Compare VM instance families and naming conventions across providers.
Balanced compute, memory, and networking for a wide range of workloads including web servers, small databases, and dev/test environments.
m6i.xlargeD4s_v5n2-standard-4High-performance processors for compute-intensive tasks such as batch processing, HPC, gaming servers, and media encoding.
c6i.2xlargeF8s_v2c2-standard-8High memory-to-CPU ratios for in-memory databases, real-time big data analytics, and caching workloads.
r6i.2xlargeE8s_v5n2-highmem-8High sequential read/write access to very large datasets on local storage, ideal for data warehousing and distributed file systems.
i3en.xlargeL8s_v3n2-standard-8 + local SSDGPU-accelerated instances for machine learning training, inference, graphics rendering, and scientific simulations.
p4d.24xlargeNC6s_v3a2-highgpu-1gCost-effective instances that provide a baseline CPU with the ability to burst above it, suited for variable workloads.
t3.microB2s_v2e2-microARM processor-based instances offering better price-performance for scale-out and cloud-native workloads.
m7g.xlargeDps_v5t2a-standard-4Very high memory instances for large in-memory databases such as SAP HANA and other memory-intensive enterprise applications.
x2idn.24xlargeM128s_v2m2-ultramem-416Purpose-built accelerators for ML inference, training, and specialized compute including custom chips from each provider.
inf2.xlargeNP10sv5litepod-4[family][generation][attributes].[size]AWS instance names encode the family, generation, processor/attribute flags, and a T-shirt size separated by a dot.
mFamily: General Purpose (M), Compute (C), Memory (R), etc.6Generation: higher is newer (5, 6, 7...)iAttribute: i = Intel, a = AMD, g = Graviton (ARM), n = network optimizedxlargeSize: nano, micro, small, medium, large, xlarge, 2xlarge, ..., metal[Family][vCPUs][Addons]_v[Generation]Azure VM sizes use a letter family prefix, vCPU count, optional addons (s = premium storage, d = local disk), and a version suffix.
DFamily: D = General Purpose, F = Compute, E = Memory, L = Storage, N = GPU4vCPU count: the number directly following the family lettersAddon: s = premium storage capable, d = local temp disk, a = AMD, p = ARMv5Version: v2, v3, v4, v5 indicating hardware generation[family]-[type]-[vCPUs]GCP machine types use a family prefix, a type describing the memory profile, and the vCPU count separated by hyphens.
n2Family: n2 = general purpose, c2 = compute, e2 = cost-optimized, m2 = memory, t2a = ARMstandardType: standard, highmem, highcpu, ultramem, megamem4vCPU count: the number of virtual CPUs[
{
"category": "General Purpose",
"description": "Balanced compute, memory, and networking for a wide range of workloads including web servers, small databases, and dev/test environments.",
"aws": {
"family": "M-series (M5, M6i, M7i)",
"naming": "[family][generation][processor].[size] e.g. m6i.xlarge",
"example": "m6i.xlarge"
},
"azure": {
"family": "D-series (Ds_v5, Ds_v4)",
"naming": "[Family][vCPUs][addons]_v[generation] e.g. D4s_v5",
"example": "D4s_v5"
},
"gcp": {
"family": "N2 / N2D / N4",
"naming": "[family]-standard-[vCPUs] e.g. n2-standard-4",
"example": "n2-standard-4"
}
},
{
"category": "Compute Optimized",
"description": "High-performance processors for compute-intensive tasks such as batch processing, HPC, gaming servers, and media encoding.",
"aws": {
"family": "C-series (C5, C6i, C7i)",
"naming": "[family][generation][processor].[size] e.g. c6i.2xlarge",
"example": "c6i.2xlarge"
},
"azure": {
"family": "F-series (Fs_v2)",
"naming": "[Family][vCPUs][addons]_v[generation] e.g. F8s_v2",
"example": "F8s_v2"
},
"gcp": {
"family": "C2 / C2D / C3",
"naming": "[family]-standard-[vCPUs] e.g. c2-standard-8",
"example": "c2-standard-8"
}
},
{
"category": "Memory Optimized",
"description": "High memory-to-CPU ratios for in-memory databases, real-time big data analytics, and caching workloads.",
"aws": {
"family": "R-series (R5, R6i, R7i)",
"naming": "[family][generation][processor].[size] e.g. r6i.2xlarge",
"example": "r6i.2xlarge"
},
"azure": {
"family": "E-series (Es_v5, Es_v4)",
"naming": "[Family][vCPUs][addons]_v[generation] e.g. E8s_v5",
"example": "E8s_v5"
