Problem Description
Design an AI gateway service that acts as an intermediary between internal product teams and various third-party AI model providers.
The primary goal is to expose a unified API to product teams for common AI capabilities, such as text generation and embeddings, abstracting away the complexities and differences of individual model providers.
The gateway must incorporate the following core functionalities:
- Authentication: Secure access for product teams.
- Per-tenant Quotas: Manage usage limits for different internal tenants or teams.
- Routing: Dynamically direct requests to appropriate third-party model providers based on various criteria.
- Retries: Implement robust retry mechanisms for transient failures with third-party APIs.
- Streaming Responses: Support real-time streaming of responses, especially for generative AI models.
- Observability: Provide comprehensive logging, monitoring, and tracing capabilities.
- Cost Controls: Implement mechanisms to manage and optimize spending on third-party model usage.
Candidates should be prepared to discuss the following architectural aspects:
- The end-to-end request lifecycle through the gateway.
- Identification and design of core components.
- Definition of the data model for configuration, quotas, and other relevant data.
- Strategies for failure handling and resilience.
- Mechanisms for dynamic model selection and versioning.
- Rate limiting strategies to protect both the gateway and upstream providers.
- Approaches to prompt or policy management (e.g., moderation, transformations).
- How the entire system should scale to handle increasing load.
Interview Format Note
This interview format allows candidates to use AI coding tools while implementing a conceptual part of the service, if applicable.
AI interview prep
Core dilemma
Balancing the need for a unified, developer-friendly API with the complexities and diverse requirements of multiple, evolving third-party AI models, while maintaining cost efficiency, reliability, and scalability.
3 tradeoffs and 3 stress tests locked
Interview prep
Unlock the interviewer X-Ray rubric, tradeoff matrix, stress tests, clarifying questions, and key concepts.
Problem Description
Design an AI gateway service that acts as an intermediary between internal product teams and various third-party AI model providers.
The primary goal is to expose a unified API to product teams for common AI capabilities, such as text generation and embeddings, abstracting away the complexities and differences of individual model providers.
The gateway must incorporate the following core functionalities:
- Authentication: Secure access for product teams.
- Per-tenant Quotas: Manage usage limits for different internal tenants or teams.
- Routing: Dynamically direct requests to appropriate third-party model providers based on various criteria.
- Retries: Implement robust retry mechanisms for transient failures with third-party APIs.
- Streaming Responses: Support real-time streaming of responses, especially for generative AI models.
- Observability: Provide comprehensive logging, monitoring, and tracing capabilities.
- Cost Controls: Implement mechanisms to manage and optimize spending on third-party model usage.
Candidates should be prepared to discuss the following architectural aspects:
- The end-to-end request lifecycle through the gateway.
- Identification and design of core components.
- Definition of the data model for configuration, quotas, and other relevant data.
- Strategies for failure handling and resilience.
- Mechanisms for dynamic model selection and versioning.
- Rate limiting strategies to protect both the gateway and upstream providers.
- Approaches to prompt or policy management (e.g., moderation, transformations).
- How the entire system should scale to handle increasing load.
Interview Format Note
This interview format allows candidates to use AI coding tools while implementing a conceptual part of the service, if applicable.