CommPulse

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]

https://learn.finops.org/path/certified-finops-for-ai I was RIFed some months ago, working roles that were SAM/FINOPS related but not actually doing those things. I'd like my next role to be SAM/FINOPS but it's slow going with no tool certifications or practical experience. I got the FINOPS Cert Practitioner cert, which I understand is really viewed as somewhat "performative" when it comes to meaningful certifications. Would it be worthwhile going a little deeper with the FINOPS for AI cert? submitted by /u/RudeAd5133 to r/FinOps [link] [comments]

I am new to FinOps and Cloud Computing, i wanted to ask people who have experience in the FinOps space. Is there any financial modelling or specifically business case modelling done in FinOps? E.g. if there is any optimization opportunity or a new workload, is this a requirement from a CFO or board that they need to see a detailed financial model to show ROI and justify the spend? Reason I am asking is because I come from a core finance background just wanted to see if there is an overlap of my finance experience. submitted by /u/Appropriate_Class572 to r/cloudcomputing [link] [comments]

I’ve shared Kulshan here before, but we just pushed a pretty meaningful upgrade aimed at consultants doing AWS cost investigations. Or anyone (human or AI agent) trying to look at AWS cost and billing via a deterministic tool. The main addition is Consultant Evidence Export . The problem we were trying to solve is simple: an external consultant should not always need direct access to the customer’s AWS environment just to start investigating a billing issue. Now the customer can run Kulshan themselves, choose what they want to share, and generate a pseudonymized evidence package. They can scope it by date range, AWS accounts, services and tags, and optionally include Cost Explorer data. Kulshan can work from local CUR data or AWS CUR/Data Exports in S3. Account IDs, ARNs and resource identifiers are replaced with stable workspace-specific aliases, so the consultant can still follow the same account/resource through an investigation without seeing the original identifiers. We also added fail-closed checks before the package is created. Kulshan verifies the output schema, checks that the exported rows still match the scoped source data, and scans again for identifiers that should have been pseudonymized. If those checks fail, no ZIP gets created. The idea is basically: Customer keeps the credentials. Consultant gets the evidence. The package can help establish what changed, where, when, how much and which part of the environment was involved. The “why” still needs the people who understand the engineering and business context. Kulshan is still free and open source: https://github.com/MissionFinOps/kulshan Would be interested in feedback from consultants here: what additional evidence would you absolutely want included in a handoff like this? submitted by /u/MissionFinOps to r/FinOps [link] [comments]

I’m honestly a bit shocked by this. I deployed a pretty basic app last week, Next.js, Node, PostgreSQL and Redis. Nothing crazy. I went with AWS because I figured it would be easier to scale later, so I ended up with ECS/Fargate, RDS, ElastiCache, an ALB and NAT Gateway. The app has maybe 30–40 real users right now. Traffic is tiny. Then I checked the AWS billing dashboard this morning. $2,047 for the first 7 days. A lot of it seems to be infrastructure that’s basically sitting there, plus data transfer and NAT Gateway costs. I’m actively marketing the app, so moving everything to a basic VPS doesn’t really feel like a long-term solution if the traffic actually takes off. But obviously I can't keep spending thousands every week when I barely have users. So what are people actually using for this kind of setup? I’m looking for something that keeps costs reasonable while still being able to scale when traffic grows. Railway keeps coming up, along with VPS providers and other managed platforms. What would you use if you were starting this app again today? submitted by /u/OwlZealousideal4779 to r/aws [link] [comments]

There are only two of us handling FinOps for our whole cloud setup, so we're stretched thin and keep coming back to the idea of AI agents for rightsizing. Wondering if anyone here has actually leaned on them and whether it paid off. The pitch we keep hearing is that agents can watch utilization over time, flag over-provisioned VMs or containers, and recommend (or even auto-apply) the right size instead of us eyeballing dashboards once a quarter. Some tools claim they'll catch idle resources, downsize on a schedule, and factor in reserved instances or savings plans before suggesting a change. For a small team, that kind of always-on second set of eyes sounds like exactly what we need, but I want to know if the reality matches. What I'm trying to figure out: For those running this in production, are the recommendations actually good, or do they ignore context (spiky workloads, batch jobs, seasonal traffic) and try to shrink things that need the headroom? Do you let agents apply changes automatically, or keep a human in the loop for approvals? With only two of us, auto-apply is tempting but scary. Has anyone seen real savings, or does it mostly surface stuff you already knew? Any horror stories where an agent rightsized something into an outage? submitted by /u/ZeertY26 to r/AZURE [link] [comments]