},
"gcp": {
"family": "N2-highmem / M3",
"naming": "[family]-highmem-[vCPUs] e.g. n2-highmem-8",
"example": "n2-highmem-8"
}
},
{
"category": "Storage Optimized",
"description": "High sequential read/write access to very large datasets on local storage, ideal for data warehousing and distributed file systems.",
"aws": {
"family": "I-series (I3, I3en, I4i)",
"naming": "[family][generation][variant].[size] e.g. i3en.xlarge",
"example": "i3en.xlarge"
},
"azure": {
"family": "L-series (Ls_v3, Ls_v2)",
"naming": "[Family][vCPUs][addons]_v[generation] e.g. L8s_v3",
"example": "L8s_v3"
},
"gcp": {
"family": "N2-standard + local SSD",
"naming": "[family]-standard-[vCPUs] with --local-ssd e.g. n2-standard-8 + local SSD",
"example": "n2-standard-8 + local SSD"
}
},
{
"category": "GPU",
"description": "GPU-accelerated instances for machine learning training, inference, graphics rendering, and scientific simulations.",
"aws": {
"family": "P-series / G-series (P4d, P5, G5)",
"naming": "[family][generation][variant].[size] e.g. p4d.24xlarge",
"example": "p4d.24xlarge"
},
"azure": {
"family": "NC-series (NCv3, NCas_T4_v3, NC_A100_v4)",
"naming": "NC[vCPUs][GPU-variant]_v[generation] e.g. NC6s_v3",
"example": "NC6s_v3"
},
"gcp": {
"family": "A2 / G2 (A100, L4)",
"naming": "[family]-[profile]-[GPUs]g e.g. a2-highgpu-1g",
"example": "a2-highgpu-1g"
}
},
{
"category": "Burstable",
"description": "Cost-effective instances that provide a baseline CPU with the ability to burst above it, suited for variable workloads.",
"aws": {
"family": "T-series (T3, T3a, T4g)",
"naming": "[family][generation][processor].[size] e.g. t3.micro",
"example": "t3.micro"
},
"azure": {
"family": "B-series (Bs_v2, B-series)",
"naming": "[Family][vCPUs][addons]_v[generation] e.g. B2s_v2",
"example": "B2s_v2"
},
"gcp": {
"family": "E2-micro / E2-small / E2-medium",
"naming": "e2-[size] e.g. e2-micro",
"example": "e2-micro"
}
},
{
"category": "ARM-based",
"description": "ARM processor-based instances offering better price-performance for scale-out and cloud-native workloads.",
"aws": {
"family": "Graviton (M7g, C7g, R7g)",
"naming": "[family][generation]g.[size] e.g. m7g.xlarge",
"example": "m7g.xlarge"
},
"azure": {
"family": "Ampere (Dps_v5, Eps_v5)",
"naming": "[Family]p[vCPUs]s_v[generation] e.g. Dps_v5",
"example": "Dps_v5"
},
"gcp": {
"family": "Tau T2A",
"naming": "t2a-standard-[vCPUs] e.g. t2a-standard-4",
"example": "t2a-standard-4"
}
},
{
"category": "High Memory",
"description": "Very high memory instances for large in-memory databases such as SAP HANA and other memory-intensive enterprise applications.",
"aws": {
"family": "X-series / u-series (X2idn, u-24tb1)",
"naming": "[family][generation][variant].[size] e.g. x2idn.24xlarge",
"example": "x2idn.24xlarge"
},
"azure": {
"family": "M-series (Ms_v2, Mv2)",
"naming": "M[vCPUs][addons]_v[generation] e.g. M128s_v2",
"example": "M128s_v2"
},
"gcp": {
"family": "M2-ultramem / M3-ultramem",
"naming": "[family]-ultramem-[vCPUs] e.g. m2-ultramem-416",
"example": "m2-ultramem-416"
}
},
{
"category": "Accelerated Computing",
"description": "Purpose-built accelerators for ML inference, training, and specialized compute including custom chips from each provider.",
"aws": {
"family": "Inf / Trn (Inf2, Trn1)",
"naming": "[family][generation].[size] e.g. inf2.xlarge, trn1.32xlarge",
"example": "inf2.xlarge"
},
"azure": {
"family": "NP-series (NP-series for FPGAs)",
"naming": "NP[vCPUs][addons] e.g. NP10s",
"example": "NP10s"
},
"gcp": {
"family": "TPU VM (v4, v5e, v5p)",
"naming": "tpu-[version]-[topology] e.g. v5litepod-4",
"example": "v5litepod-4"
}
}
]The Multi-Cloud VM Compare tool provides a side-by-side comparison of virtual machine instance families and naming conventions across AWS (EC2), Azure (Virtual Machines), and GCP (Compute Engine). It maps equivalent instance types across providers, explains naming schemes, and compares features like burstable instances, spot/preemptible pricing, and discount programs.
Disclaimer: This tool runs entirely in your browser. No data is sent to our servers. Always verify outputs before using them in production. AWS, Azure, and GCP are trademarks of their respective owners.