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Ask HN: How are people forecasting AI API costs for agent workflows?
I’ve been experimenting with agent-based features and one thing that surprised me is how hard it is to estimate API costs.
A single user action can trigger anywhere from a few to dozens of LLM calls (tool use, retries, reasoning steps), and with token-based pricing the cost can vary a lot.
How are builders here planning for this when pricing their SaaS?
Are you just padding margins, limiting usage, or building internal cost tracking?
Also curious, would a service that offers predictable pricing for AI APIs (like a fixed subscription cost) actually be useful for people building agentic workflows?
- thiago_fm 7mo agoJust add very hard high limits and add instrumentation so you can track it and re-evaluate it accordingly. This takes a couple of hours maximum at best.
- Barathkanna 7mo agoSounds like a plan, But what if you can just pay a fixed cost every month and not worry about anything?
- thiago_fm 7mo agoWhat you suggested is the best way in my opinion, but given what OP asked, I gave my answer.
- clearloop 7mo agoimo switch to local models could be an option
- Barathkanna 7mo agoLocal models solve the marginal cost problem, but they move the complexity into infrastructure and throughput planning instead.
- clearloop 7mo agomakes sense, it really depends on the use cases, I'm building my version of claw openwalrus for the local LLMs first goal, I think myself will use local models for daily tasks that heavily depend on tool callings, but for coding or doing research, I'll keep using remote models and this topic actually inspires me that I can introduce a builtin gas meter for tokens
- Lazy_Player82 7mo agoHonestly, if you're designing your agent workflows properly with hard limits on retries and tool calls, the variance shouldn't be that wild. Most of the unpredictability comes from not having those guardrails in place early on. A few weeks of real production data usually shows the average cost is more stable than you'd expect.
- Barathkanna 7mo agoTrue, but for early stage builders it’s harder to design those guardrails upfront. A lot of the time you only discover the retry patterns and cost spikes once real users start hitting the system.
- Lazy_Player82 7mo agoFair point. And honestly, with more non-technical builders shipping agent-based products these days, that's probably where a service like this makes the most sense – for people who don't yet have the experience to know what guardrails to put in place.
- Barathkanna 7mo agoExactly. That’s actually why we started building Oxlo.ai. Early stage builders usually just want to experiment without worrying too much about token cost spikes.
- sriramgonella 7mo ago[flagged]
- thiago_fm 7mo agoThat isn't true, if you run local models you'll also need to have to spend on operations. Maybe focus first on providing value and later you can optimize this setup.
- Barathkanna 7mo agoLocal models help remove token cost uncertainty, but they shift the problem to infrastructure and ops. GPUs, scaling, maintenance, and latency can add up quickly depending on the workload. For many builders it ends up being a tradeoff between predictable infra cost and flexible API usage.
- gabdiax 7mo agoIt feels like the traditional fixed SaaS pricing model is slowly shifting toward more consumption-based pricing.
- Barathkanna 7mo agoThat’s true, but AI is interesting because consumption-based pricing introduces a lot more variance than typical SaaS infrastructure. One user action can trigger dozens of model calls in an agent workflow. That’s partly why we started experimenting with models like https://oxlo.ai https://oxlo.ai where the pricing flips back to a fixed subscription and we absorb the usage spikes.
- hkonte 7mo ago[dead]
- Barathkanna 7mo agoOne underlooked source of variance is retries from formatting failures. In many agent systems the loops dominate the cost, not the raw token length. We ran into the same issue building agent workflows, which is why we started building https://oxlo.ai https://oxlo.ai — experimenting with a flat subscription model where we absorb the token variance so builders don’t have to constantly model token risk.
- gilles_oponono 7mo agoI love the idea. @Edgee.ai we are tracking cost in real time by tag, LLM, ...but not yet on forecast, and indeed it will be very useful. Something to explore; thanks for the feedback.
- Barathkanna 7mo agoThat’s great. Real-time tracking is a big step already. The tricky part we kept running into was the variance itself, especially with retries and agent loops. That’s partly why we started experimenting with Oxlo.ai (https://oxlo.ai https://oxlo.ai) where the pricing model absorbs that variance so builders don’t have to constantly model token risk.
- dkowalski 7mo agothis is exactly the problem we keep running into. the cost isn't just "how many tokens did this call use," its "how many tokens did this entire user action consume across all the agent loops, retries, tool calls, and embeddings." most observability tools show you the LLM call as one flat span. you can see it cost X tokens but you cant correlate it with the API request that triggered it, or see that the agent looped 4 times because the first 3 outputs failed validation. so you end up building custom logging and hoping the numbers add up. we've been building an APM (immersivefusion.com) where cost is a first-class dimension on every trace. so you can see one request flow from the UI through your backend through the agent workflow, and each span carries its token cost. the idea is you should be able to answer "what does a checkout cost when the recommendation agent is in the loop" without stitching together 3 different tools. for the forecasting question specifically, i think the answer is you need a few weeks of production data with good instrumentation and then you can build a distribution. the variance is real but its not random, its usually a few specific flows that blow up (retries on bad structured output like @hkonte mentioned, or RAG queries that hit the wrong chunk size). once you can see which flows are expensive the guardrails become obvious. also wrote a longer piece on this if anyone's interested: immersivefusion.com/blog/end-to-end-observability-from-ui-to-ai-agent-to-invoice
- Barathkanna 7mo agoAgreed. The real cost unit becomes the whole agent workflow, not a single LLM call. One user action can trigger dozens of calls. We ran into the same issue and ended up building https://oxlo.ai https://oxlo.ai to make the cost side more predictable for agent workloads.
- syndacks 7mo agolowercasing doesn’t obfuscate the stench of LLM
- socialinteldev 7mo ago[flagged]
- novachen 7mo agoWe've been dealing with this exact problem building agent-driven workflows. A few things that have actually helped: The unpredictability is worse than the absolute cost. Our billing model broke several times not because costs were high, but because we couldn't bound them. One approach that helped: define a 'token budget' per user action at design time - cap total tokens per session and treat hitting the cap as a first-class outcome your product handles gracefully, not an error. On the forecasting side, we track cost per workflow step rather than per request. Step-level cost is much more stable than request-level because it absorbs the variance in tool calls and retries. Once you have step costs, you can forecast by expected workflow composition. On fixed subscription pricing for AI APIs - I'd actually pay a premium for that. The unpredictability creates a hidden cost: you over-provision margins and add complexity to your pricing tier design. A flat rate for a capacity bucket would eliminate both. The question I'd ask about any such service: how do they handle the tail cases where agents go off-rails and rack up 10x normal token usage? That's where the cost risk actually lives.
- reggz 7mo agoI’d break it into a few separate numbers instead of trying to estimate one blended API cost. We usually look at cost per workflow run, runs per active account, and the heavier paths separately, then keep retries and tool calls as their own line items. That makes the pricing side easier to reason about.
- deleted 7mo ago[deleted]
- dreadpirates 7mo ago[flagged]