Model routing
13 articles
Enterprise AIA Corporate AI Strategy for 2026: From Pilots to Production
Most enterprises use AI; few run it at scale. A practical framework for moving from pilots to production, with a clear answer to the question pilots skip: where the inference capacity comes from.
5 min read
Model routingBuild, rent or network? Sourcing AI inference in 2026
CTOs now have three ways to source AI inference: build on their own hardware, rent from a cloud or model API, or buy from an open network of independent providers. This guide compares them on the criteria that decide the outcome: demand shape, model choice, jurisdiction, verification and exit.
5 min read
Computer visionComputer Vision at the Edge: Infer Locally, Escalate Rarely
Cameras produce far more pixels than anyone should ship to a data centre. A tiered architecture for vision AI: small detectors at the camera or gateway, larger models only for the frames that need them, and privacy built into the split.
4 min read
Energy and placementEnergy, power and placement: routing AI work to capacity
Power and grid connections, not chips, now limit where AI capacity can grow. This article sets out what the IEA, the US Department of Energy and EU rules say about data-centre electricity, and why routing flexible inference to where power and capacity already exist is a policy and engineering lever.
5 min read
Generative mediaGenerative Media Pipelines: Right Hardware, Pay Per Result
Image, video and audio generation have cost profiles unlike chat. How to route each job to hardware that fits it, meter it in units that match the work, check licence territory and mark outputs for provenance.
5 min read
Model routingModel Ladders: Answer With Small Models, Escalate Only When Needed
Most requests do not need the largest model. What the research on cascades and routing actually shows, where it falls short, and how Tenzro routes by measured difficulty and closes the loop on escalation.
5 min read
NetworkingMulti-region AI: latency, jurisdiction and failover
Running AI across the EU, US and APAC forces trade-offs between distance, law and resilience. This article uses measured inter-region latencies to show what each region pair costs, how jurisdiction rules constrain failover, and how to design inference that stays fast, lawful and available when a provider fails.
5 min read
Open modelsOpen models need open infrastructure: provenance and licences
Once open weights are released, the builder loses sight of how they are served. This article covers what model builders should expect from the infrastructure that serves their work: proof of which weights ran, licence terms enforced in the serving path, gated access that holds, and attribution that reaches the user.
5 min read
Model routingRouting Across Many Providers, and Why Receipts Must Be Verifiable
A model router chooses on price, latency and standing, hedges the slow tail and fails over in seconds. None of that is trustworthy unless each answer comes with a receipt a third party can check. How Tenzro does both.
5 min read
Telco edgeTelcos as AI edge clouds: inference inside the latency budget
Telecom operators have the sites, the fibre, the licences and the customer relationships that low-latency AI needs. This article covers how to split an inference request across device, edge and regional GPU, what a latency budget actually allows, and what a telco needs in order to sell edge inference to buyers it has never met.
5 min read
Enterprise AITotal Cost of AI Ownership: What Inference Really Costs
Token prices are the visible part of an AI bill. A cost model for enterprise inference that includes the harness around the model, utilisation risk and supplier lock-in, and seven levers that reduce it.
5 min read
AI factoriesUtilisation decides AI factory economics
An AI factory's accelerators cost the same whether they are busy or idle, so served work per installed accelerator is the number that matters. This article covers where utilisation is lost, and how matching demand across many operators recovers some of it.
5 min read
Voice AIVoice AI Latency Budgets: Serve Speech Close to the Speaker
People expect a reply within a few hundred milliseconds. A latency budget for voice agents, where each millisecond goes, and why speech recognition and synthesis belong near the user while the language model can sit further away.
4 min read