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May 23, 2024 - last updated
GenAI For Practitioners

AI Gateway: What is it & 5 AI Gateways you should know

Maor Michael
Maor Michael
8 min read Mar 28, 2024

The AI landscape is booming, with powerful models and new use cases emerging daily. However, harnessing their potential securely and efficiently poses a challenge. Enter the AI gateway, a key infrastructure acting as the gatekeeper between AI models and real-world applications. 

In this blog, we’ll cover everything you need to know about AI Gateway

What is AI Gateway? 

An AI Gateway acts as a central hub for integrating AI services into applications. It ensures smooth, efficient operations and cost-effectiveness. This intermediary facilitates communication between applications and AI services, like OpenAI. It manages tasks such as usage tracking, performance caching, and cost control through rate limiting.

AI applications introduce specific challenges:

  • Volume and complexity: They generate vast amounts of data in complex formats (images, audio, text), straining communication channels.
  • Model diversity: AI models vary greatly, requiring flexible management.
  • Security: Strong security measures are vital for protecting data and ensuring ethical AI use.

The AI Gateway addresses these challenges, distinct from traditional systems. It provides a tailored solution for the efficient and secure integration of AI into applications.

The power of AI Gateway

The limitations of traditional API Gateways for AI traffic pave the way for the power and potential of the AI Gateway. Here’s an example of how this innovative solution empowers organizations to navigate the complexities of AI:

AI Gateway

Image source

Standardized interface

Imagine having a single point of access for interacting with a multitude of AI models. This is precisely what an AI Gateway offers. An AI gateway will allow developers to access whatever AI model they require using a single API interface, while supporting numerous AI backends such as LLaMA, OpenAI, Anthropic, Mistral, and more. 

API interface

Image source 

This simplifies integration and streamlines the development process, enabling developers to focus on building innovative AI applications rather than wrestling with complex model-specific protocols.

Enhanced security

Security is central when dealing with AI. An AI Gateway acts as a robust guardian, implementing essential security features to protect your organization’s valuable data and ensure responsible AI usage. These features may include:

  • Authentication (AuthN): Verifying the identity of applications attempting to access AI models, ensuring only authorized applications can interact with them.
  • Authorization (AuthZ): Controlling the level of access granted to authorized applications, determining what actions they can perform on specific AI models.
  • Rate Limiting: Preventing excessive requests from overwhelming AI models and maintaining optimal performance.
  • API Lifecycle Governance: Providing a framework for managing the entire lifecycle of AI APIs, from creation and deployment to monitoring and version control. This ensures responsible development and use of AI resources.

Simplified management

The centralized nature of the AI Gateway significantly simplifies the overall management of AI traffic. Developers no longer need to juggle individual connections with various AI models. The Gateway takes care of routing requests, enforcing security policies, and providing valuable insights into AI model usage. This translates to reduced complexity for developers and IT teams, allowing them to focus on more strategic initiatives.

Comparison with API Gateway

While API Gateways have long been the backbone of modern software architecture, ensuring secure and efficient communication between services, AI Gateways bring a new dimension to the table by focusing specifically on AI consumption.

Similarities

Both API Gateways and AI Gateways act as intermediaries, abstracting the underlying complexities of connecting the systems they interact with. They provide a layer of standardization that simplifies interactions and enhances security.

Differences

  • Focus: The main functions of API gateways are to control API traffic and provide logging, rate limitation, and authentication regulations. On the other hand, AI Gateways are designed to manage duties related to AI, like monitoring, inference, and model deployment.
  • Functionality: While API Gateways are excellent at transferring and changing data between services, AI Gateways are made to simplify the method in which AI models are consumed, hence facilitating the simpler integration of AI features into applications by developers.
  • Integration: API Gateways are essential for service-to-service connectivity, guaranteeing smooth exchanges between various system components. Conversely, by abstracting the difficulties of AI model deployment and management, AI Gateways make it easier to integrate AI services into applications.

