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
FinOps Platform Comparison
Cloud Intelligence™ vs. CloudZero
Both platforms report cloud cost. What happens next is the question. CloudZero is a strong cost intelligence platform — 100% allocation via CostFormation, hourly anomaly detection, curated recommendations through Optimize, an agentic assistant. If your job ends at "here's what it cost and what to consider doing about it," it does that job well.
Cloud Intelligence™ goes further into execution. Composer tells you what to fix, CloudFlow runs the fix, PerfectScale right-sizes Kubernetes and data warehouses autonomously, and PerfectScale for Commitments ladders your Savings Plans against real hourly usage. We also bundle Forward Deployed Engineers who write code in your environment.
Allocation is the floor. Execution is the ceiling.
where the platforms diverge
Tagging is structurally broken — and we're fixing it at the kernel
Every FinOps practitioner knows the dirty secret. Tag-based attribution collapses on shared infrastructure. A multi-tenant K8s cluster has one set of tags. A shared GPU running inference for fifty customers has one set of tags. Egress, NAT, cross-AZ — none of it is taggable at all.
Runtime cost attribution, no tagging required
- How it worksA lightweight kernel-level sensor (eBPF) observes traffic and resource consumption directly on the host. From the wire, it reconstructs which customer hit which inference endpoint, which feature called which model, which team's job consumed which GPU-hour, and which workload generated which egress bytes.
Commitments — where the real money is
Effective Savings Rate is the number most FinOps teams quietly miss their target on. Coverage and utilization look fine on paper. ESR tells the truth. CloudZero reports coverage and utilization. The buy decision is yours, informed by their numbers and your spreadsheets. Cloud Intelligence™ runs the buy decision. Hourly usage analysis (min, max, median) across rolling windows. Laddered purchases across AWS Savings Plans, Database Savings Plans, and GCP CUDs. Approval thresholds, spend caps, pacing controls. Run it fully autonomous or require human approval before each purchase.
Why laddering matters
- The riskBet 3 years on this month's usage curve.
- Laddered approachStagger purchases, validate each step
- Re-evaluationContinuous, against real usage data
Both have a recommendation engine. They optimize for different things.
CloudZero Optimize ships a curated library of expert-designed recommendations with impact/effort scoring, surfaced inside the engineering workflows teams already use. Opinionated and prioritized. Cloud Intelligence™ takes a different approach. 800+ recipes run continuously against your data, resource configurations, event timeline, and full resource graph. The substantive difference is customizability: Composer ships with a policy editor. Write your own recipes against your data, simulate against historical periods, then enable in production. Custom recipes are first-class — same telemetry, same actions, same enforcement as built-in ones.
Recipes you won't find in provider recommenders
- RDS engines nearing EOL
- gp2 → gp3 by actual IOPS
- Time-to-tag leakage
- Bedrock cache effectiveness
- EKS extended support drift
- First-time service detection
CloudFlow runs the change. Optimize routes the recommendation.
Both companies talk about "automation." Worth being precise. CloudZero Optimize (GA June 2025) routes recommendations into Slack and Jira with impact/effort scoring and tracks realized savings. Good for keeping optimization work moving. It does not execute infrastructure changes — engineers still make the change. CloudFlow is an execution layer. The recommendation becomes the action. When your bottleneck is "engineers know what to do but don't have time to do it," routing a better-shaped Jira ticket doesn't unblock them. Running the change does.
Production CloudFlows shipping today
- gp2 → gp3 conversion w/ rollback
- Sandbox EC2 cleanup
- 365-day log retention enforcement
- First-time SKU spend alert
- GCE disk over-provisioning
- Underused RI detection
AI cost — visibility is parity. Optimization is the differentiator.
This is the row most likely to be misrepresented, so worth being precise. CloudZero has real AI cost intelligence — direct Anthropic Cost & Usage API integration, Bedrock support, allocation across feature/model/customer. For visibility and allocation of AI spend, the two platforms are at parity. What's different is what each platform does with that visibility. PerfectScale's GenAI optimization audits inference pipelines for waste — fewer tokens, smaller models, right-sized GPUs. Composer recipes cover Bedrock cache effectiveness with remediation paths. Runtime attribution closes the loop between inference call and per-customer COGS.
What's actually different
- BothToken-level visibility, per feature/model
- Cloud Intelligence™ OnlyInference pipeline audit + GPU rightsizing
- Cloud Intelligence™ OnlyComposer recipes with execution paths
- Cloud Intelligence™ OnlyPer-customer COGS via runtime sensor
Snowflake and Databricks — past visibility
CloudZero adds cost visibility for both. You see what you spent. PerfectScale for Snowflake, Databricks and BigQuery goes further: warehouse rightsizing, idle suspension, query efficiency recommendations, human-in-the-loop with cost impact preview before commit. The architectural point: data platform cost behaves nothing like infrastructure cost. Warehouses scale per query, idle time compounds at small intervals, and a single inefficient join can dominate a day's bill. You need workload-aware optimization, not just attribution.
