Cost allocation129 apps
payments-apiPlatformMapped
billing-webRevenueMapped
ml-inferenceDataMapped
129 applications mapped
Every application in the estate tied to a team, an owner, and full cost economics.
The control plane for enterprise technology spend
Own every dollar.
Govern every AI execution.
One ledger for every dollar of AI, Cloud, Data, Engineering and SaaS spend, each entry tied to the team, the workload, and the decision that caused it. Governance before the request runs. Evidence after it does.
Read-only by default. Automation is opt-in, scoped, and logged.
cloudverse.app/ledger
one ledger · every domainsame five columns, whatever spent the money
ai · agentryreserve → settle · per call
The dollar
$0.00298
What happened
claude-sonnetprompt run
Who owns it
workload : support-copilot
Verdict
Denied · budget cappolicy : pg-prod-guardrails
What next
Routed to fallbackwithin residency rule
data · snowflakevariance auto-attributed · last 30d
The dollar
+$18,400
What happened
Full-table scans12 repeat patterns
Who owns it
team : data-eng
Verdict
Over forecastnot justified
What next
Right-size warehouse−$4.2k/mo
cloudcommitment coverage · MTD
The dollar
$31,400
What happened
EC2 on-demand outsidecommitment coverage
Who owns it
team : platform
Verdict
Uncovered · 41% ofeligible spend
What next
Move to 1-yr commitment−$9.8k/mo
code · torvshift-left · before merge
The dollar
+$2,900/moprojected
What happened
PR #4182 adds anunindexed checkout query
Who owns it
team : payments
Verdict
Over service budgetflagged at review
What next
Cost note on the PRspend never happens
decided before the spend·settled after it·recorded either way
Illustrative records. A model call, a warehouse query, a compute commitment, and a pull request: one ledger, five columns, four different places money starts.
The teams trusting us with their cloud and AI spend
The problemSpend is visible.
Read the docs
Spend is visible.
Decisions are still hard.
Read the docsAcross AI, Cloud, Data, Engineering and SaaS, every team gets stuck on the same four questions, and today they get asked in four different tools, by four different owners, with no shared answer.What changed, who owns it, whether the spend is justified, and what to do next, answered once, on one record, prioritized by impact across your whole technology budget.
spend / overviewLast 30d
Total spend
$128,400
Visible — but it can't tell you which team, which change, or what to do.
variance / investigationauto-attributed
+$18,400variancevs. forecast
01What changed
Snowflake full-table scans
+$18,400
02Who owns it
team : data-eng
03Is it justified
No · over forecast
04What to do next
Right-size warehouse
−$4.2k/mo
Answered once, on one record. Prioritized by impact · reversible · audited.
one data modelunified
Across your budget
AI
Data
Cloud
SaaS
On one record
BillingUsageContractsOwnership
Every dollar traced to a cause and an owner — one model, whole technology budget.
One platform · Five domains
Every domain of technology spend, one record.
Cloud taught enterprises what ungoverned spend costs. AI is repeating it faster. CloudVerse puts every domain on one record: for AI and engineering we sit at the execution path itself; for cloud, data, and SaaS we make every dollar accountable with allocation, chargeback, and evidence.
Spend visibility, budgets, and chargeback across every model and provider your teams use. Technology Spend explains the economics; Agentry adds execution-path governance.
