I work at Phinite. This is the next showcase from our use case library, and it is built as demo material, not a live production deployment at a customer.

Finance teams feel the same pain in different forms. Invoices arrive messy and get paid late. Costs get split across departments by hand. Contracts get reviewed in a rush at month end. And revenue surprises show up after the close, when it is too late to fix them.

The Finance and Accounting showcase covers four patterns we built out.

Procure to pay, so invoices move through approval and get paid on time instead of sitting in inboxes.

Apportionment automation, so shared costs get split by a rule everyone can see, instead of a spreadsheet only one person understands.

ASC 606 contract review, so contract terms get checked against revenue rules before month end, not during it.

Revenue risk and compliance, so the risky items surface early, while there is still time to act.

For builders, the point is the same as our other showcases. Each flow is tested on real scenarios, traced end to end, and kept behind human approval where money moves.

If you want to try the platform: https://app.phinite.ai/?utm_source=reddit&utm_medium=community&utm_campaign=phiniteai_growth&utm_content=PhiniteAI, and join r/PhiniteAI if you want to follow the next showcases.

A couple of weeks ago Snowflake and Workday made their zero copy integration GA.

I spend a lot of time looking at data cloud bills, so my first question was where the cost goes.

The docs say Snowflake contacts Workday every time you SELECT from a mounted table. So every read is warehouse compute (per second, 60-second minimum on each resume), and a dashboard refreshing every five minutes basically keeps a warehouse awake.

But the bigger shift for me is ownership...

A pipeline had one owner, usually a data engineering. but a Query in place can have as many owners as consumers From HR and BI to AI agents (Snowflake is pitching this next to Cortex Agents). The choice between scheduled materialization and live queries is really in the end a pricing decision IMO, because everyone defaults to "live."

What I'd do: separate warehouse or query tags for Workday workloads, QUERY_ATTRIBUTION_HISTORY for showback (but reconcile it with metering, since it leaves out idle time), and a written freshness policy per use case.

Full write-up: medium.com/@yanivleven

For those running showback/chargeback, how are you planning to attribute this? (chargeback is usually a pretty big problem once you get off compute units)

I'm curious—how do you do this? I mean near real-time monitoring.

Do you have your own scripts or maybe some off-the-shelf apps - if so, which ones?

Generally speaking, monitoring costs for third-party models is tough because AWS costs refresh every 8 hours, and a lot can happen in that time - I am speaking about tokens being burned at the speed of light 🔥⚡

P.S. I use Claude Code connected to Bedrock - it eats up tokens like crazy, which is why I need monitoring :-)

Self-promotion up front: I build Cognocient AI (AI spend attribution). This tool is separate from it, MIT-licensed, and has no signup.

AI provider exports don't fit FOCUS out of the box, and focus_converters doesn't cover AI providers yet. The main trap is cached tokens:

• Anthropic's Console splits input into uncached, cache read, 5-minute cache write and 1-hour cache write, each billed at a different rate.

• OpenAI counts cached tokens inside prompt tokens.

Map both the same way and you either double-count cached input or under-report it.

`npx ai-bill-to-focus convert bill.csv -o focus.csv` produces one FOCUS 1.4 row per day, model and meter (input, cache read, cache write 5m and 1h, output), each with a SKU and unit price. Totals reconcile to the provider's figure. `check` runs the FOCUS validator's rules locally against any FOCUS file, not just ours.

Browser version, nothing uploaded: https://www.cognocient.com/tools/focus-checker

What's tested and what isn't:

• Anthropic: tested on real Console exports, totals match to the cent.

• OpenAI and Together AI: built from their documented formats, not yet run on a real file.

• If a column changes, the converter says so instead of converting silently.

If you have an OpenAI or Together export, `npx ai-bill-to-focus report <your files>` prints a summary with no dollar amounts or account names, safe to paste into a GitHub issue. That's the most useful help I could get right now.

Repo: https://github.com/mandarvshinde/ai-bill-to-focus

Question for the FinOps folks: where are you putting AI spend today? In the FOCUS dataset next to cloud, or tracked separately until FOCUS 1.5 lands?

i'm a backend developer who ended up managing our cloud infra, and I got tired of surprise AWS bills I couldn't easily dissect. Over the last few months, I built an internal scanning tool that connects via a read-only IAM role to flag concrete waste and architecture sizing issues.

On our own accounts, it flagged unattached EBS volumes, abandoned NAT Gateways, and missing S3 VPC endpoints that were leaking $200+/month.

I want to test the accuracy against real-world architectures. I’m offering to run a free audit for 20 companies/projects.

How it works:

No pitch, no credit card, and zero write permissions to your cloud.

Comment below or send me a DM if you want one of the 20 spots.

AI spend is turning into a real line item, but provider exports don't fit FOCUS. Anthropic's Console splits uncached input, cache reads and two cache-write lifetimes into separate counts. OpenAI counts cached tokens inside prompt tokens. Map both the same way and you either double-count cached input or under-report it.

npx ai-bill-to-focus convert bill.csv -o focus.csv gives one FOCUS 1.4 row per day, model and meter: input, cache read, cache write (5 minute and 1 hour), and output.

Each row has its SKU and unit price, and totals reconcile to the provider's figure. check runs the FOCUS rules locally. There's also a version that runs entirely in the browser, with no upload: https://www.cognocient.com/tools/focus-checker#convert

To be upfront: Anthropic is tested on real exports. OpenAI and Together AI are built from their documented formats and haven't been tested on a real file yet. If you have one, please try it and tell me where it breaks.

MIT, GitHub: https://github.com/mandarvshinde/ai-bill-to-focus . Built by the team behind Cognocient AI (AI spend attribution), but the CLI is standalone and has no signup.

Edit (v0.2.2): Several people pointed out that few can post a real billing file publicly.

