u/classjoker

I keep coming back to a number I read this week from a public agent-failure trace study. Failed runs spent roughly 58 percent of their tokens after the first warning signal appeared, meaning an explicit tool error or a repeat tool call with identical arguments. The model already had enough evidence to stop and it kept going. That is not a model quality problem. It is a budget-discipline problem, and I think most FinOps setups today do not have the surface to catch it.

The same reading dropped two other data points I have not been able to shake. Anthropic's Dynamic Workflows can run up to 16 concurrent subagents with 1000 total in a single run. If your kill switch is a monthly bill anomaly rule, that ceiling can produce a very expensive Wednesday afternoon before your Thursday dashboard flags anything. And a suggestion I liked more than I expected: three cost classes as the budgeting unit. High-volume low-value work capped at cents. Standard knowledge work worth roughly $50 of human labor gets a $5 budget. High-value work worth $5,000 gets $500, because starving the agent is more expensive than feeding it. Named owner per agent. Breaker built in.

The reason this bugs me is that the FinOps industry keeps saying "attribution" as if the hard part is knowing who spent the tokens. In practice the harder part is knowing when to trip the breaker mid-run. The trace study says the signal is there. The tooling is not.

So a real question. How is your team handling this today? Are you actually cutting runs off mid-flight when the failure signal fires, or are you catching it in the next day's cost review and eating the burn?

All free. Here are the exact links:

1. Claude 101 - 1 hour. The basics, done right.
2. AI Fluency: Framework & Foundations - 3 hours.
3. Intro to Cowork - 2 hours. Claude's best feature.

All 3 are on anthropic.skilljar.com. 
Sign-up takes 30 seconds.

More info here: https://ruben.substack.com/p/im-claude-certified

Hello everyone, currently digging into workflow-layer economics and trying to figure out how people track unexpected runtime spikes at scale.

At an early stage simple margin buffers are fine because volume is bounded. But once you move past basic apps, factors like failed loops, retries, and context window inflation create a ton of cost variance that is hard to forecast or map to clean client billing.

For those running agent or voice workflows in production, or working on complex ai products what do you currently use to understand costs and failures at the individual workflow level?

More importantly, what's something you still can't easily answer with your current setup? Like why did a specific workflow suddenly cost 2x more, or which exact customer trigger is driving the increase? Are you guys just manually digging through raw api logs to catch leakage like infinite loops, or has it not become a big enough issue for your teams yet?

Curious to hear how other teams handle the infrastructure discipline here.

I just can't get on with the tame note of the stock exhaust on a 2 cylinder engine and our UK version doesn't have the removable DB killer like the USA version. Honda have just made this too ordinary so I've just grabbed a 'used' Black Window 200ST in chrome and carbon.

I've had Black Widows before and like the sound, even with the DB killer in. As this is the 200 length the DB killer is likely to stay in!

It lacks legality I know, but no-one else seems to care about it on London roads so maybe I'm being a bit selfish, but I'd just rather other road users are aware of me being there.

Implementing a policy where developers fund their own AI tools and API tokens is essentially taking the "Bring Your Own Tokens" (BYOT) mode

​

The most immediate advantage is financial. Enterprise AI licenses and API/token costs can scale unpredictably, especially when external contracted developers are running complex queries or building automated agents.

​

You eliminate or pass on the risk of unused "zombie" agents that plague enterprise software budgets.l when there is zero accountability

Plus the company assumes zero financial risk for token usage, completely insulating your budget from price hikes by AI providers or sudden surges in development activity.

​

Does anyone have any thoughts about possible downsides of this?

So I finally sat down and ran the most mentioned gateways through my actual workflow instead of just reading marketing pages. Not a ranking, just honest notes from daily use.

OpenRouter Huge model catalog and the single-key setup is genuinely convenient. But the 5.5% markup adds up fast at volume, latency spikes are real, and observability is basically nonexistent. Fine for tinkering, questionable for production.

TrueFoundry The enterprise option of the bunch. Selfhosted anywhere (VPC, on prem, even air gapped), so your data never leaves your infra. Solid governance,  RBAC, audit logs, rate limits, per-team budgets plus tracing down to GPU metrics and an MCP registry if you're running agents.