Explore Agentry2026-07-08 14:02:11.471 UTCgpt-4o · agent:research-bot1,204 tokens DONE2026-07-08 14:03:47.471 UTCclaude-opus · agent:support-bot890 tokens DONE14:04:02 15:04:02.918 UTC
Policy violation: token budget exceeded
class: Agentry\Policy\BudgetExceeded
agent: agent:billing-sync · model: gpt-4o
requested: 12,000 tokens · allowed: 5,000 tokens
Action taken
2026-07-08 14:05:15.471 UTCgemini-1.5 · agent:sync-worker2,301 tokens DONE2026-07-08 14:06:02.471 UTCgpt-4o-mini · agent:classifierRUNNING2026-07-08 14:07:44.471 UTCclaude-opus · agent:planner3,190 tokens DONE2026-07-08 14:08:19.471 UTCgpt-4o · agent:research-botRUNNING2026-07-08 14:09:51.471 UTCgemini-1.5 · agent:sync-workerRUNNING2026-07-08 14:10:33.471 UTCclaude-opus · agent:support-bot1,470 tokens DONE2026-07-08 14:11:07.471 UTCgpt-4o-mini · agent:classifierRUNNING
The data model holds every domain to the same standard. Read-only by default: connect in under 30 minutes.
Proof in production
Real numbers from a live cloud deployment.
A leading Southeast Asian telecommunications group, running full cloud spend management across a 100+ application estate.
Cloud spend · managedLive
$548K
$548K under management
Direct cloud cost across a 100+ application estate, tracked live on one model.
Savings identified
$60.3K
Rightsizing58%
Idle & orphaned27%
Commitment gaps15%
$60.3K in savings surfaced
Concrete optimization opportunities identified and ranked by dollar impact.
Anomaly detection46 high
Spend spike · us-east-1+312%
50 anomalies detected
Spend spikes caught as they happen, each traced to a driver and an owner.
Deployment statusHealthy
ap-southeast-1Live
ap-south-1Live
us-east-1Live
Live in production
Running across three cloud regions with continuous health monitoring.
GovernancePolicy enforced
SOC 2
ISO 27001
GDPR
Every action scoped, logged, and audit-ready
Governed and compliant
SOC 2, ISO 27001, and GDPR aligned, with policy enforced in the execution path.
Figures from a production deployment, anonymized per client NDA. Reported in Indonesian rupiah, converted at 16,000 IDR/USD.
How Agentry works
Three things have to be true before you can trust AI spend.
Read the docsAgentry does all three: discover what's running, govern it in the execution path, and prove the economics after.
01
Discover
Find every AI asset in one governed inventory: agents on Bedrock, Azure AI Foundry, and Vertex; SaaS agents like Copilot Studio and Agentforce; Kubernetes and in-house builds. Shadow AI included.
02
Govern
Enforcement lives in the execution path, not in a report after the fact: budgets reserved and settled per call, policy and human-approval gates, model routing and failover, a kill switch that works mid-incident.
03
Prove
An execution ledger records every call, route decision, token, and cost, with policy-governed prompt and response capture, so your spend and value claims are evidence, not estimates.
How Agentry compares
Not a gateway. Not observability. Not a cost tool.
Adjacent tools see pieces. Agentry governs the whole.
Discover agents across clouds, SaaS, frameworks, K8s
- LLM Observability
- No
- Cloud-native Guardrails
- Own platform only
- Agent Registries
- Catalog focus
- IT Cost Tools
- No
Enforce budgets per call, in flight
- LLM Observability
- Observe only
- Cloud-native Guardrails
- Quota-level
- Agent Registries
- No
- IT Cost Tools
- After the invoice
Block unregistered / spoofed callers
- LLM Observability
- No
- Cloud-native Guardrails
- Platform IAM
- Agent Registries
- Identity registry
- IT Cost Tools
- No
Route & fail over across providers by policy
- LLM Observability
- No
- Cloud-native Guardrails
- Within own models
- Agent Registries
- No
- IT Cost Tools
- No
Audit-grade economics ledger + governed prompt capture
- LLM Observability
- Traces, not governance
- Cloud-native Guardrails
- Per-platform logs
- Agent Registries
- No
- IT Cost Tools
- Cost, no AI context
Private / sovereign deployment
- LLM Observability
- Varies
- Cloud-native Guardrails
- Their cloud
- Agent Registries
- No
- IT Cost Tools
- Varies
Each adjacent category is good at its piece. None combines cross-estate discovery, in-path enforcement, and an economics ledger deployable inside your own boundary.
