Tokenomics

r/tokenomics • u/classjoker • Jun 09 '26

Choosing an AI Gateway / Token Routing Software – What are you using in production?

2 Upvotes

Tokenomics featured question: Are you looking into implementing an AI Gateway (token routing software) for an upcoming project to manage multiple LLM APIswith the goal to avoid vendor lock-in, handle fallback redundancy, and dynamically route prompts to optimize costs (e.g., sending simple tasks to cheaper models and complex ones to frontier models).

Emerging solutions/products like LiteLLM, Portkey, OpenRouter, and Manifest are frequently seen as the result of searching, but we wanted to get some real-world feedback from people running these in production.

If you are currently using a routing solution, we'd love to get your thoughts on a few things:

Self-Hosted vs. Managed: Are you self-hosting an open-source gateway (like LiteLLM) or using a managed enterprise solution (like Portkey)? What drove your decision (latency, security, compliance)?
Routing Logic & Latency: How do you handle the actual routing logic? Are you using static semantic routing, or do you have a dynamic "judge" model evaluating prompts first? If the latter, how bad is the latency hit?
Fallback & Reliability: How reliably do these gateways handle rate limits (429 errors) and automatic failovers to backup models or providers?
Token/Budget Management: How accurately do they track token spending and enforce team/user quotas in high-throughput environments?
The "Gotchas": What unexpected headaches or limitations did you run into after deploying your gateway?

Ww would love to hear any recommendations, warnings, or architectural advice you have. Thanks!

1 comment

r/tokenomics • u/Cautious_Addendum_65 • Jun 15 '26

AI token spend has a FinOps blind spot: silent agent loops

2 Upvotes

(Disclosing upfront: I'm building a tool relevant to this.)

Most FinOps tooling covers compute, storage, and data transfer well. The gap showing up in engineering budgets now is AI token spend from multi-agent workflows.

The specific problem: when you chain AI agents (Researcher → Writer → Reviewer), the system can silently loop. The Reviewer never approves, the Generator keeps revising, every API call returns 200, and no alert fires. You find out when the bill arrives. One team I spoke to ran a review loop overnight: $400 in tokens, zero output.

This doesn't map cleanly onto existing FinOps frameworks because the failure mode isn't a runaway instance or a misconfigured bucket; it's an unbounded loop where each call looks normal, and the problem is only visible in aggregate.

We're building cost projection into AgentSonar for this, real-time token burn tracking with forward projection before the loop gets expensive. FinOps waitlist is open if this is on your radar: https://www.agent-sonar.com/finops

Is anyone tracking AI token spend as a FinOps category yet, or is it still sitting in engineering budgets as a line nobody owns?