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Now Hiring: Cloud & FinOps / Azure Architect (Remote) We are looking for an experienced Cloud & FinOps / Azure Architect to lead the governance, architecture, security, and cost optimization of our Microsoft Azure environment. This is a strategic leadership role for someone who has strong expertise in Azure architecture, cloud financial management, enterprise data platforms, and cost optimization. You will help design secure, scalable, and cost-efficient cloud solutions while managing large-scale Azure environments with extensive historical data. Key Responsibilities: Lead Azure architecture, governance, and cloud strategy Design secure, scalable, resilient, and cost-effective Azure solutions Develop and implement Azure FinOps strategies for cost visibility and optimization Optimize Azure spending across storage, compute, backup, licensing, and reserved capacity Create data lifecycle strategies for large-scale historical data environments Design and optimize Azure Data Lake, Azure Databricks, CDP, and data integration platforms Improve cloud efficiency through storage optimization, archiving, automation, and cost monitoring Collaborate with Engineering, Data, Security, Finance, Microsoft, and cloud partners Required Skills & Qualifications: Extensive experience with Microsoft Azure enterprise environments Strong expertise in Azure Architecture and Azure Governance Proven experience with Azure FinOps / Cloud Cost Optimization Hands-on knowledge of: Microsoft Azure Azure Storage & Lifecycle Management Azure Data Lake Azure Databricks Enterprise Cloud Architecture Cloud Security Azure OpenAI Experience managing large-scale enterprise data estates (10+ years of historical data) Strong understanding of cloud budgeting, forecasting, cost management, and optimization Experience designing scalable and cost-efficient data platforms Excellent communication and stakeholder management skills Preferred Qualifications: FinOps Foundation Certification or equivalent Experience with Customer Data Platforms (CDP) such as Amperity Knowledge of Master Data Management (MDM) and data governance frameworks Experience with enterprise analytics and large-scale data ecosystems Interested candidates are encouraged to apply! Join us and help shape the future of enterprise cloud architecture and FinOps optimization. Send your cv: [email protected] . submitted by /u/Ok_Feedback_8999 to r/FinOps [link] [comments]

I'm running FinOps for a mid-sized org and I keep hitting a wall with Azure cost forecasting. Cost Management & Billing is fine for looking at past spend, but the native forecast just extrapolates recent usage forward it has no idea we're launching a new product next quarter or decommissioning a legacy app, so the numbers are basically not useful for real budgeting. A few specific problems I can't seem to solve cleanly: Forecasts ignore future plans. How do you factor in known upcoming changes (migrations, launches, traffic growth) instead of just trending historical data? Multi-subscription sprawl. We have dozens of subscriptions and aggregating them into specific teams and forecasting their cost is not possible. Executive reporting. Leadership doesn't care about "VM cost vs storage cost" they want budget vs. actual, MoM/QoQ trends, cost per team/product, and savings from optimization. How are you turning raw Azure data into something a CFO actually reads? submitted by /u/ZeertY26 to r/AZURE [link] [comments]

just got our monthly invoice and I almost choked 40% increase. nothing changed. same workload, same number of calls, same storage. but somehow we're paying 40% more than last month. I've spent the last 2 days digging through the billing dashboard. network egress. it's always network egress. we're serving recordings and call logs to agents all over the world and apparently that costs money a lot of money. I'm looking at options compressed storage different regions. maybe even move to cold storage for older recordings. but I'm not sure if that's going to actually save us money or just shift the cost somewhere else submitted by /u/mitchare to r/googlecloud [link] [comments]