Most teams find at least one ungoverned agent in the first onboarding call.
Start a 3-week private pilotEconomics
The cost of not routing.
Every hardcoded endpoint spends money without making a decision. The same work, on the right model, often costs a fraction, at the same or better quality.
At scale (monthly)
| Monthly volume | Hardcoded spend | With Agentry | Monthly saving |
|---|---|---|---|
| 1M requests | $2,980 | $298 | $2,682 |
| 5M requests | $14,900 | $1,490 | $13,410 |
| 10M requests | $29,800 | $2,980 | $26,820 |
| 50M requests | $149,000 | $14,900 | $134,100 |
Assumption: 40–90% reduction applied at an 89% average. Your mix will differ; the audit measures yours.
Example · without Agentry
- Model
- Claude Sonnet
- Latency
- 5,537 ms
- Cost / request
- $0.00298
Example · with Agentry
- Model
- GPT-4o-mini
- Latency
- 3,962 ms-28.5%
- Cost / request
- $0.00010-96.8%
40–90% lower cost, depending on workload mix.The example above is one workload; the audit measures yours.
Integrations
Connects to the stack your teams already run.
Cloud, models, GPUs, data warehouses, and CI, connected once. Add a provider without a code change; attribution and cost tracking follow automatically.
Read-only by default. Automation is opt-in, scoped, and logged.
View all integrations Customer Stories
Trusted by teams running cloud and AI in production.
B
Berkshire Hathaway HomeServicesOptimization, governance, usage visibility, and AI spend controls across cloud and AI services, running in production.
BH
Berkshire Hathaway HomeServices
Why cloudverse Optimization Governance Visibility
XL
XL-SmartEnterprise cloud, data, and AI spend optimization and governance across providers, warehouses, and LLMs.
XL
XL-Smart
Why cloudverse Visibility Governance Cost Control
C
CarlsbergEnterprise governance, commitment management, and AI spend governance across cloud and AI services.
C
Carlsberg
Why cloudverse Governance Commitments Visibility
Agentry is in private pilot. When a named enterprise runs it in the execution path, it will say so here.
Resources
View all Latest guides and resources
Insight
AI is becoming the new cloud bill
Why AI spend outran the tools built to govern cloud, and what an AI-native FinOps layer does differently.
Learn More Case StudyFrom invisible spend to accountable architecture
How one digital organization gained visibility across 129 applications and $548K in multi-cloud spend.
Learn More GuideCloud governance solved infrastructure chaos. What solves AI chaos?
The case for routing, unit economics, and governance built for models and agents, not borrowed from infrastructure.
Learn MoreQuestions
The ones we get asked most.
Common questions we get asked the most
Cloud cost management is one part of it. CloudVerse is a technology spend platform covering cloud infrastructure, data platforms, SaaS, engineering, and AI workloads through one economic and governance model.
Yes. Most teams start with cloud or data, because that is where the largest attributed spend already sits, and add AI when the estate is ready to be governed rather than just counted.
The system that discovers every AI agent and model in use, governs execution in real time (budgets, policy, routing, kill switch), and records every call as evidence. It sets policy before a request runs and proves cost and outcome after.
A gateway runs the routing rule you already wrote. Agentry discovers what's actually running across your estate, including agents nobody registered, and enforces budget, identity, and policy on every call, not only the ones already wired through a gateway.
Routing overhead is measured under 15ms per request. Agentry can also operate as a decision layer without mediating all traffic. Execution stays in your control.
Yes. Agentry deploys into your cloud tenancy or on-premises. The data plane stays inside your boundary, and prompt capture is a per-workload policy: full capture, redacted, or metadata only.
Every route carries a fallback. Agentry reroutes within your constraints, and your prompts, policies, and audit trail keep working because they live in your control plane rather than the vendor's.
A scoped private pilot in a tenancy you control: discover what is already running, govern one real workload, read out the evidence. About three weeks.