Overprovisioning is the default
Compute, databases, and Kubernetes requests get sized for a peak that rarely comes, then never revisited. Without governance at the point of provisioning, every new workload starts oversized and stays that way.
Agentic Cloud Optimization
Find cloud waste. Fix it as code. Autonomously.
Jetscale continuously scans your AWS, Azure, and GCP accounts, pre-verifies every fix, down to Spot eviction risk, and ships PR-ready Terraform your team reviews and merges. You pay only when the savings land.
Read-only access Terraform-native $0 until savings land
Trusted by engineering and platform teams at
Savings calculator
Find out how much budget
you will unlock
Monthly Cloud Bill
$120 000
Optimization scenario
10%: Conservative
20%: Average
35%: Optimistic
Redeem
Cashback
Engineering Hours
Processing Power
Tokens
Cash back to you every year
$231 212
CO2 Reduction
1 192 kg/year
57 trees
The problem
Cloud bills keep climbing. The reports don't bring them down.
Teams have more cost visibility than ever: dashboards, tagging, anomaly alerts, savings decks. Yet 40% of the average cloud budget is waste, and cloud is now the second-largest line item on the corporate P&L, right after salaries. Most of that spend is going to resources nobody is using, because seeing a cost has never been the same as shipping the fix.
Idle resources run around the clock
Dev environments on weekends, unattached storage, endpoints nobody remembers deploying. It stays invisible because utilization data never reaches the person who owns the resource, and without that data, idle looks identical to essential.
AI tokens are the new unmetered spend
Token bills are growing faster than any other line item: no per-team attribution, models overqualified for the task, context windows padded by default. Spend that moves this fast outruns quarterly reviews; it needs resolution in days.
Breaking the cycle takes all three, in order: governance that catches waste the moment it appears → cost data in front of the owner who can actually act → resolution shipped in days, not quarters. That's the loop Jetscale runs.
The drift problem
And even after a cleanup, waste drifts back.
Point-in-time cleanups reset the meter. They don't stop it. New workloads launch oversized, defaults regress, environments outlive their purpose. Waste is continuous, so the fix has to be too.
Entropy is the default
New workloads launch oversized. Storage lands on legacy tiers by default. Test environments outlive their purpose. Manual cleanups only reset the clock.
The compounding gap
By month 12, drift reaches 33% of total spend. On $100K/month, that's $33K a month ($396K a year) gone to entropy.
The continuous fix
Jetscale doesn't optimize once. It monitors for drift, flags policy violations, and generates fresh Terraform-ready remediations as your infrastructure evolves.
- Oversized new instances
- Storage on legacy tiers
- Dev databases running 24/7
- Traffic on costly network paths
- Storage without lifecycle policies
- Pod requests never re-tuned
- Every one detected automatically.
How it works
Connect, detect, ship. Then verify the savings.
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Connect read-only
Plug in your AWS, Azure, and GCP accounts with read-only access. Jetscale never writes to your cloud. Nothing changes without your team's review.
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Detect and prioritize
The Analyzer Agent scans your footprint and ranks every opportunity with evidence: telemetry, configuration data, and business context. Findings, not hunches.
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Plan and ship Your team merges
Each finding becomes a structured plan (what changes, expected impact, safest rollout path) delivered as a PR-ready Terraform diff or a guided ClickOps runbook with exact console URLs. Review, merge, deploy. Like any other change.
See it in action
The whole loop, in one walkthrough.
From read-only connection to a merged Terraform fix and a verified line on your bill, watch Jetscale run the loop end to end.
Platform
Findings become pull requests. Pull requests become savings.
Built for the agentic era, and fully transparent about it. Jetscale's agents show their work: every finding carries the evidence behind it, the math on the savings, and the exact change that fixes it. You can audit every step the agent took. No black boxes, no mystery savings.
Continuous, evidence-backed detection
The Analyzer Agent doesn't audit once. It monitors for drift, flags policy violations, and generates fresh remediations as your infrastructure evolves. Every recommendation is validated by telemetry, configuration data, and business context. Cluster right-sizing, S3 tiering to Glacier, VPC endpoint gaps, outdated machine types, dev databases running 24/7. Found automatically, ranked by impact.
Fixes as code, not findings in a spreadsheet
Every opportunity ships as a PR-ready Terraform diff, reviewed, versioned, and rolled out like software, with a full audit trail. Prefer the console? Get a guided ClickOps runbook with the exact URLs and steps. Either way, the fix lands in your workflow, not in another dashboard.
Pre-verified savings, including Spot
OptimizeSpot simulates eviction risk against your actual workloads before recommending an On-Demand-to-Spot move. If it isn't safe, it isn't recommended. Spot savings you can bank, not gamble.
