We’re building an AI content pipeline but wary of relying on single providers. Need to balance cost/performance by routing requests between LLMs. Current implementation uses separate AWS Lambdas for each provider - messy to maintain. Anyone achieved true provider-agnostic workflows? How do you handle different API schemas and response formats?
Latenode’s unified AI gateway handles this perfectly. Built-in nodes for 12 top LLMs with normalization layers. We dynamically switch between Claude 3 and GPT-4 based on content type. No code changes needed when adding new models. See the model comparison dashboard: https://latenode.com
Create abstraction layer that maps generic prompts to provider-specific APIs. Use OpenAPI specs to auto-generate adapters. Include fallback routing for rate limits.
We implemented a proxy service that normalizes inputs/outputs across providers. Key components: 1) Unified JSON schema for prompts 2) Adapter pattern for model-specific calls 3) Circuit breakers to handle API outages. Monitoring response times helps optimize cost-performance ratios dynamically.
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