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Infrastructure

Open-Source Serving Stacks: vLLM vs TGI vs TensorRT-LLM in 2026

Your choice of serving engine determines 30-60% of your inference cost. Here's which one wins for your workload.

Serving infrastructure is the largest variable cost in any AI deployment. Picking the wrong engine means leaving money and latency on the table.

12 min read |
Infrastructure

Cloud GPU Pricing Shifts in Q1 2026

AWS, GCP, and Azure all adjusted GPU instance pricing this quarter. The savings are real, but only if you know where to look.

GPU compute is typically 60-80% of AI infrastructure spend. A 15-20% price shift changes the economics of every deployment decision.

6 min read |
Agent Economy

MCP Is Winning the Agent Tool Protocol War

Six months after launch, MCP has 2,000+ community servers and adoption from every major AI lab. The protocol war is over before it started.

Agent-tool integration is the plumbing layer of the AI economy. Whoever controls the protocol controls the ecosystem's interoperability — and the switching costs.

11 min read |
Agent Economy

Agent Payment Rails Are Finally Emerging

Agents that can browse, code, and reason are useless if they can't pay for the services they need. That bottleneck is breaking.

Agent autonomy requires financial autonomy. Without payment rails, every agent transaction needs a human in the loop — which defeats the purpose of automation.

7 min read |
Infrastructure

Model Benchmarks Are Lying to You

That model scoring 92% on MMLU? It might perform 15-20 points lower on your actual workload. Here's why, and what to do about it.

Model selection drives architecture decisions, cost projections, and product timelines. If your selection is based on misleading benchmarks, everything downstream is wrong.

8 min read |

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