Design an AI Gateway
Design an AI gateway that acts as an intermediary between internal services and multiple Large Language Model (LLM) providers, such as OpenAI and Azure OpenAI.
The gateway must expose a unified interface to internal applications and be responsible for intelligent provider routing. Two primary requirements are:
- Dynamic Fallback: Implement logic to decide when to fall back from a primary LLM provider to an alternative.
- Observability & Analytics: Produce comprehensive logging and aggregated analytics suitable for business stakeholders.
Your discussion should cover:
- The high-level architecture and major components of the gateway.
- How requests are initially routed to a primary provider and the specific conditions that trigger a fallback to a secondary provider.
- The online metrics that should drive fallback decisions, including but not limited to latency, error rates, provider-specific rate limits, quality or schema-validation failures of responses, and cost considerations.
- Mechanisms to support resilience patterns such as retries, circuit breakers, quotas for internal services, and continuous provider health checks.
- What data should be logged for each individual request and what aggregated metrics should be exposed to the business team for strategic insights.
- Strategies to protect sensitive prompt and response data while still maintaining useful observability and debugging capabilities.
- Considerations for scalability, reliability, and the inherent trade-offs involved in your design choices.
AI interview prep
Core dilemma
Balancing the need for high availability, performance, and cost-efficiency for internal services against the inherent unreliability, heterogeneity, and cost variability of external LLM providers, all while maintaining strict data privacy and comprehensive observability.
3 tradeoffs and 3 stress tests locked
Interview prep
Unlock the interviewer X-Ray rubric, tradeoff matrix, stress tests, clarifying questions, and key concepts.
Design an AI Gateway
Design an AI gateway that acts as an intermediary between internal services and multiple Large Language Model (LLM) providers, such as OpenAI and Azure OpenAI.
The gateway must expose a unified interface to internal applications and be responsible for intelligent provider routing. Two primary requirements are:
- Dynamic Fallback: Implement logic to decide when to fall back from a primary LLM provider to an alternative.
- Observability & Analytics: Produce comprehensive logging and aggregated analytics suitable for business stakeholders.
Your discussion should cover:
- The high-level architecture and major components of the gateway.
- How requests are initially routed to a primary provider and the specific conditions that trigger a fallback to a secondary provider.
- The online metrics that should drive fallback decisions, including but not limited to latency, error rates, provider-specific rate limits, quality or schema-validation failures of responses, and cost considerations.
- Mechanisms to support resilience patterns such as retries, circuit breakers, quotas for internal services, and continuous provider health checks.
- What data should be logged for each individual request and what aggregated metrics should be exposed to the business team for strategic insights.
- Strategies to protect sensitive prompt and response data while still maintaining useful observability and debugging capabilities.
- Considerations for scalability, reliability, and the inherent trade-offs involved in your design choices.