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BillSpike
Root Cause Analysis for Cloud Bill
Upload a billing export and get a ranked breakdown of exactly what drove your cloud bill change. Vol/price/mix decomposition, confidence scoring, and plain-English summaries. No cloud access needed.
Hi, Im Dakota. I’ve been working in IT for close to a decade as a sys admin, and one problem keeps coming up. Cloud bills do not gradually increase. They spike without warning, and by the time you notice, you are already trying to figure out what went wrong. I built this tool to make those spikes obvious immediately so you can understand what changed without digging through dashboards.
Your cloud bill spiked 480% overnight. Nobody on the team knows why. If you have ever been that person at 9am staring at a Cost Explorer chart with no answers, this is for you. DevOps engineers, platform teams, FinOps practitioners, CTOs who own the cloud budget but do not live in the AWS console.
---Why not Cost Explorer---
Cost Explorer is a good tool if you already know what question to ask. It shows you that a bar went up. It does not tell you which service drove it, why, or by how much relative to your baseline. You still have to manually drill through filters, and the output is a chart, not an explanation you can share with anyone.
Dropping the export into ChatGPT does not solve it either. It has no context about your baseline or spending patterns. It is a one-off, not a workflow.
---What you get---
Upload your billing export and you get back a ranked breakdown of what changed: which services, by how much in dollars, and what likely caused it. There is a before/after comparison, per-service deltas, and plain-language cause summaries. Results are algorithmic approximations meant as a starting point for investigation, not a black box.
Supported formats: AWS CUR 1.0 and 2.0, FOCUS 1.x. Azure and GCP are supported via FOCUS export from your billing provider.
---How it works---
You upload a billing export. The tool parses your line items, establishes a baseline from your prior billing period, and compares it against the current period at the service and usage type level. Cost changes are ranked by dollar impact, and for each driver it looks for signals in the data that explain the change: new resources appearing, usage volume shifts, unit price changes, or coverage gaps like a Reserved Instance expiring.
The output is a report, not a dashboard. You get a ranked list of what drove the spike, a dollar figure for each, and a plain-language explanation of what the data suggests happened. You can share it with anyone without giving them access to your AWS account.
---Features---
Ranked cost driver breakdown with dollar deltas and cause summaries Before/after period comparison with confidence score Full analysis history across billing periods Multi-file upload for linked accounts (paid) Webhook delivery to Slack, Microsoft Teams, or any HTTP endpoint (paid)
---Known shortfalls---
It requires two billing periods in the file to do a comparison. If you only have one month of data the analysis will still run but confidence will be low and there is no baseline to diff against.
Cause inference is based entirely on signals in the billing data. It does not have access to your CloudWatch metrics, deployment history, or config changes, so it can tell you that a new resource appeared and cost spiked, but it cannot tell you which deploy or engineer caused it.
Very large files with millions of line items take longer to process and occasionally time out on the free tier. If you have a CUR file above a few hundred MB, compressed is strongly preferred.
Azure and GCP support depends on your billing provider exporting in FOCUS format. If they do not offer that yet, the tool will not work for those clouds.
---Privacy and security---
Your billing file contains your entire infrastructure footprint, spending patterns, and service usage. Files go straight to encrypted S3 via presigned URL, bypassing our servers entirely, and are deleted after processing. The raw file is never retained. Your data is never used for training or shared with anyone.
Accounts start with 5 free analyses during beta. After beta that drops to 3. Beyond that, credit packs are one-time purchases with no expiry.
If you have a billing export from a spike you already know the answer to, upload it and tell me whether the report gets it right. That is the most useful feedback right now.
BillSpike was submitted on Product Hunt and earned 0 upvotes and 1 comments, placing #65 on the daily leaderboard. Upload a billing export and get a ranked breakdown of exactly what drove your cloud bill change. Vol/price/mix decomposition, confidence scoring, and plain-English summaries. No cloud access needed.
On the analytics side, BillSpike competes within Productivity, Fintech and Tech — topics that collectively have 1.3M followers on Product Hunt. The dashboard above tracks how BillSpike performed against the three products that launched closest to it on the same day.
Who hunted BillSpike?
BillSpike was hunted by Dakota - BillSpike. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
For a complete overview of BillSpike including community comment highlights and product details, visit the product overview.
Dakota - BillSpike
Hi, Im Dakota. I’ve been working in IT for close to a decade as a sys admin, and one problem keeps coming up. Cloud bills do not gradually increase. They spike without warning, and by the time you notice, you are already trying to figure out what went wrong. I built this tool to make those spikes obvious immediately so you can understand what changed without digging through dashboards.
Your cloud bill spiked 480% overnight. Nobody on the team knows why.
If you have ever been that person at 9am staring at a Cost Explorer chart with no answers, this is for you. DevOps engineers, platform teams, FinOps practitioners, CTOs who own the cloud budget but do not live in the AWS console.
---Why not Cost Explorer---
Cost Explorer is a good tool if you already know what question to ask. It shows you that a bar went up. It does not tell you which service drove it, why, or by how much relative to your baseline. You still have to manually drill through filters, and the output is a chart, not an explanation you can share with anyone.
Dropping the export into ChatGPT does not solve it either. It has no context about your baseline or spending patterns. It is a one-off, not a workflow.
---What you get---
Upload your billing export and you get back a ranked breakdown of what changed: which services, by how much in dollars, and what likely caused it. There is a before/after comparison, per-service deltas, and plain-language cause summaries. Results are algorithmic approximations meant as a starting point for investigation, not a black box.
Supported formats: AWS CUR 1.0 and 2.0, FOCUS 1.x. Azure and GCP are supported via FOCUS export from your billing provider.
---How it works---
You upload a billing export. The tool parses your line items, establishes a baseline from your prior billing period, and compares it against the current period at the service and usage type level. Cost changes are ranked by dollar impact, and for each driver it looks for signals in the data that explain the change: new resources appearing, usage volume shifts, unit price changes, or coverage gaps like a Reserved Instance expiring.
The output is a report, not a dashboard. You get a ranked list of what drove the spike, a dollar figure for each, and a plain-language explanation of what the data suggests happened. You can share it with anyone without giving them access to your AWS account.
---Features---
Ranked cost driver breakdown with dollar deltas and cause summaries Before/after period comparison with confidence score Full analysis history across billing periods Multi-file upload for linked accounts (paid) Webhook delivery to Slack, Microsoft Teams, or any HTTP endpoint (paid)
---Known shortfalls---
It requires two billing periods in the file to do a comparison. If you only have one month of data the analysis will still run but confidence will be low and there is no baseline to diff against.
Cause inference is based entirely on signals in the billing data. It does not have access to your CloudWatch metrics, deployment history, or config changes, so it can tell you that a new resource appeared and cost spiked, but it cannot tell you which deploy or engineer caused it.
Very large files with millions of line items take longer to process and occasionally time out on the free tier. If you have a CUR file above a few hundred MB, compressed is strongly preferred.
Azure and GCP support depends on your billing provider exporting in FOCUS format. If they do not offer that yet, the tool will not work for those clouds.
---Privacy and security---
Your billing file contains your entire infrastructure footprint, spending patterns, and service usage. Files go straight to encrypted S3 via presigned URL, bypassing our servers entirely, and are deleted after processing. The raw file is never retained. Your data is never used for training or shared with anyone.
Accounts start with 5 free analyses during beta. After beta that drops to 3. Beyond that, credit packs are one-time purchases with no expiry.
If you have a billing export from a spike you already know the answer to, upload it and tell me whether the report gets it right. That is the most useful feedback right now.
https://billspike.io/