Hey everyone, Tired of seeing bloated AWS bills from orphaned EBS volumes, idle staging databases, and unmanaged S3 buckets across regions, I built a fast, lightweight, and 100% free cloud waste auditor: [ https://finopsai.space\](https://finopsai.space) Key Features: \- 8 Enterprise Pillars Audited: Orphaned EBS volumes (GP2/GP3/IO1/IO2), non-prod RDS Multi-AZ redundancy (50% cut), zero-node EKS/ECS clusters, unassociated Elastic IPs ($3.60/mo each), idle NAT Gateways ($32.40/mo), stopped EC2 instances, and S3 lifecycle rules. \- 1-Click IaC Remediation: Instead of just showing graphs, it generates production-ready Terraform (cleanup\_plan.tf) and safe Bash scripts with Dry-Run protection. \- Production Immunity Shield: Automatic guardrails ignore anything tagged with 'Environment: prod' or attached to active production network interfaces. \- 100% Read-Only & Zero-PII: Ephemeral in-memory analysis; zero credentials or client metadata saved to any database. \- Multi-Region Support: Multi-threaded parallel scanning across all 17 global AWS regions with localized pricing multipliers. Try the interactive demo or connect your account in read-only mode: [ https://finopsai.space\](https://finopsai.space) Would love your brutal feedback, architecture critiques, and suggestions on what checks I should add next! (Live Tool: [ https://finopsai.space\](https://finopsai.space) | Built by Shobhit Singh) submitted by /u/iTs_Kie to r/FinOps [link] [comments]

Im convinced microsoft changes the portal layout every 3 days just to mess with our muscle memory Management asked for a simple breakdown of last months cloud spend by department and ive been clicking through this maze for two hours. The cost analysis tags arent filtering correctly and half the resources just say "unassigned" in the billing export We recently had some custom app services deployed by a vendor cisin and tracking those specific resource groups is literally the only part of our bill that makes sense right now. Everything else our internal team spun up over the years is just a wild west of un-tagged storage accounts and random NAT gateways that nobody wants to claim ownership of how do you guys actually enforce tagging policies without going insane? tbh im ready to just start deleting things until someone screams submitted by /u/gdbbdg to r/AZURE [link] [comments]

I work on cloud cost optimization for AI/infra startups, and this was one of the worst bills I took over recently. Sharing the full breakdown because 90% of it is a pattern I see everywhere. The client: a mid-size SaaS on AWS. $14,200/month. Mostly "fine", rooms of it was not fine. The four biggest leaks: EC2 instances over-provisioned and in the wrong region. An ap-southeast-1 fleet for a us-central user base. ~30% over on size, wrong region entirely. Traffic paying for NAT gateway transit. 3 NAT gateways routing S3 and DynamoDB through the public internet at $0.045/GB. They didn't need to — it was all internal data transfer. RDS sized for peak, idle at 3am. A db.r5.xlarge running at full capacity 24/7 even when nothing was on. Combined the transactional + vector DB into one RDS pgvector cluster. No CDN, no right-sizing, orphaned resources. Every API response served from ALB. 3 unattached 500GB EBS volumes, 2 floating Elastic IPs, a dead NAT gateway from a deleted VPC. What I did (3 weeks, no downtime): - Karpenter spot autoscaling on EKS (sub-minute node provisioning) - Consolidated pgvector (RDS) into the primary ACID engine - VPC gateway endpoints + S3 direct access (zero NAT transit for internal) - Right-sized everything + cleared orphaned resources Before: $14,200/mo After: $3,800/mo (73% saved) p95 latency dropped 1,450ms → 420ms. SLA held at 99.98%. The full architectural breakdown is here if you want it: https://hasanbutt.com/ (section: FinOps teardown) Happy to answer questions on the teardown in the comments submitted by /u/butt096 to r/aws [link] [comments]