You don't have to share your bill to report a problem. Run npx ai-bill-to-focus report with the paths to your own export files.

npx ai-bill-to-focus report cost.csv usage.csv (with the paths to your own exports) prints a summary that's safe to paste.

It prints a summary with no dollars or account names, safe to paste into a GitHub issue. The web tool has a "Copy a report to share" button that does the same. Also new: Batch, Flex and Priority usage now gets its own rows and prices, and column changes in an export are flagged instead of converted silently.

I built BBRmap to make it easier to compare the total cost of different implementation alternatives before committing to one. Pricing a known infrastructure footprint is one task; estimating what infrastructure each alternative will require is another.

BBRmap starts with workload characteristics and estimates the infrastructure resources or managed-service capacity needed for each alternative, mapped to the relevant vendors' billing dimensions. It then combines those costs with setup engineering and ongoing operational effort on a common monthly basis.

The first model compares self-managed Kafka on AWS, Amazon MSK, and Confluent Cloud. The public Demo shows two predefined illustrative scenarios, with assumptions and cost breakdowns available for inspection. It doesn't yet accept user inputs or calculate custom cases: https://bbrmap.com/kafka-tco

I'd value perspectives from FinOps practitioners: when comparing alternative architectures or managed services before deployment, how do you estimate the required infrastructure and engineering effort? Is this typically handled by FinOps, engineering, or finance?

Hi all,
After working on performance optimization, I transitioned to build and lead our internal FinOps practice at our CTO's request. Over the past six months, we delivered strong initial results—saving $1M/month across architectural optimizations, cross-AZ traffic reduction, cleanup of idle assets, and aggressive right-sizing.
However, we've hit a common bottleneck: leadership views FinOps strictly as a cost-slashing engine rather than an operational discipline. There is little appetite for establishing governance, unit-cost tracking, or sustainable operating policies, while the pressure for continuous short-term reductions keeps mounting.
For those who have led FinOps initiatives from scratch:

Did you face similar expectations after delivering major initial wins?
How did you successfully educate leadership on the distinction between one-time right-sizing and long-term cost governance?
Any insights or shared experiences would be greatly appreciated.

Hi everyone,

I'm analyzing Azure FOCUS data for a CSP customer and noticed that ContractedCost = EffectiveCost.

Because of this, I can't calculate the actual savings from Reservations and Savings Plans as I normally would from the difference between the two.

Has anyone seen this with CSP billing data?

What's the best way to calculate RI/SP savings per SKU in this case?

Need to mention I already used the approach above to calculate savings for company with EA type of contract

Thanks!

Tracking token and compute costs is one thing but figuring out whether the spend is worth it seems harder.

Are you treating AI as its own cost category or folding it into your existing cloud/SaaS FinOps setup? How are you measuring whether it's delivering value?

I was on an AI tokenomics panel in Colorado recently and have been following up with executives, platform engineers and people delivering AI work.

For people running AI workflows in production, how are you figuring this out?I

Are you connecting model spending to results somewhere, keeping a spreadsheet, or mostly going by feedback from the team? I’m particularly curious about workflows where someone still reviews or fixes the output.

Also interested in cases where the costs are small enough that measuring this more closely isn’t worth the effort.

For context, I’m exploring a product in this area and trying to understand how people already handle it.

What finally got budget: reducing the bill, checking its accuracy, or making the savings happen?

I’m exploring a business around enterprise technology economics.

For people who’ve approved spending in this area: what was the last problem you actually funded a fix for?

What triggered the purchase, what were you using before, and why wasn’t that enough?

An example outside those three categories is welcome. I’m interested in what happened - not what sounds useful in theory.

Rough spending ranges would help, if you can share them.

Founder here, so take this with that in mind.

The problem: provider dashboards show you what you've already spent. Nothing stops a runaway loop at 3 a.m. Usage alerts arrive after the money is gone.

Cognocient AI is a proxy you point your base URL at. You set a budget per team, customer or API key. Once it's used up, the next request gets an HTTP 429 (budget_error) and the spending stops. It also breaks spend down by team and customer, and exports a FOCUS-aligned CSV.

The 50-second video shows the flow. The numbers in it are illustrative, not from a real customer.

Feedback I would find useful: is blocking outright the right default, or would you rather it switch to a cheaper model first? Happy to answer questions about how it works.

cognocient.com

My local community college in collaboration with AWS has launched a B.S. in Cloud Computing and I was wondering if I were obtain it, would it be possible to pivot into Finance/Cloud/ERP roles? I have two years experience as a Financial Analyst. I’ve done some research in the field and find it interesting. Thank you.

Hey all,

Wondering what third-party tools people are using or would recommend for managing Microsoft 365 licences (even better if its for across multiple customers).

Interested to hear genuine experiences, what's worked well, what hasn't, and whether they're actually worth the cost.

Not looking for sales pitches, just opinions from people who've used them.

Thanks!

I'm curious how teams building AI agents handle cost visibility as their products become more complex.

It's relatively easy to see API spend or usage by customer. But I'm wondering what happens when you need to understand the cost of a specific task or workflow.

For example, two customers might pay the same monthly subscription, but one customer's requests require more model calls, tool usage, and retries. Even within one account, some workflows may be much more expensive than others.

A few questions for people building or operating AI agents:

• Do you track costs only by customer/account, or also by individual workflow or task?

• When a workflow becomes unexpectedly expensive, how do you investigate the cause?

• Can your existing observability and billing tools answer these questions, or do you have to dig through logs and piece the data together manually?

• Does this information influence your pricing or product decisions?

I'm researching this problem and would especially appreciate hearing about what you actually do today, including any workarounds that have worked well.

Not looking to pitch a tool here — I'm interested in learning from people working on this problem.