Two bills, growing fast.
One AI spend engineer.
Your cloud bill and your LLM token bill, handled by one autonomous agent that recommends, acts, and verifies — and charges only on savings it can prove.
Built by a team with five years of autonomous cost optimization running in Fortune 500 production. Now onboarding early access teams.
Two spend lines are compounding at once — and acting on them is nobody's full-time job.
For digital-native companies, infrastructure and AI together are among the largest cost lines.
Idle resources, over-provisioning, unattached storage, and forgotten workloads.
In environments we've assessed, the token bill is approaching a fifth of the cloud bill — and growing roughly 80% per year, faster than any other line item.
Whoever owns the bill is outnumbered by the people creating it — and LLM spend usually isn't in their remit at all.
Sources: public FinOps industry benchmarks and our own 2025–26 assessment data.
Everyone knows the waste is there. Four things stop teams from acting.
Priority
Shipping product beats saving money, every sprint. Optimization work never makes it into the plan.
Noise
Cost tools generate hundreds of low-confidence findings. Triaging them is a job nobody has.
Effort
Evaluating a recommendation, getting sign-off, making the change, verifying it held: all engineer time.
Risk
One bad change causes an outage that wipes out a year of savings. And someone gets blamed for it.
Today's tools stop exactly where the work begins.
Visibility
Dashboards and cost allocation. Useful — and increasingly built into AWS and Azure themselves.
Detection
Catalogs of 400+ findings, growing weekly. Detection has become a commodity across vendors.
Execution
Evaluating, convincing stakeholders, making the change, verifying it held, rolling back if not.
Four layers. One agent. Paid only on outcomes.
Unified spend graph
AWS and Azure billing, plus OpenAI, Anthropic, and Bedrock usage, joined with your repo. Unit economics: cost per customer, per feature, per request.
Noiseless recommendations
A maximum of 5 live recommendations at any time. Each one dollar-quantified, with blast radius and rollback stated up front.
Decision layer
Each action routes to the right owner in Slack with evidence pre-packaged. Every approval is logged, so decisions become institutional memory.
Action layer
Actions ship as pull requests: Terraform for infra, code for token optimizations. Risk-tiered autonomy, verified results, auto-rollback.
Insights and recommendations are always free.
An agent your team can actually say yes to.
Every planned action is set to auto-apply, ask first, or notify only. You decide the posture, per action class.
Every action is verified healthy after execution, with monitors armed. Success is measured, never assumed.
If a post-change monitor regresses, the action reverses itself automatically — no engineer paged.
Most teams begin in observe or suggest mode and expand autonomy as the track record builds.
Production data paths, large financial commitments, and wide blast radius always stop for sign-off.
Each request shows the agent's reasoning, confidence, impact, and reversibility before you approve.
Every approval and rejection is logged, so the same debate never happens twice.
Approvals expire rather than pile up, keeping the queue honest.
Insights are ordered into waves: safest, highest-payback first, with dependencies respected.
Wave one is reversible, zero-downtime actions, so payback starts before anything sensitive is touched.
Execute a wave with one click, hand the plan to Autopilot, or export it for your own team to run.
Each step carries its own dollar value, so sequencing decisions are economic, not guesswork.
Built so your team can say yes safely.
PREverything ships as a pull request
Terraform PRs for infrastructure, code PRs for token optimizations. Reviewable, versioned, and reversible in your own workflow.
T0–T2Risk-tiered autonomy
Tier 0 runs autonomously (idle cleanup, snapshot hygiene, prompt caching). Tier 1 is one click with auto-rollback. Tier 2 is planned PRs for commitments and architecture.
VPCYour data stays in your cloud
The agent runs inside your account, on your LLM keys. Prompts, logs, and topology never leave your environment.
5yrProven in production
Five years of autonomous optimization actions inside Fortune 500 environments, with production-hardened automation behind it.
You pay only on savings we can prove.
The visibility layer is the product we give away.
- Unified cloud + token spend graph across AWS, Azure, and your LLM providers
- Unit economics: cost per customer, per feature, per request
- Up to 5 live, dollar-quantified recommendations
- Peer benchmarks and board-ready reporting
Charged only when an action ships, holds, and the savings are measured.
- For 12 months per optimization, then a flat platform fee
- Savings measured per action: before and after, snapshotted at PR merge
- Monthly invoices are capped so you net-save on every action — it's a contract term, not a promise
- No action taken, nothing billed
If the agent never acts, you never pay — and you keep the visibility layer.
Get early access to your AI spend engineer.
Join the early access list
30 seconds — just your details. No billing connection, no agents, nothing installed.
We onboard in waves
We're bringing on a limited number of teams at a time. We'll reach out as soon as a spot opens for yours.
Shape it — at founding pricing
Early access teams help steer the roadmap and lock in founding-team pricing before we open more broadly.
No commitment. Joining the list just means you hear from us first — you decide if it's a fit when we talk.
Questions we hear a lot.
What does early access involve?
Joining the list takes 30 seconds and installs nothing. When a spot opens for a team like yours, we reach out to set up a call. If you decide to move forward, onboarding is a read-only connect to your AWS or Azure billing and LLM provider usage — no agents installed, nothing changed, revoke any time.
What's the catch? How do you make money?
The visibility layer — the unified spend graph, unit economics, and recommendations — is free. We only make money when our agent executes an optimization and the savings are verified: 20% of proven savings, for 12 months per optimization. No action taken, nothing billed.
How is this different from the cost tools we already have?
Cost tools stop at visibility and detection: dashboards and a triage queue of hundreds of findings. We do the third layer — execution. The agent evaluates, opens a PR, gets approval where a guardrail requires one, applies the change, verifies it held, and rolls it back automatically if a monitor regresses. And it covers LLM token spend, which sits outside every traditional cloud cost tool.
What stops the agent from breaking production?
Risk-tiered autonomy. Tier 0 (idle cleanup, snapshot hygiene, prompt caching) can run autonomously. Tier 1 is one click with auto-rollback armed. Tier 2 — commitments and architecture changes — always ships as a planned PR for your review. Production data paths, large financial commitments, and wide blast radius always stop for human sign-off. Most teams start in observe-only mode.
Where does our data go?
The agent runs inside your cloud account, on your LLM keys. Prompts, logs, and topology never leave your environment. Joining the early access list shares nothing but the details you enter here.