We spend a lot of time on unit economics for cloud, and AI spend has been resisting the same treatment. Per-token prices converged at the frontier (Claude Opus 5 and GPT-5.6 Sol both charge $5/M input), so the price sheet stopped being the decision. The number that matters is cost per completed task: cost per attempt, divided by success rate, plus the cost of wrong outputs that escape your checks. The measurement approach that worked for us, condensed: Log tokens per trajectory, not per call. Task ID on every request; sum input, cache read, cache write, reasoning, and output tokens across the whole loop. Record the outcome per task, solved or failed by an automated check, and count retries. Compute cost per solved task: total trajectory dollars divided by solved tasks. Track a leak rate: sample "passed" tasks, have a human grade them, multiply by your loaded cleanup cost. Report cost per resolved ticket / merged PR / correct action, by model and effort setting. Split batchable from interactive work at design time; Batch APIs are 50% off on both vendors. The hard part in production is attribution: tokens don't carry tags, and a gateway fronting all LLM traffic makes the bill useless for allocation. That problem is why DoiT (where I'm CEO) acquired Attribute, which does this at the kernel level with eBPF. Disclosure accordingly: we sell cost tooling, read everything above with that bias in mind. The spreadsheet version works fine to start. Full analysis with worked examples and current August 2026 pricing: https://www.doit.com/blog/cost-per-task-vs-cost-per-token Curious what unit metrics people here have actually gotten to stick for AI workloads. submitted by /u/vadimska to r/FinOps [link] [comments]

ꓮѕѕսmіոց уеаr оոе іѕ оսtꓲіոеd іո tһе fоꓲꓲоԝіոց mаոոеr: ꓮꓲꓲ fасеtѕ оf dіѕсоνеrу ԝеrе frее аոd tһе ехесսtіоո ԝоrkѕһорѕ ԝеrе զսіtе mаոаցеаbꓲе. ꓴոdеrѕtаոdіոց сrурtоցrарһіс mаtеrіаꓲѕ ꓲеd tо іdеոtіfісаtіоո оf 600 аѕѕеtѕ аոd раѕѕіոց оf 80 ѕуѕtеmѕ іոtо tһе раіd ѕtаցе оf ցеttіոց fіхеѕ аррꓲіеd, ԝіtһ ассеѕѕ tо аցеոtѕ аոd ореոеd-сꓲоսd ѕеrνісеѕ соmіոց bу ԝау оf а rеꓲаtіνеꓲу іոехреոѕіνе ѕսbѕсrірtіоո. ꓮt tһіѕ роіոt, tһе ѕресіаꓲіѕt tеаm һаѕ ոо сһоісе bսt tо соореrаtе ԝіtһ tһе ѕеrνісе рrоνіdеr tо соոdսсt роꓲісу аѕ ԝеꓲꓲ аѕ аսdіt-rеꓲаtеd fսոсtіоոѕ. ꓪіtһ tһе ѕесоոd уеаr соmеѕ tһе еոd оf tһе rеոеԝаꓲ рrосеѕѕ fоr tһе рrоνіdеr’ѕ ꓮꓪꓢ еոtіtꓲеmеոtѕ. ꓪһаt іѕ ꓲеft іѕ tо оbtаіո tһе ѕtаtսѕ оf іոνеոtоrу, rеցսꓲаtоrу роꓲісу, аսdіt һіѕtоrу аѕ ԝеꓲꓲ аѕ ꓲауеrѕ оf рrоtесtіоո. ꓪһаt соսꓲd bе tһе mоѕt rеаѕоոаbꓲе ехіt орtіоո? ꓔһіѕ іոզսіrу һаѕ аrіѕеո dսе tо tһе սѕе оf tһе qսꓢесսrе qսꓑrоtесt tесһոоꓲоցу, ԝіtһ tһе dіѕсоνеrу рrосеѕѕ bеіոց рrоνіdеd tо оrցаոіzаtіоոѕ fоr frее, аոd ԝіtһ tһе ցеոеrаꓲ рꓲаtfоrm есоոоmу bаѕеd оո ѕսbѕсrірtіоոѕ аոd fееѕ fоr аցеոtѕ. ꓧоԝеνеr, ꓲ һаνе ոоt аttеmрtеd tо mаkе սѕе оf іt. ꓔо bеցіո ԝіtһ ꓲd ꓲіkе tо һаνе tһе ехіt ѕtrаtеցу сꓲеаrꓲу ехрꓲаіոеd tо mе іո ԝrіtіոց. ꓲ ԝоսꓲd ꓲіkе tо kոоԝ аbоսt tһе ехроrt fоrmаt սѕеd fоr ꓚꓐꓳꓟ аոd һіѕtоrісаꓲ dаtа, tһе рrіոсірꓲеѕ оf оԝոеrѕһір оf tһе еոһаոсеd аѕѕеt іոfоrmаtіоո, һоԝ аցеոtѕ fսոсtіоոаꓲꓲу сһаոցе іո tһе роѕt-ехріrу реrіоd, ԝһеtһеr tһе ехіѕtіոց ցаtеԝауѕ rеmаіո еffесtіνе, һоԝ mսсһ іt ԝіꓲꓲ соѕt tо tеrmіոаtе tһе соոtrасt, ԝһеtһеr ꓲ ԝіꓲꓲ rесеіνе ꓲоցѕ аոd еνіdеոсе оf соmрꓲіаոсе, роѕѕіbіꓲіtу оf роꓲісу trаոѕfеr tо аոоtһеr ѕеrνісе рrоνіdеr. ꓟоѕt mоdеꓲѕ սѕеd tо саꓲсսꓲаtе tһе tоtаꓲ соѕt іոсսrrеd іո tеrmѕ оf tһе ореrаtіоո ѕуѕtеm аrе еffесtіνе іո соmрսtіոց mеdіսm оf аdорtіоո. ꓔһеу аrе ԝеаk іո ассоսոtіոց fоr rеνеrѕіbіꓲіtу. submitted by /u/DigRealistic3670 to r/FinOps [link] [comments]

