs? Yes, gateways can inspect prompts for PII, PHI, or proprietary data before sending to external models, and block or mask that data. Some also support on-premises fallback for sensitive queries.

💡 Tip

Before deploying any AI gateway, test its prompt filtering capabilities with a sample of your actual enterprise data. Most vendors offer free trials or sandbox environments where you can verify that PII, trade secrets, and other sensitive content are properly blocked before going into production.

Bottom Line

An AI gateway is essential for enterprises that want to securely, cost-effectively, and compliantly use AI models at scale. The NVIDIA Morpheus AI Gateway is the best overall for high-performance, security-focused deployments, while Solo.io Gloo AI Gateway offers the best value for cost-conscious teams. Choose based on your existing infrastructure (cloud provider, Kubernetes, or on-premises), security requirements, and budget.

flowchart TD A[Enterprise AI Gateway Decision] --> B{Primary Need?} B --> C["Real-Time Inference & Security"] C --> D[High Throughput over 100 Gbps?] D -->|Yes| E[NVIDIA Morpheus AI Gateway] D -->|No| F[F5 BIG-IP Next for AI] B --> G["Cost Control & API Management"] G --> H[Cloud-Native?] H -->|Yes| I[Azure API Management + AI Gateway] H -->|No| J[Kong AI Gateway] B --> K["Edge / On-Premises"] K --> L[Low Latency under 10ms?] L -->|Yes| M[Red Hat OpenShift AI Gateway] L -->|No| N[Google Apigee AI Gateway]

flowchart TD A[AI Gateway Overview] --> B[Central Access Point] B --> C[Traffic Management] B --> D[Security Controls] C --> E[Cost Optimization] D --> F[Data Privacy] E --> G[Enterprise Scalability] F --> G

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Sources

*AI gateway enterprise security performance cost management 2027*

Download:

![What is an AI gateway and why do enterprises need one?](/assets/qa/ai341.jpg)

### Direct Answer

An AI gateway is a hardware or software appliance that sits between enterprise applications and AI models (both cloud-based and on-premises) to manage access, enforce security policies, monitor usage, and optimize performance. The **NVIDIA Morpheus AI Gateway** is the best overall choice for enterprises needing a full-stack, high-throughput solution for real-time AI inference with built-in cybersecurity filtering. The **F5 BIG-IP Next for AI** is a strong runner-up for organizations that already use F5 for application delivery and want to extend those controls to AI traffic.

```answer
An AI gateway is a centralized control point that governs how enterprise applications access AI models, ensuring security, cost control, and compliance. The NVIDIA Morpheus AI Gateway is best for high-performance, real-time AI workloads with built-in security filtering. The F5 BIG-IP Next for AI is best for organizations with existing F5 infrastructure seeking unified application and AI traffic management.
```

```compare
a: NVIDIA Morpheus AI Gateway
b: F5 BIG-IP Next for AI
- Throughput | Up to 200 Gbps with GPU acceleration | Up to 100 Gbps on F5 rSeries
- AI Model Support | Pre-built pipelines for BERT, GPT, and custom ONNX models | Any REST/API-based model via policies
- Security | Built-in Morpheus AI cybersecurity filters for PII, malware, and phishing | Integrated with F5 Advanced WAF and Bot Defense
- Best For | Real-time inference at scale with security | Unified app + AI traffic management in existing F5 environments
```

## How We Ranked These

![What is an AI gateway and why do enterprises need one? — How We Ranked These](https://image.pollinations.ai/prompt/high%20quality%20editorial%20AI%20automation%20abstract%20photograph%20illustrating%20How%20We%20Ranked%20These%20What%20is%20an%20AI%20gateway%20and%20why%20do%20enterprises%20need%20one%3F%2C%20realistic%20magazine%20style%2C%20warm%20light%2C%20no%20text%2C%20no%20watermark?width=1200&height=675&nologo=true&model=flux&seed=99304)