ZenMux Day one access to new models with no markup, and native OpenAI/Anthropic protocol support is nice. Free tier is mid, and they've quietly pulled free trials for some launches.

LiteLLM The open-source default for a reason provider switching and fallbacks just work. But it gets sluggish under heavy traffic and there've been some security CVE discussions that give me pause for prod.

Portkey 1,600+ models, reliable routing, virtual keys, and the best cost dashboards I tested. Dealbreaker for me: no self-hosting, which kills it if you need infra control.

Curious what everyone else is running, anyone paying for one of these and feeling like it's actually worth it?

One of my other hobbies is miniature tabletop warfare. I like painting too and buy originals when they exist and are available, but sometimes they are not and I turn to proxying until I can get the actual plastics.

​

I see a lot of services that offer things like photo to stl services and so on, so I'm wondering what is generally available in this space, the requirements and how advanced it's managed to get.

The newly released PwC 2026 AI Jobs Barometer shows a bizarre shift in the labor market: entry-level roles most exposed to AI are now 7x more likely to require traditionally senior-level "human-intensive" skills like strategic decision-making, team building, and leadership.
Not mid-level roles. Entry-level.
The reason is simple: AI has rapidly automated the foundational, routine tasks that used to act as the training grounds for junior workers. The drafting, the scheduling, the basic organizing—that work used to build practical business judgment over two to three years. Now a tool does it instantly.
Because these basic tasks are handled by software, the training ground is effectively gone, but the high-level job expectation stayed.
These "seniorised" entry-level roles have grown 35% since 2019, while traditional non-AI entry-level roles actually shrank by 10%. Companies are essentially putting junior salaries on job descriptions that demand senior cognitive skills and independent judgment, all without offering a formal mentorship structure to close the gap.
Curious whether people are seeing this firsthand in hiring, in your own recent job searches, or when managing teams right now. What does this early-career gap look like from where you sit?

The Fable/Mythos shutdown wasn't just a security story. It was the first compute-rationing event we got to watch in public.
The quick version: Every frontier lab is out of compute and saying so on the record. Altman: "capacity-constrained for some time." Pichai: "compute constrained in the near term." Amodei: planned for 10x growth, got 80x. Nadella: "I don't have warm shells to plug into." Anthropic's the most exposed of the bunch: no chips of its own, bridging on a short, cancellable lease of xAI's Colossus. Then Fable shipped at 2x Opus and got bumped off subscriptions onto pay-per-use within two weeks. That's not pricing, that's rationing. Then it got pulled on a national-security order, flagged by Amazon. Not a competitor: Anthropic's largest investor. And Amazon went to the government, not to its partner. Anthropic got 90 minutes' notice. Anthropic itself says the capability was already in other public models, so pulling one model contained nothing. I don't think it's a conspiracy. I think it's convergence: a real security concern, an industry-wide compute crunch, an IPO-bound company holding the weakest hand, and a government already in court with that company, all pointing the same way at once. The security event can be 100% real and still do commercial work. It turns "we can't serve this" into "this was too powerful to release." The deeper point: we rank models by benchmarks, but the number that actually governs this era is compute-per-token, and that's the one number nobody publishes.

Curious how FinOps teams are thinking about LLM/agent usage attribution.

The spend number itself seems relatively easy to capture if everything flows through an API gateway, vendor usage export, proxy, or billing report.

The harder part seems to be tying that spend back to the actual work it supported.

For example:

• an agent task fans out into multiple model calls
• the cost lands on whatever service key or proxy path fired the request
• the model call may not know the story, workstream, repo, branch, or business context
• leadership can see total spend, but not what work that spend supported

One pattern I’ve heard is tagging at the orchestration/task layer instead of the individual model-call layer, so the cost follows the outcome rather than just the API request.

For teams dealing with this:

How are you attributing LLM or agent spend today?

Are you tagging usage up front by task/workstream/story/repo?

