> ## Documentation Index > Fetch the complete documentation index at: https://docs.tensor9.com/llms.txt > Use this file to discover all available pages before exploring further. # Virtual Machine Scale Sets costs > Compare Virtual Machine Scale Sets backend choices using explicit service-adapter planning assumptions. Tensor9 BYOC planning comparisons for `azure::1.0.0::vmss`, with backend choices shown together. Read [how to think about service-adapter costs](/byoc/service-adapters/debugging/costs/overview) for price scope and capacity qualification, or return to [additional services](/byoc/service-adapters/debugging/costs/additional-services). ## Autoscaling fleet allocation → EC2 Auto Scaling Origin API: `azure::1.0.0::vmss`. Target backend: `aws::1.0.0::autoscaling::group`. **Planning estimate with editable assumptions.** Unit rates, workload amplification and capacity assumptions are educated guesses awaiting verification. The displayed amounts are planning estimates, not verified provider list-price quotes or benchmark-qualified capacity. Region names identify the authored scenario assumptions. **Designed mapping.** This cost model describes an intended adapter. Its estimated price does not establish a delivered or selectable backend. ### Autoscaling fleet allocation · Allocated fleet instance-hours per month **Planning estimate with editable assumptions.** These authored unit rates and capacity assumptions require provider-price verification and representative benchmarks. They do not constitute verified list-price quotes or evidence that an adapter is available. Estimated monthly service charges from native source usage; no calculator input is required. Adapter hosting and Tensor9 fees are excluded. Source: Explicit source planning region; target: Explicit target planning region; currency: USD.
Autoscaling fleet allocation · Allocated fleet instance-hours per month: monthly service charges by Allocated fleet instance-hours per month. Exact values are in the following table. Autoscaling fleet allocation · Allocated fleet instance-hours per month: monthly service charges by Allocated fleet instance-hours per month. Exact values are in the following table.
Exact sampled model outputs in USD/month. Difference means target minus origin; a positive difference means the target costs more. | Allocated fleet instance-hours per month (instance-hour/month) | Origin | Target scenario | Target | Difference | | -: | -: | - | -: | -: | | 0 | 0 | Planning native backend | 0 | 0 | | 730 | 87.6 | Planning native backend | 87.6 | 0 | | 2190 | 262.8 | Planning native backend | 262.8 | 0 | | 7300 | 876 | Planning native backend | 876 | 0 | | 73000 | 8760 | Planning native backend | 8760 | 0 | | 730000 | 87600 | Planning native backend | 87600 | 0 | **Curve assumptions** | Target scenario | Assumption | Value | Unit | | - | - | -: | - | | Planning native backend | Origin price guess · Fleet allocated instance hours | 0.12 | USD/instance-hour | | Planning native backend | Origin price guess · Fleet management requests | 0 | USD/Requests | | Planning native backend | Target vCPUs per fleet instance | 2 | vCPU | | Planning native backend | Target RAM per fleet instance | 8 | GiB | | Planning native backend | Target price guess · read | 0 | USD/read | | Planning native backend | Target price guess · write | 0 | USD/write | | Planning native backend | Target price guess · provider operation | 0 | USD/provider operation | | Planning native backend | Target price guess · GiB-month | 0.1 | USD/GiB-month | | Planning native backend | Target price guess · published GiB | 0 | USD/published GiB | | Planning native backend | Target price guess · delivered GiB | 0 | USD/delivered GiB | | Planning native backend | Target price guess · network GiB | 0.09 | USD/network GiB | | Planning native backend | Target price guess · replica GiB-month | 0 | USD/replica GiB-month | | Planning native backend | Target price guess · vCPU-hour | 0.04 | USD/vCPU-hour | | Planning native backend | Target price guess · GiB-hour | 0.005 | USD/GiB-hour | | Planning native backend | Target price guess · resource-hour | 0 | USD/resource-hour | | Planning native backend | Target price guess · resource-month | 0 | USD/resource-month | | Planning native backend | Target price guess · input token | 0 | USD/input token | | Planning native backend | Target price guess · output token | 0 | USD/output token | | Planning native backend | Approximate target service minimum | 0 | USD/month | | Planning native backend | Minimum primary CPU | 2 | vCPU | | Planning native backend | Minimum primary RAM | 8 | GiB | | Planning native backend | Minimum primary disk | 100 | GiB | | Planning native backend | Additional full-price standby copy | 0 | 0 or 1 | | Planning native backend | Allocated share of provisioned cost | 1 | fraction | | Planning native backend | Planning read/write work per vCPU-second | 1000 | operations/vCPU-second | | Planning native backend | Planning peak / mean work | 2 | multiplier | | Planning native backend | Planning primary CPU utilization | 0.7 | fraction | | Planning native backend | Resident state reserved in RAM | 0.1 | fraction | | Planning native backend | Planning disk utilization | 0.7 | fraction | | Planning native backend | Maintenance and log disk reserve | 20 | GiB | | Planning native backend | Staging database CPU unit-price guess | 0.0413 | USD/vCPU-hour | | Planning native backend | Staging database RAM unit-price guess | 0.007 | USD/GiB-hour | | Planning native backend | Staging database disk unit-price guess | 0.17 | USD/GiB-month | | Planning native backend | Dependent object sink storage price guess | 0.023 | USD/GiB-month | | Planning native backend | Dependent object sink write price guess | 0.000005 | USD/write | * Only this native meter varies; all other source usage and translation assumptions retain the displayed defaults. Editable authored planning guesses, not verified provider tariffs, benchmark measurements or a commercial quote. Other native meters retain their explicitly displayed default quantities. * Planning estimate · editable authored rates and sizing assumptions dated 2026-10-18; not verified provider prices. Explicit source planning region → Explicit target planning region, USD/month. * Source prices are reference charges, not an invoice. Target lines cover selected service usage/configuration only; adapter hosting and Tensor9 fees are excluded. * Explicit tariff samples are not performance measurements or capacity qualification. No interpolation or break-even guarantee is implied. * Variant assumptions show the first axis sample; any axis-dependent configuration is described by this curve's workload note. * Editable authored planning guesses, not verified provider tariffs, benchmark measurements or a commercial quote. * Default-zero rates are explicit assumptions, not evidence that the deployed service or resource is free. * Provisioned-unit minimums use an editable approximate allocation floor. Modeled target service cost is max(usage/allocation, minimum), with billable network, durable staging and dependent sinks added separately; actual RU/TU sizing, scaling increments and throughput remain unqualified. * Durable staging is priced once: provisioned targets include adapter state in their selected allocation; native targets add a separate staging allocation when required. Adapter hosting is a separate allocation and must exclude resources already priced as staging, workers or target capacity. * A declared adapter mapping and cost model do not establish implementation availability or service equivalence. * This exact directed mapping has a design profile. Cost modeling does not establish an implemented or deployable adapter. * All default unit prices and capacity coefficients are editable authored planning guesses, not measured capacity or a provider quote. * Management requests do not reveal resource allocation or workload traffic. Provisioned resources and traffic are independent inputs. * This native fleet resource comparison is separate from the Auto Scaling runtime discovery adapter and its supported operations. * Instance-hours are priced exactly once as CPU and RAM allocation; management requests add no assumed fleet instances. * All target rates are editable authored planning guesses, not verified current list prices, customer discounts or benchmark evidence. * Allocated VM/fleet CPU and memory hours are explicit physical usage. Management API counts never create VM-hours. Cost scope: Modeled target native service and explicitly modeled state allocation; hosting and Tensor9 fees only when supplied. Excluded costs and workload features: * Instance disks, traffic, licenses, load balancers, spare replacement instances and metric collection. * Taxes, discounts, credits and free-tier allowances unless explicitly reflected in edited rates. * Unmodeled dependent services, task execution and operations outside the selected workload family. Planning assumptions authored: 2026-10-18. Model: `plan-376d90fcbbdb21827f801f0b6108f55e2c6f5bae` / `2026-10-18.planning.1`. Curve: `workload-1`. ## Autoscaling fleet allocation → Managed Instance Group Origin API: `azure::1.0.0::vmss`. Target backend: `google::1.0.0::compute::instance-group-manager`. **Planning estimate with editable assumptions.** Unit rates, workload amplification and capacity assumptions are educated guesses awaiting verification. The displayed amounts are planning estimates, not verified provider list-price quotes or benchmark-qualified capacity. Region names identify the authored scenario assumptions. **Designed mapping.** This cost model describes an intended adapter. Its estimated price does not establish a delivered or selectable backend. ### Autoscaling fleet allocation · Allocated fleet instance-hours per month **Planning estimate with editable assumptions.** These authored unit rates and capacity assumptions require provider-price verification and representative benchmarks. They do not constitute verified list-price quotes or evidence that an adapter is available. Estimated monthly service charges from native source usage; no calculator input is required. Adapter hosting and Tensor9 fees are excluded. Source: Explicit source planning region; target: Explicit target planning region; currency: USD.
