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. Vantage
Understanding every dollar is table stakes. Recovering them is the job.
Vantage is a well-built, self-service cost platform. Broad ingestion across 20+ providers, virtual tagging, a Terraform provider, an MCP server, and Autopilot for AWS Savings Plans. If your goal is developer-friendly cost visibility, it earns its reputation.
Cloud Intelligence™ goes further into execution — across every cloud, at flat pricing. Composer tells you what to fix, CloudFlow runs the fix, PerfectScale right-sizes Kubernetes and data warehouses autonomously, PerfectScale for Commitments ladders Savings Plans and CUDs across AWS and GCP, and runtime attribution maps every dollar to the customer that drove it — no tagging required. And we never charge a percentage of your savings.
Visibility is parity. Execution is the gap.
Where the two platforms genuinely differ, and where they are at parity. We try to be precise about both.
Both platforms see every dollar. Then what?
On cost ingestion, credit where due: Vantage's 20+ native integrations — AWS, Azure, GCP, Kubernetes, Snowflake, Databricks, Datadog, OpenAI, Anthropic, Twilio, Fastly, CircleCI, and more — make it one of the broadest visibility surfaces in the market. DoiT covers the same core estate. For seeing what you spend, the two platforms are at parity. The divergence is what allocation feeds. In Vantage, virtual tags feed cost reports, dashboards, and recommendations that engineers implement. In DoiT, the same allocations feed Composer, CloudFlow, and PerfectScale. The label that splits a cluster across teams in your cost report is the input that triggers automated rightsizing for that team's namespace. Allocations aren't a reporting layer. They're a routing layer.
// Where each is strong
- BothMulti-cloud + SaaS + AI cost ingestion
- VantageBroadest SaaS provider list (Twilio, Fastly, Temporal, Redis...)
- Cloud Intelligence™Allocations wired directly into execution engines
- Cloud Intelligence™Runtime attribution below tags entirely
Virtual tagging is still tagging. We attribute at the kernel.
Vantage's virtual tagging is a good answer to messy tag hygiene: rule-based logic that maps costs by metadata without touching provider tags. But it's still inference from metadata. A multi-tenant cluster, a shared GPU serving fifty customers, an inference endpoint behind one load balancer — the metadata simply doesn't carry the answer, no matter how good your rules are. Runtime attribution is a first-class capability inside Cloud Intelligence™. A 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.
Runtime cost attribution, no tagging required
- Per-customer COGSObserve customer identifiers in the request stream. Map cost back without tagging or tag rules.
- Per-feature AI costWhich feature shipped last month is driving $44K of net-new spend, broken down by model and token type.
- Shared resource splitGPU nodes, shared clusters, databases — by observed runtime traffic instead of declared or inferred tags.
- Network attributionEgress, NAT, cross-AZ. Attributed to the workload that generated the bytes, not aggregated per flow log.
- Day-one chargebackNo tag-rule authoring project. No metadata prerequisite. Deploy the sensor, get attribution.
Commitments: one cloud and a take-rate, or every cloud at flat pricing
Autopilot is real automation and we won't pretend otherwise. It auto-purchases AWS Compute Savings Plans and Database Savings Plans, with sensible controls. Two structural limits, both from Vantage's own documentation. One: it's AWS-only. GCP and Azure are described as planned. If you run multi-cloud, commitment automation covers one of your bills. Two: it charges 5% of savings realized, on top of the platform subscription. Their own worked example: $10K/month of compute, 30% savings found, $150/month fee. Scale that to $500K/month of compute at the same rate and the fee is $90K a year — every year, growing with your savings. PerfectScale for Commitments ladders Savings Plans, Database Savings Plans, and Compute Engine CUDs across AWS and GCP, autonomous or approval-gated, validated continuously against hourly usage. Flat platform pricing. Your savings stay yours.
// The commitment math
- Vantage Autopilot scopeAWS Compute SPs + DBSPs. RDS/ElastiCache/Redshift/OpenSearch RIs: recommendations only
- Vantage pricing5% of savings realized, plus subscription
- PerfectScale scopeAWS + GCP. Laddered, risk-aware, hourly-usage validated
- PerfectScale pricing0% of savings. Flat platform pricing
Recommendations: a curated library vs. a policy engine you can program
Vantage Cost Recommendations cover financial commitments, rightsizing, idle resources, storage optimization, and generational upgrades across AWS, Azure, GCP, Datadog, Cloudflare, and Kubernetes. A solid, curated set. Composer runs 800+ recipes continuously against your billing data, resource configurations, event timeline, and full resource graph. The substantive difference is programmability: the policy editor lets you author your own recipes against your own data, simulate them against historical periods, then enable them in production. Custom recipes are first-class — same telemetry, same actions, same enforcement as built-in ones. If your architecture has a cost pattern nobody else has, you can encode it. Both platforms ship agentic AI. Vantage's FinOps Agent (Slack + console) and MCP server are genuinely good. DoiT's FinOps AI and MCP server cover the same ground. Parity on the assistant. The difference is what the assistant can trigger.