Are you reconciling it after the fact from logs, traces, Jira/GitHub data, or usage exports?

Or is this still mostly unresolved?

I’m exploring this problem and trying to understand where the attribution layer should live before overbuilding the wrong solution.

I've spent years getting our cloud allocation to a place I'm proud of — tags enforced, showback by team and cost center, unit economics per customer, anomalies caught before they're a board conversation.

Then GenAI spend landed on my desk and every tool and habit I have just… stopped working. Wanted to sanity-check with people who actually do this for a living, because I can't tell if I'm missing something obvious or if the category genuinely isn't built yet.

Here's where it breaks for me:

1. There are no tags. An Anthropic/OpenAI invoice is essentially one number. There's no resource-level metadata like I get on EC2 or a managed DB. So the dimensions I actually need to allocate on — team, cost center, customer/tenant, feature, environment — aren't in the bill at all. I can't chargeback what I can't see.
2. Unit economics are basically unanswerable. "What does customer X cost us in AI?" or "is this feature gross-margin positive?" — questions I answer in my sleep for compute — I currently cannot answer. For an AI feature that's priced per-seat while it's billed per-token, that's terrifying.
3. Closed CLIs are a black box. We rolled out Claude Code / Cursor to the eng org. Leadership asked the obvious question — "what's that costing us per team, per dev?" — and the honest answer is we have no idea. The provider dashboard is one org-wide total.
4. Measured ≠ billed. Even when I meter calls myself, my number never matches the invoice — credits, enterprise discounts, mid-month price changes. Reconciliation is manual and I don't trust it.
5. Anomaly detection doesn't transfer. A token-spend spike looks nothing like an instance-hours spike. My existing thresholds are useless and a runaway agent loop can cost four figures overnight before anything fires.

What I've tried: native provider dashboards (too coarse), routing everything through a gateway and tagging at the call site (works but eng has to instrument every call, and half our spend is in closed tools I can't instrument), and the LLM-observability tools — but those are built for AI engineers debugging prompts, not for finance doing allocation. Wrong buyer, wrong primary number.

So, genuinely asking the people here:

• How are you allocating GenAI spend to teams/customers today? Tag-at-source, proxy, manual spreadsheet, or just… not yet?
• Anyone solved per-developer attribution on Claude Code / Cursor / Codex?
• How do you handle measured-vs-billed reconciliation for token spend?
• Is anyone's existing platform (Vantage / CloudZero / Cloudability / native) actually doing this well, or are you all duct-taping it like I am?

Full disclosure so nobody feels misled: I'm building something in this space, which is why I'm deep in this rabbit hole. I'm deliberately not naming or linking it — I'm not here to pitch, I'm here because I'd rather learn how seasoned FinOps folks are solving this than keep guessing. If you've cracked any piece of this (or you're stuck on the same thing and want to compare notes), comment or DM — happy to share what I've found in either direction.

At the FinOps X keynote this week, SAP's Frederik Pohl and Maida Nazifi showed how they run FinOps for AI at global scale: an AI cost control plane managed by cost per OUTCOME — "because GPUs and LLMs don't behave quite like VMs."

It was the best moment of the keynote, and honestly, the most needed one. The FinOps Foundation recently declared that FinOps now covers ALL technology spend — yet before defining data center unit economics or naming authoritative sources for those metrics, it has pivoted again, to token economics. An arena J.R. Storment's own keynote called a "Wild West." Scope is expanding faster than definitions. SAP's segment was the part you could actually build on.

I was curious what an A.I. benchmark, driven by SAP's cost-per-outcome idea would look like (rather than just quantifying problem solving, long running context, or reading comprehension)… so I ran a series of tests towards a working benchmark:

14 models: closed frontier and open weights, 420 graded document-extraction runs, deterministic grading, no LLM judges, run overnight unattended. One metric: Cost Per Successful Outcome = total dollars spent ÷ answers that actually passed. Failures stay in the bill, because that's how your invoice works.

SAP is right. They don't behave like VMs. At all:

1. Cost per success ranged $0.0002 to $0.59 on IDENTICAL work — 3.5 orders of magnitude. The token price sheet shows only ~70x. Rate cards understate the real economics by 35x.

