DoiT gave us the confidence to move from experimentation to production. They helped us understand the right way to build AI for the real world.
Milad Rezazadeh, CTO
you simply can't tag
FinOps Without Tagging
Your tagging program will never finish. Attribute™ allocates 100% of cloud spend at runtime, with no tags, no code changes, and full allocation by next week.
tagging carried FinOps for a decade.
Modern infrastructure broke it.
Even mature programs track only 43% of cloud costs at the unit level (Gartner, 2025). The gap keeps widening because the fastest-growing spend categories can't be tagged at all.
Shared infrastructure has no single owner
Kubernetes clusters, Kafka, databases, and queues serve many teams at once. A tag assigns one owner. It can't split what was never separable.
You can't tag a customer
Tags track infrastructure. Customers move through it. In a multi-tenant architecture there is no resource to tag, so cost-to-serve stays a manual exercise.
AI has no tagging surface
LLM gateways, shared GPU clusters, and AI agents produce one line item on the bill. Every inference call looks identical at the billing layer.
Network costs have no source data
Egress, cross-AZ transfer, NAT gateways. The bill arrives, but billing exports never say which workload caused the traffic.
the real issue
Every tag-based tool reads the wrong data source.
Tag-based tools read billing exports. Billing exports describe resources, not usage. They can tell you an EC2 instance cost $40K, but never which team, customer, or feature consumed it.
Attribut™ deploys a lightweight eBPF sensor that reads runtime network traffic. It sees which workload called which resource, which customer ID rode in on the request, and which model answered the inference call. Every dollar maps back to the consumer that drove it, based on what actually happened rather than labels someone remembered to apply.
100%
of workloads attributed
15min
sensor install, no code changes
1-day
allocation, not months of tagging
how it works
How FinOps works without tags
01. 15-minute install. A lightweight eBPF sensor deploys to your cluster via Helm, plus a Terraform billing integration. No code or configuration changes.
02. The sensor reads runtime data. eBPF observes traffic to every cloud resource: compute, databases, storage, Kafka, GPUs, and LLM endpoints. Deep packet inspection extracts identifiers like JWT tokens, HTTP headers, and client IDs directly from the traffic.
03. Cost lands where it belongs. Every dollar is attributed to the customer, team, feature, or AI agent that drove it. Shared resources split by actual consumption, not even splits or estimates.
tagging was never an answer to FinOps
Tagging program vs. runtime attribution
| Capability | Attribute™ | Tagging Program |
|---|---|---|
| Attribution & Allocation | ||
| Time to meaningful data | Native Day one, as soon as the sensor observes traffic | High-effort Weeks to months of tagging work |
| Coverage | Native 100% of workloads attributed | Manual integration 43% of costs tracked at unit level (industry average) |
| Shared resources | Native Allocated by actual consumption | Not available Even splits, pro-rated or rough estimates |
| Customer-level cost | Native Auto-detected from runtime traffic | Not available No concept of a customer |
| AI and LLM spend | Native Token-level allocation per customer, feature, or team | Limited with inference profiles Total spend only |
| Self-managed infra (Kafka, Elasticsearch on EC2) | Native Sensor sees inside the workload | Not available Invisible in billing exports |
| Network costs | Native Traced to the workload that caused the traffic | Not available Unattributable |
| Ongoing maintenance | Native None. Attribution follows the traffic | High-effort Policy enforcement, engineer chasing, quarterly cleanup |
Native capabilityPartial / preview / via integrationNot available
already invested in tags?
Keep your tags.
Teams that spent years on tagging worry the investment is wasted. It isn't. Everything they built keeps working.
Attribute™ ingests existing tags as one more signal. The difference is that attribution no longer depends on them. The 57% of spend your tags never reached gets allocated anyway, and the tagging backlog stops blocking showback, chargeback, and per-customer margins.
Attribute™ replaces months-long tagging programs with a lightweight runtime sensor
FinOps without tagging
Other FinOps tools read your billing exports. Attribute™ reads runtime data and maps costs to the workload that spent it. Instantly attribute AI, network, databases and other shared spend to customers, workloads and features.
Certified and production-safe
The sensor is read-only and runs sandboxed in the kernel. Attribute™ is SOC 2 Type 2 and ISO 27001 certified, and a FinOps Certified Platform.
AICPA
SOC 2
AICPA
ISO 27001
FinOps
Platform
every spike has an owner and an explanation.
Every FinOps tool can tell you EC2 spend rose 17% overnight
Then, the investigation starts: days of tag archaeology, dashboard digging, and Slack threads to find out the "why". Attribute™ skips the investigation.