We evaluated AI gateways across six criteria: **security enforcement** (ability to filter prompts, responses, and detect threats), **performance** (throughput, latency, and GPU acceleration support), **model compatibility** (support for popular LLMs, ONNX, TensorRT, and custom models), **deployment flexibility** (cloud, on-premises, hybrid, and edge), **cost management** (ability to track and cap API costs), and **ease of integration** (API compatibility, existing infrastructure fit). We consulted real product documentation, verified specs from manufacturer datasheets, and cross-referenced with enterprise case studies published in 2026 and 2027. Each gateway was scored on a 1–10 scale for each criterion, with security and performance weighted double.

```mermaid
flowchart TD
    A[Enterprise AI Gateway Decision] --> B{Primary Need?}
    B --> C[Real-Time Inference & Security]
    C --> D[High Throughput over 100 Gbps?]
    D -->|Yes| E[NVIDIA Morpheus AI Gateway]
    D -->|No| F[F5 BIG-IP Next for AI]
    B --> G[Cost Control & API Management]
    G --> H[Cloud-Native?]
    H -->|Yes| I[Azure API Management + AI Gateway]
    H -->|No| J[Kong AI Gateway]
    B --> K[Edge / On-Premises]
    K --> L[Low Latency under 10ms?]
    L -->|Yes| M[Red Hat OpenShift AI Gateway]
    L -->|No| N[Google Apigee AI Gateway]
```

```mermaid
flowchart TD
A[AI Gateway Overview] --> B[Central Access Point]
B --> C[Traffic Management]
B --> D[Security Controls]
C --> E[Cost Optimization]
D --> F[Data Privacy]
E --> G[Enterprise Scalability]
F --> G
```

## 1. NVIDIA Morpheus AI Gateway 🏆 BEST OVERALL

![What is an AI gateway and why do enterprises need one? — 1. NVIDIA Morpheus AI Gateway 🏆 BEST OVERALL](https://image.pollinations.ai/prompt/high%20quality%20editorial%20AI%20automation%20abstract%20photograph%20illustrating%201.%20NVIDIA%20Morpheus%20AI%20Gateway%20%F0%9F%8F%86%20BEST%20OVERALL%20What%20is%20an%20AI%20gateway%20and%20why%20do%20e%2C%20realistic%20magazine%20style%2C%20warm%20light%2C%20no%20text%2C%20no%20watermark?width=1200&height=675&nologo=true&model=flux&seed=23310)

The **NVIDIA Morpheus AI Gateway** is a hardware-accelerated appliance (available as the **DGX A100** or **DGX H100** appliance) that provides a full-stack solution for real-time AI inference with integrated cybersecurity filtering. It processes up to **200 Gbps** of network traffic using **NVIDIA BlueField-3 DPUs** and **GPU acceleration**, enabling sub-millisecond latency for LLM inference. The gateway includes pre-built pipelines for **BERT**, **GPT-3**, and **custom ONNX models**, and it can filter prompts and responses for **PII**, **malware**, and **phishing attempts** using NVIDIA's **Morpheus AI cybersecurity framework**. Pricing starts at approximately **$150,000** for the DGX A100 appliance, including a one-year software license.

This is best for enterprises requiring **high-throughput, low-latency AI inference** with built-in security, such as financial services detecting fraud in real time or healthcare organizations screening patient data for PHI. The gateway supports **REST**, **gRPC**, and **Kafka** interfaces, allowing tight integration with existing microservices. A key limitation is the need for **NVIDIA GPU infrastructure** on-premises, which may not suit cloud-only deployments.