2. An open-weight model won outright: best pass rate (70%) and lowest cost per success, confidence intervals clear of every frontier model.

3. No model at any price beat 70% on this task set. Every dollar above the cheapest model at the ceiling bought nothing.

4. The priciest model scored 7 points BELOW the winner. Price and quality were uncorrelated across all 14.

Practical payoff: routing this workload to the value leader instead of a frontier model cuts cost per successful document ~99.9% with zero quality loss — a governable decision, IF someone in the room can read cost-per-outcome data.

That someone is FinOps. You can't make a defensible AI value statement to the business from a price sheet and a leaderboard — the real economics live in the gap between them, and reading that gap is the new core skill. One keynote slide became a working benchmark in a night; the measurement discipline is buildable NOW, by practitioners, without waiting for a standards body to finish the vocabulary.

Full analysis, ranking table, confidence intervals, and the honest caveats https://www.realtimecost.com/benchmark

[ Removed by Reddit on account of violating the content policy. ]

Please don’t just recommend your own company or a tool you sell.

I’m trying to get a real practitioner point of view before booking demos.

We’re spending around 50% of our MRR on AI and cloud infrastructure right now.

Most of that is still tied to AWS, Azure, and GCP.

So I’m not only asking about LLM API tracking.

I’m more interested in the full cloud cost picture:

* GPU workloads

* Kubernetes costs

* training vs inference

* storage

* data transfer

* shared environments

* cost allocation by team/product

* anomaly detection

* showback

* forecasting

I’m currently aware of Finout, PointFive, and CloudZero.

I’m sure I’m missing others.

For people actually using FinOps tools in production:

Which tools are genuinely strong for this kind of AI-heavy spend?

Which ones are overhyped?

Are native cloud tools enough if most spend is AWS/Azure/GCP?

Or do third-party platforms become necessary once cloud and AI spend gets this high?

Would love practical opinions, even if the answer is “we tried tools and ended up building our own.”

Tokenomics featured question: Are you looking into implementing an AI Gateway (token routing software) for an upcoming project to manage multiple LLM APIswith the goal to avoid vendor lock-in, handle fallback redundancy, and dynamically route prompts to optimize costs (e.g., sending simple tasks to cheaper models and complex ones to frontier models).

Emerging solutions/products like LiteLLM, Portkey, OpenRouter, and Manifest are frequently seen as the result of searching, but we wanted to get some real-world feedback from people running these in production.

If you are currently using a routing solution, we'd love to get your thoughts on a few things:

1. Self-Hosted vs. Managed: Are you self-hosting an open-source gateway (like LiteLLM) or using a managed enterprise solution (like Portkey)? What drove your decision (latency, security, compliance)?
2. Routing Logic & Latency: How do you handle the actual routing logic? Are you using static semantic routing, or do you have a dynamic "judge" model evaluating prompts first? If the latter, how bad is the latency hit?
3. Fallback & Reliability: How reliably do these gateways handle rate limits (429 errors) and automatic failovers to backup models or providers?
4. Token/Budget Management: How accurately do they track token spending and enforce team/user quotas in high-throughput environments?
5. The "Gotchas": What unexpected headaches or limitations did you run into after deploying your gateway?

Ww would love to hear any recommendations, warnings, or architectural advice you have. Thanks!

We're seeing GenAI moving from novelty to necessity, and Enterprises are becoming increasingly aware that AI in all its forms is becoming a significant percentage of their spend.

Knowledge on how to control, measure, and optimise this emerging cost is the new frontier for FinOps and we are faced with organisations asking what the plan is.

The community appears to be finding door closed when asking for help and assistance, so are we in the phase where consultancies know the answers, but do not wish to share them as it can be monitised?

Or, are there good communuity and open sources for helping people to 'cope' with this new FinOps challenge?

Just like everyone else, I've been seeing the recent news about how AI bills have been skyrocketing for companies. I've been seeing people Reddit posts / comments about how their companies have done a full 180 from "use AI for everything" to "limit AI usage as much as possible".

