FeatureCost: No-Latency LLM Spend Tracker for AI SaaS

How are you tracking LLM API costs per feature in production?

Disclosure: I work on Credyt, so factor that in. The pattern you're describing is the most common AI billing story I hear: nobody knows where the cost is coming from until the invoice lands, then it turns out one feature is eating most of the budget. Your enumeration of the bad options is also pretty accurate, the enterprise tools are expensive and overkill, proxies add latency to every call which is unacceptable in production, and provider dashboards are useless the moment you use more than one model. The way we built Credyt for exactly this: you send a usage event after each LLM call (one POST, no proxy, no latency), tag it with feature, user, workflow, whatever dimensions you care about, and you get per-feature, per-user, per-workflow cost breakdowns in real time. Multi-provider works out of the box because the cost is something you compute and pass in, so OpenAI, Anthropic, Google, open-source models all unify in one view. First 1M events a month are free, which covers most products at your stage. To your actual questions: tracking at feature level is the right move, total monthly spend tells you nothing about which feature is profitable. The summarization-eats-60% problem you hit is the classic one, and it's almost always either a too-large context window or a chatty system prompt, both invisible without per-feature attribution. And yes, surprise bills are the rule, not the exception, that's basically why the category exists. It's at credyt ai if you want to skip building the tracking yourself.

Disclosure: I work on Credyt, so factor that in. The pattern you're describing is the most common AI billing story I hear: nobody knows where the cost is coming from until the invoice lands, then it turns out one feature is eating most of the budget. Your enumeration of the bad options is also pretty accurate, the enterprise tools are expensive and overkill, proxies add latency to every call which is unacceptable in production, and provider dashboards are useless the moment you use more than one model. The way we built Credyt for exactly this: you send a usage event after each LLM call (one POST, no proxy, no latency), tag it with feature, user, workflow, whatever dimensions you care about, and you get per-feature, per-user, per-workflow cost breakdowns in real time. Multi-provider works out of the box because the cost is something you compute and pass in, so OpenAI, Anthropic, Google, open-source models all unify in one view. First 1M events a month are free, which covers most products at your stage. To your actual questions: tracking at feature level is the right move, total monthly spend tells you nothing about which feature is profitable. The summarization-eats-60% problem you hit is the classic one, and it's almost always either a too-large context window or a chatty system prompt, both invisible without per-feature attribution. And yes, surprise bills are the rule, not the exception, that's basically why the category exists. It's at credyt ai if you want to skip building the tracking yourself.