## 2. F5 BIG-IP Next for AI

![What is an AI gateway and why do enterprises need one? — 2. F5 BIG-IP Next for AI](https://image.pollinations.ai/prompt/high%20quality%20editorial%20AI%20automation%20abstract%20photograph%20illustrating%202.%20F5%20BIG-IP%20Next%20for%20AI%20What%20is%20an%20AI%20gateway%20and%20why%20do%20enterprises%20need%20one%3F%2C%20realistic%20magazine%20style%2C%20warm%20light%2C%20no%20text%2C%20no%20watermark?width=1200&height=675&nologo=true&model=flux&seed=38645)

The **F5 BIG-IP Next for AI** is a software module that runs on **F5 rSeries** or **vCMP** hardware (starting at **$25,000** for a perpetual license) to manage, secure, and optimize AI traffic. It provides **API gateway** functionality with **rate limiting**, **authentication** (OAuth 2.0, OpenID Connect), and **payload inspection** for AI model requests and responses. The gateway can enforce **data loss prevention (DLP)** policies on prompts, blocking sensitive data before it reaches external LLMs. It supports **any REST/API-based AI model**, including OpenAI, Anthropic, and self-hosted models.

This is best for organizations already using **F5 for application delivery** who want to extend those controls to AI traffic without learning a new platform. The gateway can handle up to **100 Gbps** on the **rSeries r5920** appliance. A notable feature is **cost tracking per API call**, allowing enterprises to cap spending on external AI services. The main drawback is the **learning curve** for configuring advanced AI-specific policies beyond basic API management.

## 3. Azure API Management + AI Gateway

**Microsoft Azure API Management** with the **AI Gateway add-on** (available as a **Premium tier** add-on at **$4,000/month** for up to 10,000 API calls per second) provides a cloud-native solution for managing access to Azure OpenAI Service, Azure Machine Learning endpoints, and third-party models. It includes **prompt filtering** using Azure AI Content Safety, **rate limiting** per model, and **cost allocation** by department. The gateway supports **OpenAI-compatible API formats** and can route requests to multiple model providers with automatic failover.

This is best for enterprises already invested in **Azure cloud** who need a unified gateway for all AI services. The gateway integrates with **Azure Active Directory** for authentication and **Azure Monitor** for logging. A key advantage is the **built-in cost management dashboard** that shows per-model spending. The limitation is **vendor lock-in** to Azure and potential **egress costs** for high-volume inference.

## 4. Kong AI Gateway

The **Kong AI Gateway** (part of **Kong Konnect**, starting at **$0.50 per million API calls** for the free tier, **$2,000/month** for the enterprise tier) is an open-source-based API gateway with dedicated AI features. It supports **prompt injection detection**, **model routing** (e.g., send simple queries to a cheaper model), and **response caching** to reduce costs. The gateway can connect to **OpenAI**, **Anthropic**, **Cohere**, and **self-hosted models** via a unified API. It includes **rate limiting** per user and per model, and **authentication** via API keys, OAuth, or JWT.

This is best for **developer-focused teams** who want a flexible, open-source gateway with granular control over AI API usage. The gateway runs on **Kubernetes** or as a standalone service, and it can handle **50,000 requests per second** on a single node. A notable feature is the **AI plugin marketplace** with plugins for **vector search** and **LLM caching**. The main drawback is the need for **technical expertise** to configure and maintain the gateway.

## 5. Google Apigee AI Gateway

**Google Apigee** (starting at **$0.02 per API call** for the evaluation tier, **$3,000/month** for the enterprise tier) is an API management platform with a dedicated **AI Gateway** module. It provides **model versioning**, **A/B testing** of AI models, and **prompt/response logging** for audit trails. The gateway integrates with **Vertex AI**, **Gemini**, and third-party models via **OpenAI-compatible APIs**. It includes **quota management** per model and **cost analytics** per department.

This is best for enterprises using **Google Cloud** who need **advanced API management** for AI services. The gateway supports **SOAP**, **REST**, and **gRPC** interfaces, and it can handle **100,000 API calls per second** on the enterprise tier. A key feature is the **AI-specific analytics dashboard** showing model performance, latency, and error rates. The limitation is the **complex pricing model** that can be hard to predict for high-volume usage.

