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Bring your own AI subscription: a practical approach to token costs

I started by bundling AI usage, token metering, and workflow in one product, then realized token billing was becoming its own business. Here’s why I’m testing a bring-your-own-AI-subscription model for PailFlow.

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Bring your own AI subscription: a practical approach to token costs
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I’ve been rethinking how AI usage and token billing should work in PailFlow, and I think this matters for anyone building agent systems.
I started where many AI products start: bundle everything. Host workflows, run agents, meter tokens, and charge credits or usage tiers. On paper, this architecture looks clean because one tool owns the full loop.
In practice, token costs can get ugly quickly, and billing logic starts to become its own product. Instead of improving post-call delivery workflows, I was spending more time thinking about reselling AI usage. That is real work, but it is not the highest-value layer for what I’m building.

What bring your own AI subscription changes

The pattern I’m seeing in tools like OpenClaw and OpenCode is bring your own AI subscription.
The user already has paid AI access. They bring that access into a third-party tool. The tool still adds real value through workflow structure, agent behavior, prompts, orchestration, and better execution patterns than raw prompting alone.
So the value exchange changes. You pay for workflow value, not token costs twice.
I’ve mostly seen this pattern in open source tools so far, but I think it can extend further. If users already trust and fund their AI subscription, many will prefer one AI access path and multiple workflow tools on top.

How I’m applying this in PailFlow

I’m testing this direction now: keep AI subscription access user-owned, and keep PailFlow focused on reliable workflow outcomes.
That means I care most about workflow state, approvals, execution quality, and predictable handoffs after client conversations. It also reduces pressure to build a second business around token billing before the workflow layer is fully matured.
This is still a speculative bet. Some big model labs are tightening ecosystem control and limiting third-party tool access, while open-source and API-first players are expanding it. For now, I’m watching this closely and designing around what stays usable for real workflows.

Conclusion

AI and software are moving fast, and where long-term value settles is still unclear. For now, I’m choosing the setup that helps me ship and learn faster: users bring AI subscription access, and I focus on building better workflow structure, review quality, and reliable delivery outcomes.
And that’s it! I’m building PailFlow in the open and sharing how I use AI systems to scale a one-woman business.
If you work in client services and want to see how AI can increase your project delivery capacity, book a PailFlow Delivery Audit.

Written by

Lola
Lola

Lola is the founder of Lunch Pail Labs. She enjoys discussing product, app marketplaces, and running a business. Feel free to connect with her on Twitter or LinkedIn.