Hey everyone, ​I wanted to share a small but huge personal milestone: after weeks of building, debugging, and testing, I just logged my first sale on Gumroad! ​As someone building in the cloud space, nothing beats the feeling of seeing a real user find value in something you created from scratch. ​Why I built it ​Managing cloud infrastructure often leads to "cloud drift" and silent budget leaks. It's surprisingly easy to overlook: ​Zombie VMs & idle EC2 instances running 24/7 without active workloads. ​Unattached volumes & orphaned snapshots accumulating hidden storage charges. ​Overprovisioned resources that could easily be downscaled without impacting performance. ​Most enterprise FinOps suites are bloated, expensive, and require complex enterprise agreements just to see where your money is bleeding. ​I wanted something lean and direct: an AI-assisted CloudOps script/tool that plugs straight into AWS and Azure, scans your environment read-only, and gives you an actionable report of exactly what’s idle and how much you can save by pruning it. ​What’s next ​Getting that first customer notification made all the late nights worth it. My next focus is refining the detection models and adding support for automated scheduled cleanup reports. ​If your team is trying to trim cloud waste this quarter, or if you're curious about how the scanner identifies idle resource patterns, feel free to drop a comment below or send me a DM—happy to share insights, run a quick check, or swap notes with fellow builders! submitted by /u/Arima247 to r/FinOps [link] [comments]

Your AWS, Azure & GCP costs could be hiding thousands in unnecessary spend. LessBill uses AI to uncover waste, identify savings opportunities, and help teams take control of cloud costs. 🤖💰 🚀 The opportunity is here. Go grab it. Try LessBill → lessbill.org Building in public. Looking for founders, startups, DevOps teams, early users & investors to join us early. #LessBill #AI #CloudComputing #CloudCostOptimization #FinOps #AWS #Azure #GCP #DevOps #SaaS #Startups #StartupIndia #BuildInPublic #Founders #Entrepreneurship #VentureCapital #Cloud #ArtificialIntelligence #YCombinator #Techstars #500Global #Antler submitted by /u/rushi2105 to r/FinOps [link] [comments]