// Recipes you won't find in a curated library
- RDS engines nearing EOL by region
- gp2 → gp3 by your actual IOPS
- Time-to-tag leakage
- Bedrock cache effectiveness
- EKS extended support drift
- Your own — authored, simulated, enforced
CloudFlow runs the change. Today.
Vantage's own documentation is candid here: Autopilot "does not impact or make changes to your infrastructure." The FinOps Agent executes financial commitment purchases; direct infrastructure changes are described as a future capability. Their GitHub integration — in private preview — opens issues from recommendations for a coding agent or engineer to pick up. The recommendation still ends in someone else's queue. CloudFlow is an execution layer, shipping now. Visual where it should be, code-extensible where it has to be, with rollback safety. The finding becomes the action, not a ticket about the action. If your bottleneck is "engineers know what to do but don't have time to do it," a better-shaped GitHub issue 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
Kubernetes: metrics and advice, or reconciled requests and limits
The Vantage K8s Agent collects granular metrics, breaks compute down by namespace and label, surfaces pod waste and idle cluster cost, and generates rightsizing recommendations. Good telemetry, honest product. PerfectScale for Kubernetes closes the loop. Autonomous, workload-aware rightsizing at pod and container level, GPU utilization tracked separately, stability-first guardrails per environment, policies that respect business-hours criticality. Cost numbers in a dashboard don't shrink your bill. Reconciled requests and limits do. Customers typically see 30–50% K8s cost reductions.
// K8s capability split
- BothNamespace / label / pod cost allocation
- BothIdle and waste identification
- Cloud Intelligence™ onlyAutonomous rightsizing execution
- Cloud Intelligence™ onlyStability guardrails + GPU-aware policies
- Typical result30–50% K8s reduction
Data platforms — past visibility
Vantage integrates Snowflake and Databricks for cost visibility: allocation, anomaly alerts, DBU-plus-infrastructure joins, commitment health checks. You see what you spent, cleanly. 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
AI cost — visibility is parity. Optimization and attribution are not.
Vantage's AI story is real: native OpenAI, Anthropic, Cursor, and Anyscale integrations, plus an MCP server for querying cost data from ChatGPT or Claude. For seeing AI spend, the platforms are at parity — and Vantage's SaaS breadth arguably gives them an edge on niche AI providers. What's different is what happens next. 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. And runtime attribution closes the loop no tag rule can: when the kernel sensor sees an inference call cross the network, it identifies which customer made it, which feature triggered it, and which model served it — tied to the GPU-hour or provider invoice that paid for it.
// What's actually different
- BothAI provider cost visibility + MCP server
- Cloud Intelligence™ OnlyInference pipeline audit + GPU rightsizing
- Cloud Intelligence™ OnlyComposer recipes with execution paths
- Cloud Intelligence™ OnlyPer-customer AI COGS via runtime attribution
Who shows up, and what the bill looks like
Vantage is deliberately self-serve: fixed-rate plans, free tier, dedicated account rep at the enterprise level. That's a feature for teams that want a tool, not a partner. DoiT includes Forward Deployed Engineers — engineers who write code in your environment. Architecture reviews, K8s tuning, GenAI inference audits, on-call when production breaks. Same phone number, same SLA, no separate SOW. And procurement: DoiT resells AWS, GCP, and Azure at no markup, with the platform bundled. Vantage is platform-only. For multi-cloud teams, consolidation often pays for the platform before the optimization work even starts — and there's no percentage-of-savings fee compounding on top.
// Commercial model
- VantageFixed plans + 5% of savings on Autopilot/Agent commitments
- Cloud Intelligence™Flat platform pricing, or bundled with resold billing at no markup
- Vantage expertsDedicated account rep (enterprise)
- DoiT expertsFDEs who write code in your environment
No marketing categories. The questions FinOps practitioners actually ask when evaluating. Sourced from public documentation on both sides.
