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 resonates. Once an AI feature has more than one step, monthly totals stop being useful pretty quickly.
The thing I’ve found most important is separating “model cost” from “workflow cost.” A single expensive call might be fine if it produces the final user-facing result, while a
bunch of cheap retries or duplicated intermediate calls can be much worse.
I’d definitely be interested in the minimal attribution schema. Especially how you label workflow steps and distinguish real user demand from retry / failure noise.
Request-level cost attribution is something every team running AI in production needs and almost nobody has. We run multiple AI agents daily (content generation, security auditing, SEO analysis, competitive intelligence) and our biggest cost surprise was discovering that retry storms during API timeouts were burning 3x the tokens we expected. A single flaky connection would trigger 5 retries, each sending the full conversation history.
The three cost drivers you identified — duplicate calls, context bloat, and retry storms — are exactly right. We solved retry storms with a circuit breaker pattern (stop retrying after N consecutive failures) and context bloat by aggressively pruning conversation history. But we built all of this manually because nothing off-the-shelf existed.
The branding question in the comments is interesting. "Key management" undersells what you're building. If you can show a team that Agent X is costing $400/month because it's sending 12KB of context per request when 2KB would suffice, that's not cost monitoring — that's AI architecture consulting delivered as a dashboard.
The attribution schema piece is the right place to invest. Pattern that works well: treat each AI call as an event fact row with dimensions for model, workflow/agent, project/user, is_retry (boolean), and outcome_status. With that grain, "cost per successful completion" vs "cost per retry storm" is just a GROUP BY — no additional instrumentation needed downstream.
The spike diagnosis problem is usually a time granularity issue. Most teams aggregate by day, which flattens the shape of a retry storm entirely. Keeping request_timestamp at minute granularity and flagging the first call in a sequence separately makes those patterns visible in any BI tool without building custom anomaly logic.
Would definitely read a follow-up on your anomaly rules — particularly curious how you're handling partial failure attribution across multi-step agent chains.
This problem hits hard once you cross a certain MRR. Running SocialPost.ai, the moment we passed the threshold where AI spend became a meaningful percent of COGS, the lack of per-customer attribution went from annoying to existential. Most cost tools treat AI like infrastructure. It is closer to variable cost of goods sold. The companies that figure out attribution per workflow and per customer first are the ones who can confidently price tier upgrades. Curious if AiKey supports tagging at the request level by end-user, not just project.
Cost without explainability is the new 'we have logs but no traces' problem for AI. Are you attributing spend by prompt/route, by user/session, or by feature? The first surfaces obvious wins, the third is what PMs actually want to act on.
The 'cost-per-outcome' framing is exactly what's missing from most AI infrastructure discussions right now. As we move from simple chatbot calls to complex, multi-step agentic workflows, the ability to attribute spend to a specific feature or customer outcome is the only way to keep unit economics healthy. Moving from opinion-based optimization to request-level evidence is a game changer for teams scaling their production AI and trying to justify the infra costs at the end of the month.
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.
That makes sense, especially if you are already evaluating brand architecture.
I would treat that as a near-term decision, not a later polish item.
AiKey is clear for the credential/key-management wedge, but if the product is moving toward runtime governance, cost attribution, policy, routing, anomaly detection, and workflow-level control, the category becomes much bigger than “AI keys.”
The risk with waiting is that users, docs, integrations, and early customers start remembering the product through the key-management frame. Then when the product becomes broader infrastructure, the market still describes it by the original narrow wedge.
Exirra feels stronger for the broader direction because it can carry AI infrastructure, governance, and runtime intelligence without locking you to credentials.
If Exirra is genuinely close to the kind of brand architecture you are evaluating, message me here or on LinkedIn. I may be able to help with that side privately without turning this thread into a public pricing discussion.
https://www.linkedin.com/in/aryan-y-0163b0278/
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.