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Inference APIs

A collection of 4 posts

Why Point-Solution Vector Databases Fail the Enterprise Long-Term Scale Test

Retrieval-Augmented Generation

Why Point-Solution Vector Databases Fail the Enterprise Long-Term Scale Test

During early Proof-of-Concept (PoC) phases, engineering teams frequently rely on specialized, point-solution vector databases like Pinecone, Qdrant, or Chroma. Their value proposition at this stage is undeniable: simple APIs, fast initial integration, and rapid prototyping for AI applications like Retrieval-Augmented Generation (RAG). However, as AI projects move from small-scale pilots

Shadow AI in the Enterprise: How to Detect and Secure Rogue LLM API Calls

Shadow AI in the Enterprise: How to Detect and Secure Rogue LLM API Calls

Shadow IT used to mean an employee spinning up an unsanctioned Dropbox account or plugging a personal laptop into the corporate network. Today, the fastest-growing version of that problem has a new name: Shadow AI. It's the unsanctioned use of large language models, AI copilots, and third-party API

How to Use a Dedicated GPU ROI Calculator to Decide When to Switch

How to Use a Dedicated GPU ROI Calculator to Decide When to Switch

"Should we move off the managed API and rent our own GPU instance?" comes up in almost every team's growth curve. Usually around the point the monthly API bill starts showing up in leadership meetings. The answer isn't a gut call — it's

The Hidden Costs of Managed Inference APIs Nobody Mentions Upfront

The Hidden Costs of Managed Inference APIs Nobody Mentions Upfront

Managed inference APIs look simple on the pricing page. Pay per token, no infrastructure to manage, done. Then your usage scales, and the bill starts telling a different story. The hidden costs of managed inference APIs rarely show up in the headline rate — they show up in the fine print,