We could see our AI bill, but not explain it — so I built AiKey

This pattern is the same one cloud spend hit around 2014. Everyone could see the AWS bill, nobody could tell you what a specific product line was costing until Cloudability and Apptio tied spend to unit economics. The wedge is real and the timing on AI is right. Two things worth thinking about: cost-per-successful-outcome is the metric finance and product will actually want, not cost-per-request, so how you define a 'workload' matters more than the attribution itself. The curl install works for early adopters but enterprise buyers will need a hosted dashboard before they standardize. Curious whether you're seeing pull from devs or finance first, that usually predicts the GTM motion.

This is a real pain point. I run multiple LLM APIs across different projects and tracking which calls actually drive value vs which are waste is nearly impossible with just provider dashboards. The "cost-per-outcome" framing is key — most teams only look at total spend, not whether that spend produced better results. Would be interested in seeing the attribution schema if you share it.

“Cost-per-outcome” is the important part here. Feels like a lot of teams are still optimizing token cost in isolation without knowing whether the workflow itself actually got better.

Also retry storms + context bloat are painfully real in multi-agent setups 😅

This is a genuinely useful direction, especially as more teams move from “experimenting with AI” to actually managing AI costs at scale.
The part about request-level attribution really stands out. Most teams still have one shared API key and a monthly invoice with zero clarity on which workflow, agent, or retry loop caused the spike. That becomes painful fast once multiple tools and automations are involved.
Also like the focus on virtual keys instead of exposing raw provider credentials everywhere, feels much closer to how mature cloud infrastructure evolved.

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.