The rapid pace of AI advances demands experimentation-driven approaches for organizations to remain at the forefront of the industry. With AI steadily becoming a game-changer for various sectors, maintaining a fast-paced innovation trajectory is crucial for businesses aiming to leverage its full potential.
AI services are predominantly accessed via APIs, highlighting the essential need for a robust and efficient API management strategy. This strategy is crucial for maintaining control and governance over the consumption of AI services, ensuring their reliable and scalable deployment.
Bridging the Gap: From Experimentation to Production
Many companies are currently in the experimentation phase with LLM APIs, recognizing their transformative potential. However, a significant gap exists between this experimentation phase and the willingness to move these APIs into production. This gap is often due to the complexities of managing and scaling AI services, ensuring reliability, and maintaining performance under varying loads. Organizations need a robust framework to confidently transition from experimentation to full-scale production.
To address these challenges, the concept of an AI Gateway has emerged. This comprehensive solution extends the core principles of API management, aiming to accelerate the experimentation of advanced use cases and pave the road for further innovation in this rapidly evolving field. The well-architected principles of the AI Gateway provide a framework for the confident deployment of intelligent applications into production. It ensures that AI services are reliable, scalable, and manageable, even under heavy usage and potential downtimes.
The AI Gateway framework features layers, each serving as the foundation for the next in a pyramid manner:
Foundational architecture
1. “Foundational Architecture” – The Infrastructure of an AI Gateway.
2. “Building Blocks” – The Core Capabilities of an AI Gateway.
3. “Gateway Operations” – Advanced implementations to address aspects of reliability, scalability, cost, and security.
‘Foundational Architecture’ – The Infrastructure of an AI Gateway
Integrating an AI Gateway within your infrastructure requires a unique approach, as it acts as the crucial layer managing all LLM API calls and responses. Unlike traditional ingress gateways placed in front of your infrastructure to handle incoming traffic, AI Gateways are strategically positioned close to your applications. This placement reduces latency and ensures efficient traffic capture between your infrastructure and third-party providers.
The infrastructure of an AI Gateway must facilitate real-time controls and governance over API traffic. This approach involves routing all outbound traffic from your applications to external APIs through the gateway. AI Gateways use advanced protocol- and application-aware proxy mechanisms, operating on the server side to manage API or service communication effectively. Additionally, AI Gateways empower API consumers by providing visibility and control over their LLM API usage. This innovative solution extends beyond the capabilities of traditional API gateways by focusing on the nuanced requirements of managing LLM API consumption. By tunneling and optimizing external traffic, AI Gateways enable organizations to maintain robust governance, ensure seamless integration, and enhance the overall performance of their AI-driven applications.
The Key Considerations for Building an AI Gateway
Infrastructure:
1. Selective API Traffic Tunneling: Efficiently route API traffic from multiple applications in a distributed environment through the AI Gateway. This ensures only relevant traffic is managed, optimizing performance and resource use.
2. Handling HTTPS Traffic: Managing egress traffic encrypted with HTTPS protocols requires specialized tools and protocols to observe, manipulate, or tunnel the traffic securely. This ensures data integrity and performance without compromising security.
3. Minimizing Latency: The AI Gateway must be designed to have as low a latency impact as possible, ensuring seamless application performance and user experience.
4. Scalable Gateway Clusters: Implement multiple AI Gateways across applications and environments, necessitating a scalable infrastructure. This approach distributes traffic loads and ensures high availability and reliability.
5. Extensibility of the Gateway: Add management and optimization policies to ensure the AI Gateway can adapt to evolving needs. This flexibility is crucial for addressing the dynamic requirements of LLM API management and diverse use cases.
‘Building Blocks’ — The Core Capabilities of an AI Gateway
The Building Blocks Layer represents the core capabilities of an AI Gateway, which are essential for controlling, regulating, and shaping LLM API traffic according to tailored business logic. This layer encompasses several critical functionalities that ensure AI service’s efficient and reliable operation in production environments.
1. Logging API Calls: Logging all LLM API calls before launching into production is crucial for confidence in the system and fast debugging. Since prompts and responses can be large, traditional logging can become costly. Consider removing text pieces before logging or using a specialized logging system to manage costs while maintaining insights into request responses and tracking token usage.
2. Request Forwarding: This capability allows the AI Gateway to forward API calls to specified LLM APIs, enabling model switching based on defined triggers or thresholds. By dynamically selecting the most appropriate model for each task, this ensures optimal performance and cost-efficiency.
3. Tagging API Calls and Responses: Adding headers to API interactions enables granular control based on tenant, user, application, and environment. This allows for precise traffic management, prioritization, and policy enforcement tailored to different segments of the user base.
4. Modifying Requests and Responses: The ability to modify requests and responses allows for optimization and security enhancements. The gateway can reduce costs and address security concerns by altering prompts, ensuring API calls are efficient and aligned with business objectives.
5. Circuit Breaker Functionality: This capability handles API provider rate limits and unexpected behavior, maintaining system stability and reliability. Circuit breakers prevent system overloads and ensure robustness even when external APIs encounter issues.
