Cloudera Data Platform (CDP)
Public Cloud service rates
The table below reflects hourly pricing for Cloudera Data Platform Public Cloud Services offerings. The prices reflected do not include infrastructure cost. Infrastructure pricing is available through the respective cloud providers.
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Amazon Web Services
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Microsoft Azure
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Google Cloud Platform (GCP)
Data Engineering - AWS instances
| Instance | Category | Cores | vCPUs | RAM | GPU | CCUs | Rate/hr |
| m5.2xlarge | General purpose | 4 | 8 | 32 | 0 | 4.00 | $0.2800 |
| m5.4xlarge | General purpose | 8 | 16 | 64 | 0 | 8.00 | $0.5600 |
| m5.8xlarge | General purpose | 16 | 32 | 128 | 0 | 16.00 | $1.1200 |
| m5.16xlarge | General purpose | 32 | 64 | 256 | 0 | 32.00 | $2.2400 |
| m5.24xlarge | General purpose | 48 | 96 | 384 | 0 | 48.00 | $3.3600 |
| m5a.2xlarge | General purpose | 4 | 8 | 32 | 0 | 4.00 | $0.2800 |
| m5a.4xlarge | General purpose | 8 | 16 | 64 | 0 | 8.00 | $0.5600 |
| m5a.8xlarge | General purpose | 16 | 32 | 128 | 0 | 16.00 | $1.1200 |
| m5a.16xlarge | General purpose | 32 | 64 | 256 | 0 | 32.00 | $2.2400 |
| m5a.24xlarge | General purpose | 48 | 96 | 384 | 0 | 48.00 | $3.3600 |
| m5ad.2xlarge | General purpose | 4 | 8 | 32 | 0 | 4.00 | $0.2800 |
| m5ad.4xlarge | General purpose | 8 | 16 | 64 | 0 | 8.00 | $0.5600 |
| m5ad.8xlarge | General purpose | 16 | 32 | 128 | 0 | 16.00 | $1.1200 |
| m5ad.24xlarge | General purpose | 48 | 96 | 384 | 0 | 48.00 | $3.3600 |
| m5d.2xlarge | General purpose | 4 | 8 | 32 | 0 | 4.00 | $0.2800 |
| m5d.4xlarge | General purpose | 8 | 16 | 64 | 0 | 8.00 | $0.5600 |
| m5d.8xlarge | General purpose | 16 | 32 | 128 | 0 | 16.00 | $1.1200 |
| m5d.16xlarge | General purpose | 32 | 64 | 256 | 0 | 32.00 | $2.2400 |
| m5d.24xlarge | General purpose | 48 | 96 | 384 | 0 | 48.00 | $3.3600 |
| r5.2xlarge | Memory optimized | 4 | 8 | 64 | 0 | 6.67 | $0.4667 |
| r5.4xlarge | Memory optimized |
8 | 16 | 128 | 0 | 13.33 | $0.9333 |
| r5.8xlarge | Memory optimized |
16 | 32 | 256 | 0 | 26.67 | $1.8667 |
| r5.16xlarge | Memory optimized |
32 | 64 | 512 | 0 | 53.33 | $3.7333 |
| r5.24xlarge | Memory optimized | 48 | 96 | 768 | 0 | 80.00 | $5.6000 |
| r5a.2xlarge | Memory optimized | 4 | 8 | 64 | 0 | 6.67 | $0.4667 |
| r5a.4xlarge | Memory optimized | 8 | 16 | 128 | 0 | 13.33 | $0.9333 |
| r5a.8xlarge | Memory optimized | 16 | 32 | 256 | 0 | 26.67 | $1.8667 |
| r5a.16xlarge | Memory optimized | 32 | 64 | 512 | 0 | 53.33 | $3.7333 |