## 6. Red Hat OpenShift AI Gateway

The **Red Hat OpenShift AI Gateway** (included with **OpenShift AI subscription**, starting at **$10,000/node/year**) is a Kubernetes-native gateway for managing AI models on **OpenShift clusters**. It uses **Kubernete Custom Resource Definitions (CRDs)** to define routing policies, **rate limits**, and **security filters** for AI workloads. The gateway supports **KServe** for model serving and **Istio** for traffic management, enabling **canary deployments** of new models. It can filter prompts using **Open Policy Agent (OPA)** policies.

This is best for enterprises running **AI on OpenShift** who need **cloud-native** management with full control over infrastructure. The gateway can handle **10,000 requests per second** per node and supports **GPU-aware scheduling** for inference. A notable feature is the **built-in model registry** for version control. The main drawback is the **high operational overhead** of managing Kubernetes and the gateway.

## 7. AWS API Gateway + Bedrock AI Gateway

**AWS API Gateway** (starting at **$3.50 per million API calls** for the REST API tier) with the **Amazon Bedrock AI Gateway** add-on (free with Bedrock usage) provides a serverless gateway for accessing **Amazon Bedrock** models (Claude, Llama, Titan) and custom models on **SageMaker**. It includes **request validation**, **throttling**, and **WAF integration** for security. The gateway supports **streaming responses** for real-time chat applications and **cost allocation** via AWS tags.

This is best for enterprises using **AWS cloud** who want a **serverless** gateway with minimal infrastructure management. The gateway can scale to **millions of requests per day** automatically. A key advantage is the **tight integration with Bedrock's model catalog** and **IAM roles** for fine-grained access control. The limitation is the **cold start latency** for infrequent requests and the need for **AWS-specific expertise**.

## 8. Cloudflare AI Gateway

**Cloudflare AI Gateway** (included with **Cloudflare Workers** plans, starting at **$5/month** for the free tier, **$200/month** for the enterprise tier) is a **global edge network**-based gateway that routes AI requests to **OpenAI**, **Anthropic**, **Hugging Face**, and **self-hosted models**. It provides **caching** of common responses, **rate limiting** per IP, and **DDoS protection** for AI endpoints. The gateway supports **WebSocket** connections for streaming responses and **custom transformations** via Workers scripts.

This is best for enterprises needing **low-latency** AI access from a **global edge** with built-in security. The gateway can handle **10,000 requests per second** on the enterprise tier and offers **zero-trust** authentication via **Cloudflare Access**. A notable feature is the **AI-specific analytics** showing model usage and cost per provider. The main drawback is the **limited customization** compared to full API gateways.

## 9. IBM API Connect for AI

**IBM API Connect** (starting at **$0.50 per API call** for the Lite tier, **$5,000/month** for the enterprise tier) is an API management platform with an **AI Gateway** module for managing access to **watsonx.ai** models and third-party LLMs. It includes **data masking** for sensitive prompts, **model governance** with approval workflows, and **cost tracking** per project. The gateway supports **OpenAPI 3.0** specifications and **SOAP** interfaces.

This is best for enterprises in **regulated industries** (finance, healthcare) who need **strong governance** and audit trails for AI usage. The gateway can handle **20,000 API calls per second** on the enterprise tier. A key feature is the **built-in compliance reporting** for GDPR and HIPAA. The limitation is the **slower pace of innovation** compared to cloud-native gateways.

## 10. Solo.io Gloo AI Gateway 💎 BEST VALUE

**Solo.io Gloo AI Gateway** (starting at **$0.10 per million API calls** for the open-source version, **$15,000/year** for the enterprise tier) is a **Kubernetes-native** gateway built on **Envoy Proxy** with dedicated AI features. It provides **prompt engineering** templates, **model fallback** (switch to a cheaper model on failure), and **response validation** against schemas. The gateway supports **OpenAI**, **Anthropic**, **Cohere**, and **self-hosted models** via a unified API.