6. Collecting Metrics: Gain visibility by collecting and aggregating metrics from API calls and responses, including payloads, enabling offline analysis. This helps detect usage patterns, predict trends, and identify anomalies, providing valuable insights for continuous improvement and optimization.
7. Tokenization: Keeping real-time track and control over tokens used in LLM models is vital. Tokenization ensures that token usage is monitored and managed efficiently, preventing overuse and optimizing resource allocation. By tracking tokens in real-time, the AI Gateway can enforce usage limits, provide detailed usage reports, and adjust traffic flows to align with business policies and budget constraints.
‘Gateway Operations’
The Gateway Operations layer represents the advanced implementations that ensure the AI Gateway operates reliably, scales efficiently, manages costs, and maintains security. This layer integrates multiple building blocks and advanced capabilities to streamline complex AI handling operations. We can categorize these operations into four main areas: cost, reliability, security, and scalability.
Cost Management
1. Controlling Prompt Size:
● Measure: Start by logging, measuring, and analyzing the lengths of prompts in different scenarios.
● Budget: Create a token budget per request based on AI features’ business value and usage frequency.
● Improve: Rewrite prompts for brevity to stay within budget.
● Truncate: Implement safeguards to automatically truncate longer prompts, prioritizing less crucial parts to truncate.
2. User-Level Limits:
● Introduce user-level rate limits to prevent cost overruns, especially since AI API costs scale with the number of requests and tokens used per request. ● Set Limits: Measure consumption at the 95th percentile of users and set limits above standard usage patterns.
● Compare Costs: Ensure financial viability by comparing API costs at limits to users’ lifetime value (LTV).
3. Semantic Caching: Implement caching mechanisms to store and reuse responses for similar or repeated requests, reducing redundant API calls and associated costs.
4. Additional AI Operations can include:
● Prompt model routing
Reliability
1. LLM API Fallbacks:
● Multiple Deployments, Same Model: Use the same model deployed by different providers (e.g., OpenAI and Azure OpenAI) for consistent outputs.
● Different Models: Use different models as fallbacks if proprietary models lack multiple deployments, periodically adjusting prompts and testing fallbacks.
2. System Response Filter: Filter out common system messages from LLMs to prevent them from reaching user interfaces. Adjust prompts to avoid these messages and use string filtering to remove common system responses.
Safety
1. Prompt Abuse Filtering: Check prompts for potential abuse and restructure them to prevent user misuse.
2. PII Removal: Remove personally identifiable information (PII) from prompts and API call payloads to ensure data privacy and compliance with regulations. 3. Additional AI Operations can include:
a. Prompt Guarding
b. Content Filtering
Scalability
1. Cache Repetitive API Calls: Implement caching to store responses for repeated requests, such as standard inputs or actions shared among users, to reduce redundant processing and associated costs.
2. Prepare for Rate Limits:
● Load Balance: Distribute API requests across multiple endpoints and deployments to stay below rate limits, ensuring continuous service availability.
● Monitor Limits: Track and manage API usage to avoid hitting rate limits, ensuring smooth operation as the application scales.
3. Additional AI Operations can include:
● Load balancing between OpenAI endpoints
The Gateway Operations layer of an AI Gateway integrates these advanced capabilities to create a robust, scalable, cost-effective, and secure infrastructure for managing LLM API traffic. By implementing these strategies, businesses can ensure their AI services operate efficiently and reliably in production environments.
Putting the AI Gateway into Practice
Here’s a real example of a complex AI Gateway operation:
This AI operation flow is helpful if your app offers AI-based functionality using the OpenAI API and you want to limit the number of requests an individual user can make.
1. Rewrite Request — Copies the value from the header X-On-Behalf-Of to the header X-Lunar-User-Key, with a fallback value if X-On-Behalf-Of is undefined. It will provide a fallback for services that don’t send an OBO header.
2. Count Tokenized Value —Tokenizes the value in req.body.input and counts the number of tokens, placing the result in the request header
X-Lunar-Request-Tokens. This will determine the token usage for the current request.
3. Get Quota Usage — This function fetches the quota usage statistic for the last day for the user based on the determined header. It retrieves the user’s API usage to check against rate limits.
4. Check Rate Limit — Checks the rate limit against a provided limit (10,000 tokens) and the fetched usage statistic. It excludes any requests with the “system” header.
If the check passes, the request continues to the API; if it fails, it is redirected to a different stream.
5. Count Tokenized Value (Response) — Tokenizes the value in res.body.input and counts the number of tokens, placing the result in the response header X-Lunar-Request-Tokens.
6. Update Quota Usage — Updates the quota usage statistic based on the counted tokens in both the request and response to maintain an accurate record of the user’s token usage for future rate limit checks.
To Conclude
The AI Gateway is essential for managing the rapid pace of AI advancements and transitioning from experimentation to full-scale production. Businesses can efficiently handle LLM API traffic by leveraging a robust framework divided into Infrastructure, Building Blocks, and Gateway Operations layers. This comprehensive solution allows organizations to control costs, enhance performance, and maintain strong governance, driving continuous innovation and successful AI integration into production environments.
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The AI Gateway is essential for managing the rapid pace of AI advancements and transitioning from experimentation to full-scale production.