One row per practitioner question.
| Capability | Cloud Intelligence™ | Vantage |
|---|---|---|
| // Attribution & allocation | ||
| Cost allocation across multi-cloud, K8s, data platforms, AI | Native Out-of-the-box, multi-method: usage %, utilization, shared cost spreads. | Native Virtual tagging across 20+ providers — one of the broadest ingestion surfaces available. |
| Tagless / runtime cost attribution | Native Kernel-level telemetry (eBPF). No tagging or tag rules required. | Rule-based only Virtual tagging infers from metadata; cannot observe runtime traffic. |
| Per-customer COGS without tagging | Native Runtime customer-identifier observation in the request stream. | Via virtual tags + unit costs Requires authored mapping rules and identifiable metadata. |
| Network cost attribution (egress, NAT, cross-AZ) by workload | Native Workload-level attribution from observed runtime traffic. | Network Flow Reports VPC flow log analysis for AWS; visibility, not per-workload attribution. |
| // Workload optimization | ||
| K8s cost allocation (pod / namespace / cluster) | Native | Vantage K8s Agent Granular metrics by namespace and label. |
| K8s autonomous rightsizing (pod, container, GPU) | PerfectScale Workload-aware, stability-first, autonomous execution. Typical reduction 30–50%. | Recommendations Rightsizing recommendations; no autonomous execution. |
| Snowflake optimization (warehouse + query + idle) | PerfectScale for Snowflake Automated rightsizing, idle suspension, query efficiency. | Visibility + alerts No warehouse rightsizing or query tuning. |
| Databricks optimization | Visibility, Insights & Optimization | Visibility + alerts DBU + underlying infra cost joins. |
| AI / LLM cost visibility | 9 AI Providers & Custom Models per-customer, per-feature, per-unit | OpenAI, Anthropic, Cursor, Anyscale Native provider integrations. |
| AI workload optimization (inference, GPU, model selection) | PerfectScale GenAI + Composer | Visibility only |
| // Commitments & rate optimization | ||
| Autonomous commitment purchasing — cloud coverage | AWS + GCP SPs, DBSPs, and Compute Engine CUDs. Laddered, hourly-usage validated, autonomous or approval-gated. | AWS only Compute SPs + DBSPs via Autopilot/Agent. GCP and Azure described as planned. RDS/ElastiCache/Redshift/OpenSearch RIs: recommendations only. |
| Cost of commitment automation | Included Flat platform pricing. 0% of savings taken as fees. | 5% of savings realized Charged on Autopilot and FinOps Agent commitment purchases, on top of subscription. |
| Commitment / ESR reporting | Unified, multi-cloud | Financial Commitment Reports |
| // Intelligence & automation | ||
| Anomaly detection | Real-time, with topology context | All providers, Slack/Teams/email |
| Curated recommendations engine | Composer — 800+ recipes With custom policy editor + historical simulation. | Cost Recommendations Commitments, rightsizing, idle, storage, generational upgrades. |
| Custom policy authoring with historical simulation | Native Author, simulate against history, then enforce. | Not available VQL queries data; recommendation set is curated, not authorable. |
| Agentic AI assistant | FinOps AI | FinOps Agent Slack + console; executes commitment purchases. |
| MCP server for LLM access to cost data | Native | Hosted + local, ChatGPT App |
| Infrastructure-level automation / remediation | CloudFlow Executes changes today. Visual + code, 40+ templates, rollback safety. | Not GA Autopilot makes no infrastructure changes; Agent infra actions described as future; GitHub issue creation in their private preview. |
| FinOps as Code (Terraform provider for platform config) | Platform APIs Programmatic access via APIs and Zapier; no Terraform provider. | Terraform provider Every Vantage resource manageable as code. |
| Architecture / resource graph with cost overlay | Cloud Diagrams Live topology with cost + performance overlays. | Not available |
| // Integrations | ||
| Cost source / billing data ingestion | Broad coverage AWS, GCP, Azure, K8s, Snowflake, Databricks, BigQuery, Datadog, MongoDB, OpenAI, Anthropic, more. | 20+ native providers Including Twilio, Fastly, CircleCI, Temporal, Redis, Cursor, Anyscale, Vercel, custom providers. |
| Ticketing & work-tracking integrations | Jira, Asana, GitHub Issues | Jira GitHub issue creation via FinOps Agent in their private preview. |
| Comms integrations | Slack, Teams, Discord, Gmail | Slack, Teams, email |
| Incident management integrations | PagerDuty | Not available |
| General workflow automation | Zapier (2,000+ apps) + Platform APIs | APIs + Terraform Strong programmatic surface; no Zapier-style app fan-out. |
| // Procurement, pricing & experts | ||
| Multi-cloud procurement / billing | Optional cloud procurement, no markup AWS / GCP / Azure. Bundled platform access. | Platform only MSP partner program exists for MSPs using Vantage, not procurement. |
| Pricing model | Flat / bundled No percentage-of-savings fees anywhere. | Fixed plans + savings take-rate 5% of savings on automated commitments; agent token billing planned. |
| Included expertise | Forward Deployed Engineers Write code in your environment. K8s tuning, inference audits, incident response. | Dedicated account rep Enterprise tier. Self-serve by design elsewhere. |
| Time to first realised savings | Days Out-of-the-box recipes fire on connect; execution engines act on them. | Days for visibility; you implement Recommendations land fast; realizing them is engineering work (except AWS commitments). |
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 Vantage alternative?