// Data platform optimization scope
- SnowflakeWarehouse, query, idle
- DatabricksWarehouse, query, idle
- BigQueryQuery and slot visibility and optimization
One row per practitioner question.
No marketing categories. The questions FinOps practitioners actually ask when evaluating.
| Capability | Cloud Intelligence™ | CloudZero |
|---|---|---|
| // Attribution & allocation | ||
| Cost allocation across multi-cloud, K8s, data platforms, GenAI | Native Out-of-the-box, multi-method: usage %, utilization, shared cost spreads. | Native CostFormation allocates 100% regardless of tag quality. |
| Tagless / runtime cost attribution | Native Kernel-level telemetry. No tagging required. | Tag/code-based only via CostFormation rules. |
| Per-customer COGS | Native Runtime customer-identifier observation; no tagging required. | via CostFormation Requires engineering investment to model. |
| Network cost attribution (egress, NAT, cross-AZ) | Native Workload-level attribution from runtime traffic. | Not-available Aggregate / tag-inferred |
| // Workload optimization | ||
| K8s autonomous rightsizing (pod, container, GPU) | PerfectScale Workload-aware, stability-first, autonomous execution. Typical reduction 30–50%. | Rightsizing recommendations Via Optimize; no autonomous execution. |
| Shared GPU / cluster cost split by actual usage | PerfectScale + runtime attribution | CostFormation inference |
| Snowflake optimization (warehouse + query + idle) | PerfectScale for Snowflake Automated rightsizing, idle suspension, query efficiency. | Allocation + alerts only No warehouse rightsizing or query tuning. |
| Databricks optimization | Visibility, Insights & Optimization | Allocation + alerts |
| AI cost visibility — per feature / per customer | 9 AI Providers & Custom Models per-customer, per-feature, per-unit | Anthropic + Bedrock native per inference profile only |
| GenAI workload optimization (inference, GPU, model selection) | PerfectScale GenAI + Composer | Visibility only |
| // Commitments & rate optimization | ||
| Commitment purchasing (SPs / RIs / CUDs) | PerfectScale for Commitments Risk-aware laddering, autonomous or approval-gated. | Requires 3rd party solution Dashboards in-product; purchasing handled by partner. |
| Effective Savings Rate (ESR) tracking | Unified, multi-cloud | Reported |
| // Intelligence & automation | ||
| Curated recommendations engine | Composer — 800+ recipes With custom policy editor + historical simulation. | Optimize — curated library Impact/effort scoring, Slack/Jira routing. |
| Custom policy authoring with historical simulation | Native | Not available |
| Anomaly detection | Real-time, with topology context | Based on billing data |
| Agentic AI assistant | FinOps AI | Ask Advisor |
| Infrastructure-level automation / remediation | CloudFlow Visual + code, 40+ templates, executes changes. | Routes to Slack/Jira Engineers execute the change. |
| Architecture / resource graph with cost overlay | Cloud Diagrams | Not available |
| // Integrations | ||
| Cost source / billing data ingestion | Broad coverage AWS, GCP, Azure, K8s, Snowflake, Databricks, BigQuery, Datadog, MongoDB, OpenAI, Anthropic, more. | Broad coverage AnyCost framework + adapters for AWS, Azure, GCP, K8s, Snowflake, Databricks, MongoDB, NewRelic, Datadog, OpenAI, Anthropic. |
| Ticketing & work-tracking integrations | Jira, Asana, GitHub Issues | Jira only |
| Comms integrations | Slack, Teams, Discord, Gmail | Slack only |
| Incident management integrations | PagerDuty | Not available |
| Observability / dashboard integrations | Grafana, Datadog | Grafana, Backstage |
| AI assistant integrations for cost analysis | ChatGPT, Claude, MCP AI-driven analyses on top of cost reports. | Amazon Q Developer Chat AWS console only. |
| General workflow automation | Zapier (2,000+ apps) + Platform APIs | Not available |
| SaaS tools wired into automation engine | 40+ via CloudFlow Terraform, Cloudflare, Wiz, Confluent, Vercel, ClickHouse, PlanetScale, Looker, more. | N/A — no execution engine |
| // Procurement & experts | ||
| Multi-cloud procurement / billing | Optional cloud procurement, no markup AWS / GCP / Azure. Bundled platform access. | Platform only |
| Included expertise | Forward Deployed Engineers Write code in your environment. | FinOps + AWS-certified CS team Support, onboarding, best practices. |
| Time to first realised savings | Days Out-of-the-box recipes fire on connect. | Weeks for allocation; Months for full setup CostFormation modelling time. |
Native capabilityPartial / preview / via integrationNot available
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
Frequently asked
questions
Is DoiT a CloudZero alternative?