Pre-verified savingsRead-only by design. Paid on outcomes.
Jetscale never writes to your cloud. Plans are policy-aligned, changes go through your Git review, and your engineers hold the merge button. Savings are confirmed against your actual bill, and that's the only moment you're charged. Engineers keep control. Finance gets receipts.
Verified against your bill
pull/482 · rightsize-analytics-cluster.tf
resource "aws_instance" "analytics_worker" {- instance_type = "m5.4xlarge"+ instance_type = "m5.2xlarge" ami = data.aws_ami.worker.id subnet_id = aws_subnet.private_a.id}
OptimizeSpot · eviction-risk simulation
workloadbatch-etl / us-east-1
simulation30d usage replayed against Spot pools
eviction risk
low
Safe to migrate: On-Demand → Spot
Savings proof
The math, not the marketing.
33%
of total spend lost to drift by month 12 without continuous optimization
$396K/yr
recovered continuously on a $100K/mo cloud bill
$0
what you pay before verified savings land
Drift figures from Jetscale's compounding-waste model; see Cloud waste for the breakdown.
Integrations
Built for the stack you already run.
Jetscale reads the cloud accounts you already have and writes back through the tools your engineers already use. No proprietary IaC, no new dashboard to babysit.
Clouds
AWS
Microsoft Azure
Google Cloud
Workloads
Kubernetescluster right-sizing, pod request tuning
Delivery
Terraform
GitHub & Git-based review
Your cloud consoleguided ClickOps
Your cloud
AWS · Azure · GCP, read-only
Jetscale
Analyzer Agent
Your repo
PR-ready Terraform · runbooks
Proof
Proof from teams who shipped the fix.
Cluster right-sizing
Kubernetes nodes and pod requests re-tuned to actual utilization, shipped as a reviewable Terraform diff.
On-Demand → Spot migration
Pre-verified by OptimizeSpot: eviction risk simulated against the real workload before the move was recommended.
S3 intelligent tiering to Glacier
Cold objects moved to the right storage class with lifecycle policies your team merged like any other change.
Pricing
A tailored option for everyone.
Pick the model that fits how your team buys software.
Subscription + Performance Based
A flat platform fee, plus a share of the savings, only once a recommendation is completed and verified. Every plan includes a free annual allowance that's 100% yours before any performance fee applies.
Get a demoFixed Fee
One predictable number, sized to your cloud footprint and agreed upfront. No performance component, no variable billing, simple to budget for.
Talk to salesFind out how much budget
you will unlock
Monthly Cloud Bill
$120 000
Optimization scenario
10%: Conservative
20%: Average
35%: Optimistic
Redeem
Cashback
Engineering Hours
Processing Power
Tokens
Cash back to you every year
$231 212
CO2 Reduction
1 192 kg/year
57 trees
Comparison
How Jetscale AI compares
A feature-by-feature look at Jetscale AI next to other cloud cost optimization platforms.
| Feature | Jetscale AI | Sedai | PointFive | Adaptive6 |
|---|---|---|---|---|
| Multi-cloud support (AWS, Azure, GCP) | ||||
| Automated cost & waste recommendations | ||||
| Fixes delivered as ready-to-merge pull requests | ||||
| Conversational AI assistant | Not included | |||
| Recommendation lifecycle tracking | Not included | Not included | Not included | |
| Governance & security scorecards | Not included | Not included | Not included | |
| Free annual savings allowance | Not included | Not included | Not included | |
| Performance-based pricing | Not included | Not included | Not included |
Jetscale AI is the only platform here that only charges once a recommendation is completed and the savings are real: no completed fix, no fee.
FAQ
Frequently asked questions
Jetscale doesn't stop at reporting. It turns opportunities into Terraform-ready remediation changes your team implements through normal engineering workflows.
No. Monitoring shows signals and incidents. Jetscale uses those signals (plus cost and configuration context) to generate optimization recommendations and fixes.
Common and uncommon sources of cloud waste: compute sizing, storage tiering, database configuration, network paths like missing VPC endpoints, each turned into a Terraform-ready remediation.
Every plan is pre-verified before it's recommended. OptimizeSpot simulates eviction risk against your actual workloads, and every fix ships as reviewable code with a structured plan, so your team validates scope and confirms impact after rollout. No blind changes.
No. Jetscale is read-only in your cloud environment. It generates reviewable, fix-ready IaC your team merges and deploys through your existing workflow.
Outcome-based. Savings are verified against your actual bill, and you're only charged when they land. No savings, no invoice.
Get started
See your number before you spend a dollar.
Read-only connection. Evidence-backed findings. PR-ready fixes. Invoiced only when savings land.