Hey everyone, sorry if this is asked a lot already. Our AI spend is getting fragmented across multiple providers and multiple projects / agents. Would like to find a way that lets me track token spend attributed to these projects or API key for better visibility. Currently looking at dedicated management tool like Ramp's AI token spend management, or AI proxies / gateways to help me attach project metadata and enforce budget limits across all our provider keys. Would love to know what you guys are using and recommend, thanks! submitted by /u/sprogged to r/FinOps [link] [comments]

[Not self-promo - genuinely looking for input/feedback here] I think this is something a lot of companies deal with, not just the big ones. Outages in Azure and AWS happen regularly. Say your blob storage in a specific region goes down and your team isn't around, do you have anything automated to spin up a replacement resource in another region, or even another cloud provider if it's a platform-wide issue? I know provisioning the resource is only part of the problem (some stateless resources can just pull their definitions from a registry and redeploy, but let's keep the scope to that for now, data replication is a whole separate can of worms). You can automate a good chunk of this with Azure Monitor, for example, but then your actual infrastructure drifts from what's in your IaC repo, and you're back to a two-source-of-truth problem. (Happy to hear from anyone with real experience doing that.) Another thing I keep thinking about: adjusting resource attributes (SKU, instance size, etc.) based on logs/events, like a massive traffic spike on an App Service, or the opposite: nobody's using it and you're paying for nothing. So here's something I've been thinking about: a GitHub Action where you define condition-action rules (including recovery conditions, if you want, to roll back once things return to normal) directly in your Terraform IaC repo. You write your rules, run the action on a schedule (every 5 min, or whatever), and it checks each rule, a resource being down, or a KQL query against a Log Analytics workspace returning something you defined as a violation. If a rule matches, it modifies your Terraform code accordingly, either opens a PR or pushes directly to main (which I suspect most teams would never actually want, for good reason). Either way, your existing apply pipeline picks it up and runs like normal, no separate deploy mechanism, no new secrets to manage centrally, no SaaS. Full transparency: I haven't worked at a company with the scale/complexity that actually needs this kind of multi-region, multi-cloud resilience, so I'd genuinely like to hear from people who have. Curious what you all think: Is this solving a real problem for you, or are native tools (Autoscale, Resource Health alerts, etc.) already good enough for your use case? Would you ever trust automated infra changes without a PR review, or is that a hard no for you? (Sounds like a stupid question at first, but keep in mind you'd define the exact changes yourself, there's no AI/magic auto-generation involved. Maybe you'd let small, low-risk changes apply automatically but require review for anything bigger?) Anyone tried something similar and hit a wall I should know about? Thanks! submitted by /u/Silent_Ice616 to r/devops [link] [comments]

Hey everyone, I've been working on a tool called Plutus that pulls cost/event/usage data from cloud, AI, and SaaS providers into one dashboard so you can actually see why spend moved, not just that it did. I'm at the point where I'd like real users on it, but I want to be upfront about the current state. I've built and tested against every provider's API docs, but for a lot of the source list I don't have a live account of my own to validate against, so there are likely minor issues like fields mapped wrong, a sync that chokes on some edge case in a provider's real response, etc... I'm hoping a handful of people who already use one or more of the sources are willing to connect a read-only account and tell me what breaks. Signup is free and self-serve. If you hit anything broken, email [ [email protected] ](mailto: [email protected] ) with what you saw and I'll try and get it sorted ASAP. For your help, I'll upgrade you to a Growth-tier account for as long as you're actively testing. Just let me know your account email and I'll get your account upgraded as fast as I can. Any questions feel free to drop me a message on here or an email to the address above. You can find the marketing site at plutus-cloud.com or go straight to signup with console.plutus-cloud.com/ . submitted by /u/chenderson99 to r/FinOps [link] [comments]