I've been looking into this recently, and tagging is probably the most common advice I keep coming across when it comes to allocating cloud costs. And it makes sense, if everything is tagged properly, it should be much easier to understand who owns what. But then I started wondering: how often does everything actually stay tagged properly? Tags get missed, conventions change, some resources don't support the same dimensions, and you can still end up with spend that doesn't have an obvious owner. So, I'm curious what people have found in practice. Is strict tagging enough, or does a good cost allocation setup usually need account structure, automation, or something else alongside it? P.S. Also curious about tools here, has anyone used a cloud cost visibility tool that can actually help uncover where the leakage is when tagging isn't perfect? Would be great to hear what you've tried and whether it genuinely helped. submitted by /u/whispered_word12 to r/FinOps [link] [comments]

I built CloudPouch 2.0, a local-first AWS FinOps desktop app, and I'd value technical feedback from people doing FinOps in practice. The problem I'm trying to solve: AWS already exposes cost data and recommendations, but turning them into a prioritised, explainable list of actions can still mean jumping between several services and tools. CloudPouch runs deterministic checks locally against the AWS profiles the user already controls. The new Savings Workspace shows the affected resource, evidence, recommended action and estimated monthly savings. A local, read-only MCP server can then let an AI agent help prioritise those results. It does this without sending billing data to another SaaS. I'm the maker, so this is self-promotion. I'm specifically looking for criticism, not praise. For people doing FinOps in practice: Which part of this workflow is genuinely useful, and which part is already solved well by AWS-native or existing FinOps tools? What evidence would you need before trusting a savings recommendation? Does the local MCP angle solve a real problem, or create more complexity than value? I can share a screenshot and the beta link in the comments or by DM if that is permitted here. submitted by /u/cloud-fanboy to r/FinOps [link] [comments]

Been building a way to diagnose how well founders actually understand their AI costs, not just "what do you spend," but whether you know who's driving it, how fast you'd notice a spike, and whether you could act on it. Drop your answers below (just the letters, e.g. 1B 2A 3C 4B 5A 6D 7C 8B ) and I'll run them through the scoring model and reply with your personalized breakdown, a score out of 100, a dimension-by-dimension chart, and your single biggest blind spot. Here's a sample of what you'll get back: https://preview.redd.it/e3r7xs2xjqkh1.png?width=1058&format=png&auto=webp&s=2d05f3e78488ca330fd6db42573d5486e3f488b2 Q1: Spend. Right now, without looking it up, do you know your total AI spend this month? A) No idea B) Could probably estimate C) Could find out manually D) I track this regularly E) I know automatically Q2: Drivers. Do you know which features or workflows are responsible for the biggest share of your AI cost? A) No idea B) Could probably estimate C) Could find out manually D) I track this regularly E) I know automatically Q3: Customers. Do you know which customers or user segments are driving the most AI spend? A) No idea B) Could probably estimate C) Could find out manually D) I track this regularly E) I know automatically Q4: Features. Do you know the per-call cost for each of your AI-powered features? A) No idea B) Could probably estimate C) Could find out manually D) I track this regularly E) I know automatically Q5: Profitability. Do you know whether each AI-powered feature generates more revenue than it costs to run? A) No idea B) Could probably estimate C) Could find out manually D) I track this regularly E) I know automatically Q6: Change. When your AI costs spike or drop significantly, how quickly do you find out? A) Usually at month-end, from the invoice B) After a few days, if I notice C) Weekly, when I check manually D) Within 24 hours E) Real-time automated alert Q7: Action. When you discover a cost problem, how quickly can you make a change? A) I'd need to investigate before doing anything B) A few days, after digging C) Within a day, once I know what to change D) I have a playbook, hours E) Automated responses / tight SLAs Q8: Automation. How automated is your AI cost monitoring today? A) I check the monthly invoice, that's it B) Some dashboards, checked inconsistently C) Regular reports or exports D) Dashboards I check most days E) Automated alerts, real-time visibility Reply format: just the 8 letters in order, e.g. 1B 2A 3C 4B 5A 6D 7C 8B, I'll score it and reply with your card. submitted by /u/Neat-Ad-4224 to r/FinOps [link] [comments]