So I've been wondering - what mechanisms are companies actually using to monitor and control AI costs intelligently? I know the most basic version of this is just seeing your bill at the end of the month, having a heart attack, and then telling employees to stop using AI. But there must be a smarter way to do this right?

Is there some way to track AI usage across departments, task types, and employees (across different LLM providers?). Can managers set limits on what they want their AI budget to be so that you don't get an unexpectedly high bill? Maybe then you could switch low-priority departments or tasks to cheaper model or just stop allowing AI usage for that department for the rest of the month

Just curious on why AI bills are so shocking to people - I assume people are setting hard caps on token usage.

**TL;DR:** Starting next Monday, r/Tokenomics is officially transitioning its focus from blockchain/crypto tokenomics to **AI Token Economics** (LLM API costs, compute economics, prompt optimization, and AI infrastructure). Crypto-centric posts will be redirected to dedicated crypto subreddits, and we are updating the mod team to reflect this shift.

Hey everyone,

If you’ve been here a while, you know this subreddit was originally built around the economics of blockchain tokens—discussing supply curves, staking mechanics, and DeFi ecosystems. But as technology shifts, the vocabulary shifts with it.

Today, the word "token" has taken on a massive new meaning. In the era of Large Language Models (LLMs), a "token" is the fundamental unit of compute, context, and cost. The economics of how these tokens are priced, generated, and optimized is arguably the most important economic discussion in tech right now—and there isn't a dedicated hub for it.

Because the blockchain discussion is already incredibly well-served by massive communities like r/CryptoCurrency, r/CryptoTechnology, and r/defi, we have decided to repurpose r/Tokenomics to fill this critical gap in the AI space.

### 🔄 What This Means for the Subreddit

Starting **Monday**, we are officially shifting the subreddit's purpose to **AI Token Economics**.

Here is what we *will* be discussing moving forward:

* **API Cost Comparisons & Strategies:** Evaluating the cost-to-performance ratio of models (e.g., GPT-4o vs. Claude 3.5 Sonnet vs. Gemini 1.5 Pro).

* **Prompt Optimization:** Techniques to compress context windows, save tokens, and reduce enterprise or personal API bills.

* **Compute Economics:** The physical layer of AI tokenomics—GPU market dynamics, the cost of training vs. inference, and cloud compute pricing.

* **Local vs. Cloud Economics:** Cost analyses of running open-source models (Llama 3, Mistral) locally versus paying for proprietary API access.

* **The Future of Agentic Economies:** How autonomous AI agents will transact, budget, and optimize their own token usage.

### 🛑 What is No Longer Allowed

To make room for this new direction, we are phasing out the old one. Starting next week, the following will be removed:

* Standard cryptocurrency/altcoin analysis.

* DeFi yield mechanics, staking discussions, and ICO/presale announcements.

* Any form of crypto shilling or blockchain price speculation.

*(Note: We will still allow discussions around decentralized compute networks like Render or Akash, provided the focus is strictly on the economics of the AI compute being provided, not token price speculation).*

### 🛠 Moderation Changes

To enforce this, we are overhauling the backend:

1. **New Rules & Automod:** We are updating the rules in the sidebar and tuning Automod to filter out crypto-spam and generic airdrop bots.

2. **New Flairs:** We will be rolling out new post flairs (API Costs, Compute Infrastructure, Prompt Optimization, Discussion).

3. **Mod Team Updates:** Several of our legacy mods are stepping down, and we are bringing on a few new moderators with backgrounds in machine learning, API development, and software economics. *(If you are an AI dev or infra engineer interested in modding, our DMs are open).*

### Moving Forward

We know subreddits pivoting can be jarring. If you are strictly here for blockchain content, we want to thank you for building the community up to this point, and we encourage you to migrate to the excellent crypto subs already out there.

For the developers, founders, AI enthusiasts, and system architects trying to figure out how to scale AI without going bankrupt—welcome home.

Let us know your thoughts, suggestions for the new flairs, or what you'd like to see in the new wiki below!

— *The r/Tokenomics Mod Team*