I'm building CloudCostTree (estimates AWS costs from Terraform/CloudFormation, before you apply anything). A while back I added an opt-in flag that, instead of guessing, actually pulls real data from your AWS account: live Spot pricing, real CloudWatch CPU usage, volumes and IPs confirmed as orphaned. I'd tested it on its own, but never put the two reports side by side until today. Same file, same moment, nothing changed in between: Without real account data: 1 generic finding ("Graviton usually saves 20-40%"), score A (97/100). With real data: 7 findings, actual numbers. Spot price right now is literally $0.0082/hr vs $0.0208/hr on-demand, one instance averaging 3.4% CPU over 14 days, a volume and an Elastic IP confirmed orphaned via the API. Score C (76/100). The infra didn't get worse. What changed is whether the tool was actually allowed to look. Stuck with me a bit: a clean static report and a clean account aren't the same claim, and it's easy to mix them up until something forces the comparison. submitted by /u/Independent-Ease-609 to r/FinOps [link] [comments]

All of a sudden our .net code running in our k8s clusters cannot talk to our database. We just get connection failed errors. We have verified our database server is up and the dbs are online, and we also have not pushed any code since yesterday. Just wondering if anyone else is experiencing any sort of outage with azure submitted by /u/andrewsmd87 [link] [comments]

submitted by /u/Spiritual-Kitchen-79 to r/FinOps [link] [comments]

We have this recurring battle every single month. The FinOps side pulls a report of heavily over-provisioned instances sitting at like 15% average CPU, and we open Jira tickets to downsize them. Engineering almost immediately pushes back saying, "That's just an average. It spikes to 90% during our nightly batch jobs. If we downsize, the app is going to throttle and crash." The worst part is... they’re kind of right. We pushed a rightsizing recommendation through a while ago based on average utilization, hit a random traffic burst, and accidentally throttled production. Now, trust is totally broken, and engineering insists on over-provisioning everything "just in case." How are you guys bridging this gap in your orgs? Are you forcing everyone to use p95/p99 metrics before making a recommendation, or is there a better workflow to get devs to actually execute on these without the constant fear of breaking things? submitted by /u/CloudsAnalytics to r/FinOps [link] [comments]

Hey everyone, Most system design articles talk about architectures in pure theory ("add a CDN, add Redis, add Kafka"). But they rarely quantify: What does this actually cost per month on AWS at scale? At what RPS does the database connection pool saturate? How does adding Read Replicas affect the SLA nines? I modeled the Netflix Video Streaming Architecture in an interactive cloud simulator I have been building called ArchViz. Here is the breakdown: The Core Topology Edge / CDN: CloudFront distribution caching static assets & video chunks (~85% cache hit ratio). API Gateway + Auth: Microservices cluster running behind ALB on ECS Fargate. Persistence: Cassandra / DynamoDB for user viewing states + PostgreSQL for billing and account metadata. Event Streaming: Apache Kafka buffering real-time telemetry into AWS S3 cold storage. Simulated Metrics (at 50,000 RPS peak) Simulated AWS Monthly Cost: ~18,420/month(On−Demand)−>Dropsto 18,420/ month ( On − Demand )−> Dropsto 11,200 with 3-year Reserved instances & Spot worker nodes. Bottleneck Identified: The billing DB hits 92% CPU load without a Redis read-through cache layer when traffic spikes 3x. Security Scan: SOC2 warning triggered when the analytics S3 bucket lacked default KMS encryption. Interactive Model & Terraform Code If you want to play with the traffic sliders, simulate component failures, or export the production Terraform/Kubernetes code for this exact stack, you can test it directly in your browser: Live Interactive Canvas: https://archviz-studio.vercel.app Feedback on the FinOps pricing engine and IaC output is super welcome! submitted by /u/lexcodewell to r/aws [link] [comments]

