We could see our AI bill, but not explain it — so I built AiKey
Hey IH — sharing a real problem we kept hitting in production.
By 2026, most teams I work with can see monthly AI totals, but still can’t answer basic questions like:
Which workflow caused the spike?
Was it real usage or retry noise?
Did higher cost actually improve outcomes?
The biggest token drains we keep seeing:
Duplicate calls across tools/agents
Context bloat (too much history per request)
Retry storms during partial failures
The issue isn’t just “high AI cost.”
It’s low visibility + weak controls.
So I built AiKey as a runtime credential + governance layer:
unified access across accounts/keys
request-level attribution by project/workflow/model
policy guardrails (budget alerts, routing, permissions)
What changed for us after implementing this:
cost discussions moved from opinions to evidence
spikes became diagnosable within minutes, not month-end
optimization focused on cost-per-outcome, not just “cheaper calls”
I’m sharing this to compare notes with other builders operating AI in production.
Project: https://github.com/aikeylabs/launch
If useful, I can share our minimal attribution schema + anomaly rules in a follow-up post.
This hits a real pain point — we had the exact same problem at my company. Azure OpenAI costs would spike and we'd spend hours cross-referencing logs trying to figure out which pipeline or feature was the culprit. The "see it but can't explain it" feeling is exactly right.
Quick question: does AiKey break down costs at the prompt/feature level, or is it more at the model/API key level? That granularity question was always our sticking point — knowing we spent $400 on GPT-4 is useless; knowing which endpoint burned $400 is actionable.
This is a strong infra problem because the pain is not the AI bill itself. It is that teams are running production AI workflows without request-level accountability. Once agents, retries, routing, and context history are involved, monthly spend becomes too blunt to explain what is actually happening.
The “cost-per-outcome” framing is the sharpest part. That moves AiKey away from being only a key-management layer and closer to AI runtime governance: attribution, policy, routing, anomaly detection, and control at the workflow level.
The naming is worth taking seriously too. AiKey explains the credential layer, but it may become too narrow if the product grows into broader AI cost governance and runtime control. For that direction, Exirra .com would feel more like infrastructure software, not just an AI key utility.
Great take — really appreciate this.
You captured the core problem exactly: the bill isn’t the hardest part, the lack of request-level accountability is. Once agents, retries, routing, and long context chains enter production, monthly totals stop being operationally useful.
Also +1 on your read of our direction. We started from credential orchestration, but the product is clearly moving toward runtime governance: attribution, policy, routing, anomaly detection, and workflow-level control.
And thanks for the naming feedback — that’s a very thoughtful point. We’re actively evaluating brand architecture as the scope expands beyond key management.
Really appreciate you taking the time to write this.
If anyone wants to try AiKey quickly, here are install links:
macOS/Linux: curl -fsSL https://aikeylabs.com/zh/i/ih01 | sh
Windows: https://aikeylabs.com/d/ih01?platform=windows
Happy to hear feedback on setup friction / missing docs.