Autoscaling fleet allocation · Allocated fleet instance-hours per month: monthly service charges by Allocated fleet instance-hours per month. Exact values are in the following table. Autoscaling fleet allocation · Allocated fleet instance-hours per month: monthly service charges by Allocated fleet instance-hours per month. Exact values are in the following table.
Exact sampled model outputs in USD/month. Difference means target minus origin; a positive difference means the target costs more. | Allocated fleet instance-hours per month (instance-hour/month) | Origin | Target scenario | Target | Difference | | -: | -: | - | -: | -: | | 0 | 0 | Planning native backend | 0 | 0 | | 730 | 87.6 | Planning native backend | 87.6 | 0 | | 2190 | 262.8 | Planning native backend | 262.8 | 0 | | 7300 | 876 | Planning native backend | 876 | 0 | | 73000 | 8760 | Planning native backend | 8760 | 0 | | 730000 | 87600 | Planning native backend | 87600 | 0 | **Curve assumptions** | Target scenario | Assumption | Value | Unit | | - | - | -: | - | | Planning native backend | Origin price guess · Fleet allocated instance hours | 0.12 | USD/instance-hour | | Planning native backend | Origin price guess · Fleet management requests | 0 | USD/Requests | | Planning native backend | Target vCPUs per fleet instance | 2 | vCPU | | Planning native backend | Target RAM per fleet instance | 8 | GiB | | Planning native backend | Target price guess · read | 0 | USD/read | | Planning native backend | Target price guess · write | 0 | USD/write | | Planning native backend | Target price guess · provider operation | 0 | USD/provider operation | | Planning native backend | Target price guess · GiB-month | 0.1 | USD/GiB-month | | Planning native backend | Target price guess · published GiB | 0 | USD/published GiB | | Planning native backend | Target price guess · delivered GiB | 0 | USD/delivered GiB | | Planning native backend | Target price guess · network GiB | 0.09 | USD/network GiB | | Planning native backend | Target price guess · replica GiB-month | 0 | USD/replica GiB-month | | Planning native backend | Target price guess · vCPU-hour | 0.04 | USD/vCPU-hour | | Planning native backend | Target price guess · GiB-hour | 0.005 | USD/GiB-hour | | Planning native backend | Target price guess · resource-hour | 0 | USD/resource-hour | | Planning native backend | Target price guess · resource-month | 0 | USD/resource-month | | Planning native backend | Target price guess · input token | 0 | USD/input token | | Planning native backend | Target price guess · output token | 0 | USD/output token | | Planning native backend | Approximate target service minimum | 0 | USD/month | | Planning native backend | Minimum primary CPU | 2 | vCPU | | Planning native backend | Minimum primary RAM | 8 | GiB | | Planning native backend | Minimum primary disk | 100 | GiB | | Planning native backend | Additional full-price standby copy | 0 | 0 or 1 | | Planning native backend | Allocated share of provisioned cost | 1 | fraction | | Planning native backend | Planning read/write work per vCPU-second | 1000 | operations/vCPU-second | | Planning native backend | Planning peak / mean work | 2 | multiplier | | Planning native backend | Planning primary CPU utilization | 0.7 | fraction | | Planning native backend | Resident state reserved in RAM | 0.1 | fraction | | Planning native backend | Planning disk utilization | 0.7 | fraction | | Planning native backend | Maintenance and log disk reserve | 20 | GiB | | Planning native backend | Staging database CPU unit-price guess | 0.0413 | USD/vCPU-hour | | Planning native backend | Staging database RAM unit-price guess | 0.007 | USD/GiB-hour | | Planning native backend | Staging database disk unit-price guess | 0.17 | USD/GiB-month | | Planning native backend | Dependent object sink storage price guess | 0.023 | USD/GiB-month | | Planning native backend | Dependent object sink write price guess | 0.000005 | USD/write | * Only this native meter varies; all other source usage and translation assumptions retain the displayed defaults. Editable authored planning guesses, not verified provider tariffs, benchmark measurements or a commercial quote. Other native meters retain their explicitly displayed default quantities. * Planning estimate · editable authored rates and sizing assumptions dated 2026-10-18; not verified provider prices. Explicit source planning region → Explicit target planning region, USD/month. * Source prices are reference charges, not an invoice. Target lines cover selected service usage/configuration only; adapter hosting and Tensor9 fees are excluded. * Explicit tariff samples are not performance measurements or capacity qualification. No interpolation or break-even guarantee is implied. * Variant assumptions show the first axis sample; any axis-dependent configuration is described by this curve's workload note. * Editable authored planning guesses, not verified provider tariffs, benchmark measurements or a commercial quote. * Default-zero rates are explicit assumptions, not evidence that the deployed service or resource is free. * Provisioned-unit minimums use an editable approximate allocation floor. Modeled target service cost is max(usage/allocation, minimum), with billable network, durable staging and dependent sinks added separately; actual RU/TU sizing, scaling increments and throughput remain unqualified. * Durable staging is priced once: provisioned targets include adapter state in their selected allocation; native targets add a separate staging allocation when required. Adapter hosting is a separate allocation and must exclude resources already priced as staging, workers or target capacity. * A declared adapter mapping and cost model do not establish implementation availability or service equivalence. * This exact directed mapping has a design profile. Cost modeling does not establish an implemented or deployable adapter. * All default unit prices and capacity coefficients are editable authored planning guesses, not measured capacity or a provider quote. * Management requests do not reveal resource allocation or workload traffic. Provisioned resources and traffic are independent inputs. * This native fleet resource comparison is separate from the Auto Scaling runtime discovery adapter and its supported operations. * Instance-hours are priced exactly once as CPU and RAM allocation; management requests add no assumed fleet instances. * All target rates are editable authored planning guesses, not verified current list prices, customer discounts or benchmark evidence. * Allocated VM/fleet CPU and memory hours are explicit physical usage. Management API counts never create VM-hours. Cost scope: Modeled target native service and explicitly modeled state allocation; hosting and Tensor9 fees only when supplied. Excluded costs and workload features: * Instance disks, traffic, licenses, load balancers, spare replacement instances and metric collection. * Taxes, discounts, credits and free-tier allowances unless explicitly reflected in edited rates. * Unmodeled dependent services, task execution and operations outside the selected workload family. Planning assumptions authored: 2026-10-18. Model: `plan-5e6c83d6f4ca1bc0e7997473315ff6746d53cf9a` / `2026-10-18.planning.1`. Curve: `workload-1`. See the [service catalog](/byoc/service-adapters/catalog) for operation coverage, supported directions and backend limitations. This documentation is built and hosted on [Mintlify](https://mintlify.com), a developer documentation platform.