Yes. Vantage is a self-service cloud cost platform focused on visibility and reporting across 20+ providers. Cloud Intelligence™ includes that visibility, plus autonomous execution: infrastructure remediation (CloudFlow), workload optimization (PerfectScale for Kubernetes and Snowflake), commitment automation across AWS and GCP (PerfectScale for Commitments), runtime cost attribution without tagging, and Forward Deployed Engineers included in the platform.
What's the single biggest difference between DoiT and Vantage?
Execution scope and pricing model. Vantage automates one thing end-to-end — AWS Savings Plan purchasing — and charges 5% of the savings for it. Everything else is recommendations your engineers implement. DoiT executes across workloads (K8s, Snowflake, GPU), commitments (AWS and GCP), and infrastructure (CloudFlow), at flat pricing with no percentage of savings taken as fees.
Does Vantage automate commitments on GCP or Azure?
No. Per Vantage's documentation, Autopilot supports only AWS, with GCP and Azure described as planned. It automates AWS Compute Savings Plans and Database Savings Plans; Reserved Instances for RDS, ElastiCache, Redshift, and OpenSearch get recommendations only. PerfectScale for Commitments automates purchasing across AWS and GCP — Savings Plans, Database Savings Plans, and Compute Engine CUDs — with laddered, risk-aware purchases validated continuously against hourly usage. Azure is on the roadmap.
How much does Vantage Autopilot cost compared to PerfectScale for Commitments?
Vantage charges 5% of savings realized through Autopilot and FinOps Agent commitment purchases, on top of the platform subscription. Their own example: $10K/month of compute spend with 30% savings found means a $150/month fee. The fee scales with your savings, indefinitely. PerfectScale for Commitments is included in DoiT's flat platform pricing — 0% of savings taken as fees.
Does Vantage optimize Kubernetes?
The Vantage K8s Agent provides granular cost metrics by namespace and label, identifies pod waste and idle cluster cost, and generates rightsizing recommendations. Implementation is your team's work. PerfectScale for Kubernetes executes autonomous, workload-aware rightsizing at pod and container level, with GPU utilization tracking and stability-first guardrails. Customers typically see 30–50% K8s cost reductions.
Does Vantage execute infrastructure changes?
Not in general availability. Vantage's documentation states Autopilot "does not impact or make changes to your infrastructure." The FinOps Agent executes financial commitment purchases; direct infrastructure changes are described as a future capability, and the GitHub integration — currently in their private preview — opens issues from recommendations rather than executing changes. DoiT CloudFlow executes infrastructure changes today, with rollback safety and 40+ ready templates.
Can I get per-customer cloud cost without tagging?
With DoiT, yes. Runtime cost attribution in Cloud Intelligence™ uses kernel-level telemetry (eBPF) to observe actual traffic and resource consumption, mapping every dollar to the customer, feature, team, or AI agent that drove it — no tagging or tag rules required. Vantage's virtual tagging is a good answer to tag hygiene, but it's rule-based inference from metadata: shared clusters, shared GPUs, and multi-tenant inference endpoints stay opaque to it because the metadata doesn't carry the answer.
How does DoiT handle Snowflake and Databricks costs vs. Vantage?
Both platforms provide Snowflake and Databricks cost visibility, allocation, and anomaly alerts. The difference is optimization: PerfectScale for Snowflake provides automated warehouse rightsizing, idle suspension, and query efficiency recommendations with cost impact preview before commit. Vantage's data platform integrations stop at visibility and commitment health checks.
Does DoiT have an MCP server and AI assistant like Vantage?
Yes. DoiT ships an MCP server for querying your cost data from Claude, ChatGPT, and other LLM clients, and FinOps AI, an agentic FinOps assistant. Vantage's MCP server and FinOps Agent cover similar ground and are well built. The difference isn't the assistant — it's what the assistant can trigger. FinOps AI sits on top of execution engines that can run the fix, not just describe it.
How does DoiT pricing compare to Vantage?
Vantage uses fixed-rate platform plans plus 5% of savings realized on automated commitment purchases, with token-based billing planned for FinOps Agent conversations. DoiT can be procured as SaaS at flat platform pricing, or bundled with multi-cloud procurement — DoiT resells AWS, GCP, and Azure at no markup, and many teams find that consolidation effectively pays for the platform. DoiT never charges a percentage of your savings.
When does Vantage make more sense than DoiT?
Teams that want self-serve, developer-first cost visibility with the broadest SaaS ingestion available, FinOps-platform-as-code via Terraform, and a free tier to start on — where engineering owns implementation of recommendations and commitments are AWS-centric. The fit weakens when you run multi-cloud commitments, need autonomous workload execution, want data platform optimization beyond visibility, need per-customer attribution on shared infrastructure, or when the 5%-of-savings fee starts to compound.