New playbook: when your own AI model pays. Read

Pinterest: <8% of closed-model cost per transaction

Pinterest (consumer internet and advertising, ~5,000 employees) reported this on 5 Aug 2026 in earnings call (source). It belongs to the models and routing layer.

What they built

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.

The result

Cost per transaction with open models at less than 8% of comparable closed proprietary models, with 'superior performance for our use cases' (e.g., Pinterest Assistant).

The number covers pinterest's own use cases where open models replaced comparable closed ones; stated by CEO on earnings call.

“With open models, we are achieving cost per transaction at less than 8% of the cost of comparable closed proprietary models,” Pinterest said.

The layer: models and routing

One gateway picks the AI model for each task and records what it costs.

  • One gateway for every model call
  • Routing rules by task, cost and data
  • Weekly cost per task, with quality

Read the guide to models and routing.

Playbooks that use this case

More cases on models and routing

  • Up to 90%

    lower AI cost, AT&T says

    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 (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).

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