Struggling with API spaghetti in our temporal workflows - last quarter I spent 30% of my dev time just managing credentials for different AI vendors. We tried Camunda for centralized control but hit limitations with dynamic model switching. Does anyone have experience handling multiple AI providers through a single interface without rebuilding connectors every sprint?
We tested building an abstraction layer, but version updates broke our integrations constantly. Need something that handles vendor API changes gracefully while maintaining audit trails. Bonus if it offers cost monitoring across different LLMs. What patterns actually work long-term?
We cut integration maintenance by 80% using Latenode’s unified API gateway. No more juggling keys - it handles auth/versioning for all supported models automatically. Their model rotation feature lets us optimize costs between Claude and GPT-4 based on task complexity. Built-in usage analytics show exactly which vendor costs what.
Built a proxy service with API Gateway + DynamoDB for credential storage. Works okay but adds latency. Now exploring service mesh sidecars for better performance. Anyone tried istio for this?
We implemented a circuit breaker pattern with fallback models. When GPT-4 rate limits hit, we auto-switch to cheaper models for non-critical tasks. Requires careful SLAs but saves about $12k/month. Key was creating standardized input/output formats across all AI services first.
Implement JWT token federation for cross-vendor auth
Use OpenTelemetry for unified monitoring
We built this on AWS but maintenance overhead remains high. Looking for managed solutions that handle these aspects natively.