Use cases of AI Gateway

AI gateways find applications across various industries and domains, enabling organizations to leverage AI in diverse ways. Some common use cases include:

  • Healthcare: AI gateways streamline the integration of advanced medical imaging tech, enhancing the accuracy and speed of X-ray and MRI analysis for healthcare professionals. For instance, the University of California, San Francisco leverages an AI Gateway to optimize AI models, elevating healthcare standards and predicting patient prognoses effectively.
  • Manufacturing: By enabling proactive equipment maintenance and reducing downtime, AI gateways in the manufacturing sector implement predictive maintenance solutions. For instance, a well-known global company, Siemens, uses an AI Gateway to efficiently manage and use AI models to reduce operational downtime and optimize production procedures.
  • Retail: Personalized recommendation engines are used by retailers using AI gateways to increase consumer interaction and boost revenues. A massive retailer, Walmart uses an AI gateway to manage and implement AI models to improve supply chain management efficiency and optimize pricing tactics.
  • Finance: Using AI gateways, financial organizations can install detection systems that instantly detect and stop fraudulent transactions. An AI Gateway is used by JPMorgan Chase, a well-known financial organization, to manage and implement AI models that are essential for fraud detection and investment strategy optimization.

The benefits of AI Gateway

AI gateways offer many benefits to organizations looking to integrate AI into their operations. Some key advantages include:

Enhanced efficiency: AI gateways enable businesses to cut down on time-to-market for AI initiatives and enhance operational efficiency by simplifying the deployment and maintenance of AI applications.

Scalability: AI gateways let companies easily expand their AI infrastructure, giving them the capacity to manage increasing data volumes and processing demands efficiently.

Cost savings: AI gateways help enterprises cut expenses related to AI implementation and maintenance costs by maximizing resource usage and lowering the need for manual intervention.

Better security: Access control and encryption are two security features that AI gateways have, which assist businesses in safeguarding critical information and guarantee that security regulations are followed.

Enhanced innovation: AI gateways enable enterprises to create and investigate new applications for AI across several industries by offering a platform to integrate AI capabilities smoothly.

Exploring 5 AI Gateways

With a clearer understanding of AI gateways and their value, we now shift gears to explore five prominent platforms leading the charge:

1. AI Gateway for Cloudflare: Cloudflare AI Gateway prioritizes model protection and performance with encryption, access controls, and load balancing. It boasts industry-leading security features and a worldwide reach. It performs well in situations with significant traffic that call for scalability and strong security.

2. Portkey: As an open-source champion of artificial intelligence, Portkey offers flexibility and customization for on-premises or cloud deployments. Its focus on governance and explainability enables organizations to implement responsible AI practices and understand model decision-making.

3. MLFlow Deployment Server: If your workflow heavily relies on MLflow, this server-based gateway seamlessly integrates with your existing infrastructure. It’s ideal for managing, deploying, and tracking MLflow models within your established MLflow-centric environment.

4. LiteLLM: Compact and efficient, LiteLLM shines on edge devices with limited resources. Its lightweight design caters to applications like real-time inference and remote sensing, where minimal latency and low hardware footprint are critical.

5. Wealthsimple LLM Gateway: This cloud-based gateway offers access to a curated selection of pre-trained language models, simplifying integration for tasks like text generation, summarization, and translation. Wealthsimple LLM Gateway is perfect for developers or businesses seeking easy access to high-quality language models without extensive setup.

Remember: Each gateway has strengths and weaknesses, pricing structures, and community support. Carefully evaluate your specific needs and priorities before making your choice.

Safeguarding AI with Guardrails

While AI gateways unlock tremendous potential, AI risk management concerns remain. This is where Aporia Guardrails steps in – compatible with any AI Gateway. Using Guardrails, organizations ensure reliable and goal-oriented AI chatbot interactions, protecting user experience and brand reputation. 

Say goodbye to AI risks:

Aporia empowers you to go beyond basic security with complete Ai control, fortifying your defenses and ensuring transparent, secure, and responsible AI performance.

Learn more about Aporia Guardrails – Book your demo today!

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