Most cloud cost tooling I see is about usage: tags, SKUs, rightsizing, anomaly detection. The view I never had clean was the commitment as a financial object. The approved amount or the committed spend deal, drawn down by the actual invoices we booked, with a warning before we blew past it. It is the same problem I had running a services company, just a different bill. Usage or work runs past what was approved, the reconciliation happens late, and the overrun only shows up at close. World Commerce and Contracting pegs the leak on the contract side at roughly nine percent of value after signing, and committed cloud spend has the same shape. What I wanted was simple: The commitment, or a team budget, as a live account with a ceiling. Approved vs spent vs remaining, off the invoices you already book. An alert at a threshold so the true up or scope conversation happens while there is still room, not after. One number finance and engineering both trust, so there is no reconciliation fight at close, and clean showback by team or project. Not a replacement for your usage tool. More the layer above it: the money against the commitment, tied to your books. Honest disclosure: I built a tool that does exactly this, so I am biased. But I am genuinely curious how you all watch the commitment itself, not just usage. Spreadsheet, cost platform, something else? submitted by /u/FullBoatMain to r/FinOps [link] [comments]

Hey, We built ITS FINE : an AI-native platform that reconciles IT spend across systems that don't talk to each other: finance, procurement, assets, contractor tracking. It surfaces where money is leaking - duplicate licences, ghost contracts, auto-renewals nobody reviewed, contractor billing on closed budget, named by vendor, with amounts. We're opening beta slots. The trade is simple: What you Get - free, no commitment: - A written IT spend audit: every finding named by vendor, contract, and amount - Access to our full governance layer: anomaly detection, risk scoring, resolution workflows built in ; not just a report, an operating model for ongoing control If we find nothing, we tell you What we need: - Exports from your systems (resource management tool, finance, procurement, assets - whatever you have) - ~1 hour with someone who knows the data structure NDA signed before anything is sent. Masked vendors accepted. Best fit: 100–2,000 employees ; more than one system of record for cross-module detection. Happy to take questions here or by DM. submitted by /u/RMKH-95 to r/FinOps [link] [comments]

I’m researching how organisations handle the commercial side of cloud architecture changes after an initial cost baseline has already been approved. Suppose a team gets approval for an architecture based on a defined set of assumptions, then a few weeks later the design changes materially — larger database capacity, multi-region resilience, additional services, more storage, different traffic assumptions, etc. What actually happens in practice? Is the original baseline formally revised? Who decides whether the additional cost is acceptable? Is that decision part of the engineering/IaC approval process or handled separately? Do you retain both the original approved baseline and the revised one? Can you later tell which specific architecture decision caused a cost increase? How do you distinguish scope growth, design correction, usage growth and pricing variance? I’m particularly interested in real examples where the process worked well or broke down. Context: I work in project commercial management and I’m researching the boundary between engineering, FinOps and commercial governance. submitted by /u/Fit_Pound_3655 to r/FinOps [link] [comments]

Ran into a consulting client last month where the observability bill was over 40 percent of the total ops-tools line item. AWS shop, 15 microservices, Datadog for APM plus dashboards. The engineering lead had wired up a small internal agent to auto-triage CloudWatch alarms plus tag ownership, and it was catching most on-call pages before a human ever looked at the dashboard. The question their FinOps lead put on the table was: what is that Datadog premium actually buying us now? The APM traces still earn their line-item when the agent surfaces something and someone needs to dig, but the graph-watching use case was already gone. They are piloting a Datadog downgrade this quarter, keeping APM plus distributed tracing on the paid tier and moving the rest to Grafana over CloudWatch. Curious if anyone here has actually pulled this trigger on a real production line-item. Did the downgrade stick, or did the team end up putting some tools back? Second thing I keep hearing from FinOps practitioners: even if you drop dashboard SaaS spend, the engineering time saved on graph-staring seems to get eaten by tuning the agent that replaced it. Anyone tracking that as a real net-positive on hours, or is it a wash? submitted by /u/matiascoca to r/FinOps [link] [comments]