- Get the cause in the alert itself. "ACME Corp is up $44K, driven by the /api/checkout feature launched 45 minutes ago."
- Trace the full chain from the spike to the workload, the resource behind it, and the customer or team that drove it.
- Catch runaway costs before they hit margins.
- Route every anomaly to its owner automatically, since it already knows which team it belongs to.
three things you can finally do
without a tagging program
Each of these normally waits on a tagging program that never finishes. Attribute makes them day-one capabilities.
Showback/chargeback from day one
Allocate shared infrastructure across teams by real usage. Every stakeholder, from engineering to finance, works from the same numbers, so cost reports stop getting disputed.
True cost per customer
COGS and margin per account, auto-detected from traffic. One customer found 360+ accounts where cost exceeded revenue, over $1.3M in losses that were invisible before.
AI cost intelligence
Token-level attribution per customer, feature, and team across OpenAI, Anthropic, Bedrock, and Vertex AI. Works through managed and self-hosted LLM gateways.
“”
We eliminated the need to tag thousands of resources. Attribute gave us full cost visibility with zero tags required.
Ziv Sivan, VP of Engineering at Akamai
FinOps without tagging
Retire the tagging backlog.
Install in 30 minutes and see per-customer, per-feature, per-team allocation by next week. Keep the tags you have and lose the dependence on them.
Frequently asked
questions
Can you do FinOps without tagging?
Yes. Tagging is one method of cost allocation, not a requirement of FinOps. Runtime attribution reads live traffic to determine which team, customer, or feature consumed each resource, producing allocation that tags can't reach: shared infrastructure, AI workloads, network traffic, and multi-tenant customer costs.
What's wrong with tagging for cloud cost allocation?
Tagging has three structural problems. Coverage decays because tags require ongoing human effort. Shared infrastructure can't be split accurately because a tag assigns one owner per resource. And the fastest-growing spend categories, AI and network, have no tagging surface at all. Industry data shows only 43% of cloud costs are tracked at the unit level.
Do I need to remove my existing tags to use Attribute™?
No. Existing tags keep working and Attribute uses them as an additional signal. Attribution no longer depends on them, so untagged and untaggable spend is allocated anyway.
How does runtime cost attribution work?
A lightweight eBPF sensor observes network traffic at runtime. Deep packet inspection identifies workloads, customer identifiers, and model calls directly in the traffic, then correlates them with billing data to allocate every dollar by actual consumption. No code or configuration changes are required.
How long until I see allocated costs?
Most customers see attributed data the same day. The sensor installs in about 15 minutes and attribution begins as soon as it observes traffic.
Does this cover AI and LLM costs?
Yes. The sensor reads traffic into and out of LLM gateways and API endpoints across OpenAI, Anthropic, Bedrock, Azure OpenAI, and Vertex AI, attributing token consumption to the customer, feature, or team that triggered each call.
Is the eBPF sensor safe to run in production?
Yes. eBPF runs sandboxed in the kernel and is the same technology behind mainstream observability and security tooling. The sensor is read-only, requires no application changes, and Attribute is SOC 2 Type 2 and ISO 27001 certified.
What they say
Attribute™'s cost grouping technology took our cost visibility and allocation to a whole new level. Now, our teams are fully accountable for their budgets, significantly improving our cloud efficiency and helping us minimize unnecessary costs.
Eli Zilbershtein, Head of DevOps, Hippo
You can't tag a customer in a multi-tenant environment. Attribute™ finally shows us what each customer costs and what's driving those costs.
Omri Cohen, Director of Engineering, Platform
Attribute™'s data is truly unmatched. No other solution on the market could deliver the precise customer cost and usage profiles we needed in such a complex infrastructure. Within weeks, the data from Attribute™ transformed our understanding of cost structures, influencing key strategic decisions in pricing, renegotiations, and market positioning.
Jonathan Langer, COO, Claroty
Attribute™ simplified tracking customer costs in our multi-tenant environments. Customer cost measurement is now clear and standardized, and finance gets the business context they need. Integration was quick and required no changes.
Kfir Lippmann, CFO, Salt Security
Attribute™ translates complex cloud bills into actionable, business-centric insights that empower our engineering teams to take true ownership of their costs.
Balamurugan Mohandossgandhi, Head of IT and Infrastructure, PropertyGuru
This has let us get a better idea of what our cost of goods sold really is. It's not every day you come across something that delivers value as quickly as yours did for us. I was seeing useful insights inside the POC, and we had only deployed it to a couple of real clusters.
Jason Moore, Principal DevOps Engineer, Accrete AI
Eliminating the need to tag thousands of resources has freed up my team and we've invested our efforts in enhancing our platform significantly.
Ziv Sivan, VP of Engineering