Yes, and for most multi-cloud or Kubernetes-heavy teams, a more complete one. CloudZero is a cost intelligence platform. DoiT Cloud Intelligence™ is a FinOps platform that includes cost intelligence, plus automated remediation (CloudFlow), workload optimization (PerfectScale), commitment automation (PerfectScale for Commitments), runtime cost attribution, and embedded FinOps expertise.
What's the single biggest difference between DoiT and CloudZero?
Execution. CloudZero shows you what to fix. DoiT shows you what to fix and runs the fix — through CloudFlow automation, PerfectScale rightsizing, and Forward Deployed Engineers who do the work alongside your team.
Can I get per-customer cloud cost without tagging everything?
Tagging is structurally incomplete for shared services, GPUs, databases, and network. Gartner reports roughly 43% of cloud cost gets allocated at the unit level under a tag-based approach. DoiT's runtime cost attribution uses kernel-level telemetry (eBPF) to observe actual traffic and resource consumption, then maps every dollar back to the customer, feature, team, or AI agent that drove it.
CloudZero's approach is code-driven via CostFormation — which works, but requires engineering investment to maintain.
Does CloudZero optimize Kubernetes?
CloudZero allocates 100% of Kubernetes cost with a proprietary CPU+memory allocation algorithm and surfaces rightsizing recommendations through Optimize, routed into Slack and Jira. PerfectScale for Kubernetes goes further with autonomous, workload-aware rightsizing — the platform makes the change, not just the recommendation. Customers typically see 30–50% K8s cost reductions.
Can CloudZero automate commitment purchasing?
CloudZero reports on coverage and utilization but does not purchase commitments itself — that requires a 3rd party solution. PerfectScale for Commitments handles purchasing natively: laddered purchases across AWS Savings Plans, Database Savings Plans, and GCP CUDs, validated continuously against hourly usage, with configurable guardrails and either autonomous or approval-based execution. Azure is on the roadmap.
How does DoiT handle Snowflake and Databricks costs?
Both platforms allocate Snowflake and Databricks costs and provide anomaly detection. The difference is optimization: PerfectScale for Snowflake (formerly SELECT, acquired by DoiT in early 2026) provides automated warehouse rightsizing, idle suspension, and query efficiency recommendations with cost impact preview before commit. CloudZero provides allocation and alerts; warehouse rightsizing and query optimization are not part of their Snowflake or Databricks integrations. Databricks optimization is on the DoiT roadmap.
Do I still need a FinOps team if I use DoiT?
You'll still benefit from one — FinOps is a practice, not a product. Both DoiT and CloudZero include expert staff in their platform pricing, but the models differ. CloudZero ships a FinOps- and AWS-certified customer success team focused on advisory and platform onboarding. DoiT ships Forward Deployed Engineers who write code in your environment and own outcomes across cost optimization, K8s tuning, incident response, migration, and reliability. Pick the model that matches what your team needs help with.
How does DoiT support GenAI and AI workload costs?
For visibility and allocation of AI spend, DoiT and CloudZero are at parity — CloudZero has a native Anthropic API integration and supports Bedrock plus other providers; DoiT GenAI Intelligence covers the same ground via Composer recipes.
The differentiation is optimization: PerfectScale's GenAI optimization audits inference pipelines for waste (token reduction, model selection, GPU rightsizing). Runtime attribution closes the loop by mapping AI spend to the customer or feature that triggered it.
How does DoiT pricing compare?
DoiT can be procured as SaaS (BYO billing) or bundled with multi-cloud procurement at no markup. Many teams find that consolidating AWS, GCP, or Azure billing through DoiT effectively pays for the platform. CloudZero is SaaS only, priced as a percentage of annualized cloud spend.
When does CloudZero make more sense than DoiT?
Teams whose primary deliverable is cost allocation, unit economics reporting, and curated recommendations routed into engineering workflows — where engineering then executes the changes. CloudZero is strong at this and has real depth on allocation, anomaly detection, and the Optimize workflow. The fit weakens when you need autonomous execution of infrastructure changes, commitment purchasing handled by the platform itself, warehouse-level data platform optimization, runtime attribution, or multi-cloud procurement bundled with the tool.