New playbook: when your own AI model pays. Read
AT&T (telecom, ~100,000 employees using the internal AI platform) reported this on 23 Jul 2026 in company self-report (source). It belongs to the models and routing layer.
An internal AI Gateway that, turn by turn, weighs speed, cost and expected quality and routes each prompt to the most cost-effective model, switching models mid-session, plus a library of post-trained open telco models (OTel 2.0) that run on-prem or in cloud.
AI costs reduced 'as much as 90%', 'already saving millions'; 45 billion tokens/day on average.
The number covers up-to figure; company-wide average disclosed is not reported.
“…we're reducing AI costs as much as 90%, already saving millions,” AT&T said.
One gateway picks the AI model for each task and records what it costs.
Read the guide to models and routing.
<8%
of closed-model cost per transaction
A blended model portfolio (in-house compact models, open-source models post-trained on Pinterest data in its own secure cloud, closed models only in 'very limited cases') behind a centralized routing layer that sends routine work to lighter models; being extended to internal engineering and sales tools in H2 2026 (source).
52%
lower cost per agent session since the June peak
A cost equation and weekly metric stack for its agentic 'software factory' (70%+ of PRs agent-attributed), with benchmark-driven Pareto model selection (incl. open-weight models), cheaper default subagent models, Medium reasoning effort default, 400K-token compaction cap and prompt-cache strategy (source).
30%+
lower average task cost from smart routing, internal coding work
An internal control layer between developers and models: moving defaults to newer cheaper models in one place, task-aware routing across models and harnesses, per-developer spend reports, and progressive friction (warning, approval, downshift to cheaper model) in place of hard caps (source).