How to Monitor LLMs in Production
Most teams log LLM calls and call it monitoring. Real production monitoring needs logs, metrics, traces, route decisions, retries, approvals, and cost visibility.
Read more →Latest engineering updates and tutorials
Most teams log LLM calls and call it monitoring. Real production monitoring needs logs, metrics, traces, route decisions, retries, approvals, and cost visibility.
Read more →Step-by-step guide to building a RAG pipeline. Covers ingestion, chunking, embeddings, vector search, and LLM generation with real trade-offs.
Read more →AWS Bedrock gives you managed AI models inside the AWS ecosystem. Sinapsis AI gives you the same capabilities without the lock-in. A practical comparison.
Read more →Vercel AI SDK gives you building blocks. Sinapsis AI gives you production workflow APIs, routing, traces, approvals, and cost visibility.
Read more →n8n added AI to a workflow automation tool. Sinapsis AI was built for AI from the ground up. Why that distinction matters in production.
Read more →Flowise excels at visual LLM prototyping. Sinapsis AI adds production workflow APIs, routing, traces, approvals, and cost visibility.
Read more →Dify builds great AI workflows. Sinapsis AI focuses on production workflow APIs, model routing, traces, approvals, and cost visibility.
Read more →Replicate hosts models. Sinapsis AI turns model calls into production workflows with APIs, routing, traces, approvals, and cost visibility.
Read more →Langfuse and Helicone track model calls. Sinapsis AI adds workflow APIs, routing, traces, approvals, retries, and cost visibility around production AI execution.
Read more →LangChain is a framework for LLM apps. Sinapsis AI turns AI workflows into production APIs with routing, traces, approvals, and cost visibility.
Read more →OpenAI API or on-premise Llama? The answer depends on your use case, compliance requirements, and scale. Here is a practical framework for deciding.
Read more →Most teams overspend on AI by 40-70%. Here are the proven strategies to cut costs without sacrificing quality, from model selection to intelligent routing.
Read more →Guide to building AI workflows that chain multiple models with conditional logic. Real examples for support, content, and document intelligence.
Read more →LLM observability tracks model calls. Production AI also needs workflow traces, route decisions, retries, approvals, and cost visibility.
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