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    Tag: Inference Infrastructure

    Sep 21
    17 min read
    Cloud Hosted APIs vs. Self-Hosted vLLM: Cost, Throughput, and Failure Rate Comparison at Scale

    In the lifecycle of deploying enterprise autonomous agent swarms, organizations invariably begin their journey plugged directly into centralized cloud-hosted model APIs like OpenAI, Anthropic, or AWS Bedrock. Commercial APIs offer frictionless time-to-market. Developers provisioning digital coworkers configure an API key, declare their client tooling via the Model Context Protocol, and immediately consume frontier models without […]

    Sep 21
    18 min read
    Batching vs. Streaming in Agentic Runtimes: Profiling Inference Infrastructure Throughput Under Heavy Load

    In high-density artificial intelligence infrastructure, serving standard chat interfaces represents a largely solved problem. Modern inference engines (such as vLLM, TensorRT-LLM, and TGI) leverage continuous iteration-level batching (dynamic batching), PagedAttention, and streaming Server-Sent Events (SSE) to multiplex hundreds of concurrent human dialogues across shared GPU clusters. In human-facing chat workloads, the operational model is clear: […]