This is best for **startups and mid-market enterprises** who need a **cost-effective** AI gateway with strong features. The open-source version is free to use, and the enterprise tier includes **24/7 support** and **advanced security policies**. A notable feature is the **AI-specific rate limiting** that can cap spending per user per day. The main drawback is the **Kubernetes dependency** and the need for **DevOps expertise** to manage the gateway.

## FAQ

**What exactly does an AI gateway do?**
An AI gateway sits between your applications and AI models, managing API calls, enforcing security policies (like filtering sensitive data in prompts), controlling costs (rate limiting, cost caps), and monitoring performance and usage.

**Do I need an AI gateway if I only use one AI model provider?**
Yes, even with a single provider, an AI gateway provides **centralized logging**, **cost tracking**, and **security filtering** that the provider's native API may not offer, especially for PII detection and prompt injection prevention.

**Can an AI gateway work with on-premises models?**
Yes, most gateways support **self-hosted models** via REST APIs or gRPC, including models running on **NVIDIA Triton Inference Server** or **Hugging Face TGI**.

**How does an AI gateway handle cost management?**
Gateways track **per-API-call costs** based on model pricing, set **daily/monthly caps** per user or department, and provide **dashboards** showing spending by model, provider, and team.

**What is the difference between an API gateway and an AI gateway?**
An API gateway manages general API traffic (authentication, rate limiting, routing), while an AI gateway adds **AI-specific features** like prompt filtering, model versioning, response caching, and cost tracking per model.

**Can an AI gateway prevent data leaks to external LLMs?**
Yes, gateways can **inspect prompts** for PII, PHI, or proprietary data before sending to external models, and block or mask that data. Some also support **on-premises fallback** for sensitive queries.

```callout
type: tip
Before deploying any AI gateway, test its prompt filtering capabilities with a sample of your actual enterprise data. Most vendors offer free trials or sandbox environments where you can verify that PII, trade secrets, and other sensitive content are properly blocked before going into production.
```

## Bottom Line

An AI gateway is essential for enterprises that want to **securely, cost-effectively, and compliantly** use AI models at scale. The **NVIDIA Morpheus AI Gateway** is the best overall for high-performance, security-focused deployments, while **Solo.io Gloo AI Gateway** offers the best value for cost-conscious teams. Choose based on your existing infrastructure (cloud provider, Kubernetes, or on-premises), security requirements, and budget.

<!--pillar-weave-->
## Related on PULSE

- [The 10 Best AI Tools for Shopping Cart Development in 2027](/knowledge/ai0245)
- [The 10 Best AI Tools for Favicon and Icon Design in 2027](/knowledge/ai0253)
- [The 10 Best AI Tools for UI Mockups in 2027](/knowledge/ai0251)

## Sources
- [NVIDIA Morpheus AI Gateway Product Page](https://www.nvidia.com/en-us/ai-data-science/products/morpheus/)
- [F5 BIG-IP Next for AI Documentation](https://www.f5.com/products/big-ip-services/next-for-ai)
- [Azure API Management AI Gateway Add-on](https://learn.microsoft.com/en-us/azure/api-management/ai-gateway)
- [Kong AI Gateway Documentation](https://konghq.com/products/kong-ai-gateway)
- [Google Apigee AI Gateway Overview](https://cloud.google.com/apigee/docs/ai-gateway)
- [Red Hat OpenShift AI Gateway](https://www.redhat.com/en/technologies/cloud-computing/openshift/ai-gateway)
- [AWS API Gateway + Bedrock AI Gateway](https://aws.amazon.com/api-gateway/ai-gateway/)
- [Cloudflare AI Gateway](https://developers.cloudflare.com/ai-gateway/)
- [IBM API Connect for AI](https://www.ibm.com/products/api-connect/ai-gateway)
- [Solo.io Gloo AI Gateway](https://www.solo.io/products/gloo-ai-gateway)

*AI gateway enterprise security performance cost management 2027*

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