The FinOps Agent Google dropped in GoogleCloudPlatform/professional-services/examples/FinOps-agent has been sitting there since June 3rd. One commit, one author, no updates. Wanted to know if anyone actually put it into prod. The architecture is four ADK sub-agents behind a root orchestrator. NL2SQL over BigQuery for the billing side, Google Agent Search over policy PDFs for the qualitative side, a chart specialist, and a synthesis writer. Runtime is Gemini 2.5 Flash on Vertex AI, FastAPI on 8080 for streaming SSE. Ran it in a lab project against synthetic billing data. NL2SQL was better than expected against a normalized schema. RAG over a real policy doc surfaced the right clauses about 70 percent of the time, close to what similar demos land on. The blocker for real use is session state. InMemorySessionService from google.adk.sessions means every conversation resets on pod recycle. Swapping in a Firestore-backed SessionService subclass is doable, but once you own that surface you own the schema, the TTL, and the migration path. Not one line of code. Two things I want to hear from people who took it further: How did you handle session persistence? Custom SessionService subclass to Firestore or Cloud SQL, or something else? Did you keep policy as freeform PDFs, or did you move to a structured schema so the agent could run rule checks instead of RAG extractions? Not looking to dunk on the reference. Sample data included, license is Apache 2.0, and it's a reasonable multi-agent skeleton. Just curious if anyone got it past the demo. submitted by /u/matiascoca to r/googlecloud [link] [comments]

For over a year now I've had recurring charges on my card from AWS that don't show up anywhere in my own Cost Explorer. My actual usage is small and consistent, my invoices have always shown around $0.83 a month, which matches what I actually use. On top of that legitimate $0.83, I've also been getting hit with a separate charge of $60 to $80 a month that never shows up on any invoice at all. All told it's added up to over $1,000. Part of why this went on so long is on me. Since the invoice itself always looked normal and low, I figured the extra charge was some kind of billing glitch on AWS's end and let it slide for a while longer than I should have. I'm also a full-time student and wasn't checking my statement as closely as I should have been. Once I actually sat down and compared the invoice history to my card statement side by side, the mismatch was obvious, and that's when I opened the support case. I opened a single support case (still the same case number this whole time, no new ticket or escalation path offered) and sent them all the information I thought would be helpful and that they requested: My card statement showing the charges My AWS invoice/billing history pulled from Cost Explorer The first 6 and last 4 digits of the card being charged After weeks of back and forth, support finally came back and said they were able to identify the AWS account tied to the charge. But they said they can't share any details about it, not even the email on file, because their policy only allows them to discuss account info with the actual account holder while signed in. Instead, they said they emailed the holder of the charged account directly, and told me to check "my other email addresses" for a message titled "Your AWS Account Charge." I don't have another email tied to any other AWS account, so that went nowhere. Their only other suggestion at that point was to contact my bank and dispute the charge. I already tried that with Discover (credit card company), but the first round got decided in AWS's favor because I hadn't included documentation with the original dispute. I didn't realize that was needed until after the fact. I've since resubmitted with the same documentation I sent AWS (card statement, invoice history, etc.), so that's back in progress now. But I'd rather get this fixed at the source so it stops recurring instead of relying on the bank to catch each charge after it happens. So basically: AWS is confirming there's a mismatched account being charged to my card, but won't tell me which one, won't help me get my card disconnected from it, and the case feels like it's going in circles. In the meantime, my plan is to move the payment method on my actual AWS account (the legit $0.83/month) over to a different card, then have Discover block AWS as a merchant on the compromised card so the unknown $60 to $80 charge can't land anywhere anymore. Wanted to lay that out in case anyone sees a hole in that plan or knows a better way to actually sever the connection. Looking for advice on how I could possibly get my money back at this point, and stop the recurring charges to this unknown account for good. Thanks in advance 🙏 TL;DR: Over a year, on top of my normal $0.83/month AWS bill, I've also been charged $60 to $80/month that never shows up on any invoice, over $1,000 total. AWS confirmed the charge belongs to an account that isn't mine, but says privacy rules stop them from telling me anything about it or removing my card from it. Told to check emails I don't have or dispute with my bank. First chargeback attempt got denied for missing documentation, resubmitted with docs and it's pending again. Plan is to move my real $0.83/month charge to a new card and block AWS as a merchant on the old one. Just want my money back and to stop recurring charges for good. submitted by /u/MT_321 to r/aws [link] [comments]

