Custom Models and Self-hosting Considerations

Abi discusses enterprise interest in fine-tuning and bespoke models while noting most midsize firms stick with public models today.

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In this episode of Engineering Enablement, Laura Tacho and Abi Noda discuss how engineering leaders can plan their 2026 AI budgets effectively amid rapid change and rising costs. Drawing on data from DX’s recent poll and industry benchmarks, they explore how much organizations should expect to spend per developer, how to allocate budgets across AI tools, and how to balance innovation with cost control.

Laura and Abi also share practical insights on building a multi-vendor strategy, evaluating ROI through the right metrics, and ensuring continuous measurement before and after adoption. They discuss how to communicate AI’s value to executives, avoid the trap of cost-cutting narratives, and invest in enablement and training to make adoption stick.

Where to find Abi Noda:

• LinkedIn: https://www.linkedin.com/in/abinoda

• Substack: https://substack.com/@abinoda

Where to find Laura Tacho:

• LinkedIn: https://www.linkedin.com/in/lauratacho/

• X: https://x.com/rhein_wein

• Website: https://lauratacho.com/

• Laura’s course (Measuring Engineering Performance and AI Impact): https://lauratacho.com/developer-productivity-metrics-course

In this episode, we cover:

(00:00) Intro: Setting the stage for AI budgeting in 2026

(01:45) Results from DX’s AI spending poll and early trends

(03:30) How companies are currently spending and what to watch in 2026