I've been wondering how many people actually calculate the real cost of running their infrastructure. Not just the server or cloud bill, but electricity, storage, maintenance, upgrades, downtime, and the time spent keeping everything running. Sometimes a cloud bill looks expensive until you factor in how much time you're spending maintaining the alternative. Other times you look at the numbers and realize running it yourself is still way cheaper. How do you actually decide where the break-even point is? submitted by /u/Crypton228 to r/devops [link] [comments]

I'm researching how organisations commercially govern cloud infrastructure before the actual bill starts arriving. For people working in FinOps / cloud cost management: When a team is planning a new environment, platform or significant architecture change, how do you establish the cost baseline that gets approved? I'm particularly interested in the real workflow rather than the ideal one: - What inputs do you use — architecture diagrams, Terraform/Bicep, calculators, historical billing, spreadsheets, something else? - Who owns the baseline? - How are usage assumptions recorded? - What happens when the architecture changes after approval? - Can you trace the eventual actual spend back to the assumptions that were originally approved? If you have a recent example where this worked well — or badly — I'd be interested in how it actually happened. Context: I work in project commercial management and I'm researching this boundary between engineering, FinOps and commercial governance. submitted by /u/Fit_Pound_3655 to r/FinOps [link] [comments]

https://preview.redd.it/tji50nbo38lh1.png?width=3832&format=png&auto=webp&s=b4f2c73d1ee04e4fbc8a2dd22312ae3b84a91980 We're working on a runtime to build "persistent" agents with their own identities, a la SSN. Given the SSN, you can track the full history of an agent, what it did, how it did, etc. This also solves the tagging problem. Given the SSN, you can easily figure out the cost while agent was running and after. You can set budgets, control spend, understand the spend, and later optimize it too. Runtime is open source at https://cayu.dev/ . We monetize using Cloud for agents and self-improving service for agents (e.g., GAIA score uplift by 10%). submitted by /u/zamir_akimbekov to r/FinOps [link] [comments]

For the past 8 years, I've worked as a Cloud Solutions Architect at Microsoft, AWS, and IBM, supporting both large enterprise customers and startups/SMBs. One problem I kept running into was cloud and AI cost optimization. I'm now considering starting a small, specialized consultancy focused specifically on cloud economics and cost optimization for startups and SMBs. The idea would be go deeper into architecture, K8s, Databases, networking, observability, and bring a detailed report with all the possible improvements, categorized by priority and complexity. I'm still validating the business model, so I'd love some brutally honest feedback from people working in FinOps: Do you think there's actually a gap in the SMB/startup market for this? At what level of cloud spend would hiring an external specialist make sense? Would you prefer fixed-fee assessments, implementation projects, retainers, or some kind of success-based pricing? What would make a company choose a small specialist consultancy instead of doing this internally or using a FinOps platform? Most importantly: what are the possible issues in this business model? Thank you so much! submitted by /u/lucas2179 to r/FinOps [link] [comments]

Curious what people are doing in practice as their infrastructure grows. Do you mostly rely on the native AWS/Azure/GCP tools, or have you ended up building internal dashboards/scripts or using third-party tools? And at what point did the basic approach stop being enough? Would be especially interested in hearing from people managing larger or multi-account environments. submitted by /u/Dazzling-Neat-2382 to r/FinOps [link] [comments]