| r5a.24xlarge | Memory optimized | 48 | 96 | 768 | 0 | 80.00 | $5.6000 |
| r5ad.2xlarge | Memory optimized | 4 | 8 | 64 | 0 | 6.67 | $0.4667 |
| r5ad.4xlarge | Memory optimized | 8 | 16 | 128 | 0 | 13.33 | $0.9333 |
| r5ad.8xlarge | Memory optimized | 16 | 32 | 256 | 0 | 26.67 | $1.8667 |
| r5ad.24xlarge | Memory optimized | 48 | 96 | 768 | 0 | 80.00 | $5.6000 |
| r5d.2xlarge | Memory optimized | 4 | 8 | 64 | 0 | 6.67 | $0.4667 |
| r5d.4xlarge | Memory optimized | 8 | 16 | 128 | 0 | 13.33 | $0.9333 |
| r5d.8xlarge | Memory optimized | 16 | 32 | 256 | 0 | 26.67 | $1.8667 |
| r5d.16xlarge | Memory optimized | 32 | 64 | 512 | 0 | 53.33 | $3.7333 |
| r5d.24xlarge | Memory optimized | 48 | 96 | 768 | 0 | 80.00 | $5.6000 |
| c5.2xlarge | Compute optimized | 4 | 8 | 16 | 0 | 2.67 | $0.1867 |
| c5.4xlarge | Compute optimized |
8 | 16 | 32 | 0 | 5.33 | $0.3733 |
| c5.9xlarge | Compute optimized |
18 | 36 | 72 | 0 | 12.00 | $0.8400 |
| c5.12xlarge | Compute optimized |
24 | 48 | 96 | 0 | 16.00 | $1.1200 |
| c5.24xlarge | Compute optimized |
48 | 96 | 192 | 0 | 32.00 | $2.2400 |
| c5ad.2xlarge | Compute optimized |
4 | 8 | 16 | 0 | 2.67 | $0.1867 |
| c5ad.4xlarge | Compute optimized |
8 | 16 | 32 | 0 | 5.33 | $0.3733 |
| c5a.2xlarge | Compute optimized |
4 | 8 | 16 | 0 | 2.67 | $0.1867 |
| c5a.4xlarge | Compute optimized |
8 | 16 | 32 | 0 | 5.33 | $0.3733 |
| c5d.2xlarge | Compute optimized |
4 | 8 | 16 | 0 | 2.67 | $0.1867 |
| c5d.4xlarge | Compute optimized |
8 | 16 | 32 | 0 | 5.33 | $0.3733 |
| c5d.9xlarge | Compute optimized |
18 | 36 | 72 | 0 | 12.00 | $0.8400 |
| i3.2xlarge | Storage optimized | 4 | 8 | 61 | 0 | 6.42 | $0.4492 |
| i3.4xlarge | Storage optimized |
8 | 16 | 122 | 0 | 12.83 | $0.8983 |
| i3.8xlarge | Storage optimized |
16 | 32 | 244 | 0 | 25.67 | $1.7967 |
Machine Learning - AWS instances
| Instance | Category | Cores | vCPUs | RAM | GPU | CCUs | Rate/hr |
| m4.large | General purpose | 1 | 2 | 8 | 0 | 1.00 | $0.2000 |
| m4.xlarge | General purpose | 2 | 4 | 16 | 0 | 2.00 | $0.4000 |
| m4.2xlarge | General purpose | 4 | 8 | 32 | 0 | 4.00 | $0.8000 |
| m4.4xlarge | General purpose | 8 | 16 | 64 | 0 | 8.00 | $1.6000 |
| m4.10xlarge | General purpose | 20 | 40 | 160 | 0 | 20.00 | $4.0000 |
| m4.16xlarge | General purpose | 32 | 64 | 256 | 0 | 32.00 | $6.4000 |
| m5.large | General purpose | 1 | 2 | 8 | 0 | 1.00 | $0.2000 |
| m5.xlarge | General purpose | 2 | 4 | 16 | 0 | 2.00 | $0.4000 |
| m5.2xlarge | General purpose | 4 | 8 | 32 | 0 | 4.00 | $0.8000 |
| m5.4xlarge | General purpose | 8 | 16 | 64 | 0 | 8.00 | $1.6000 |
| m5.8xlarge | General purpose | 16 | 32 | 128 | 0 | 16.00 | $3.2000 |
| m5.12xlarge | General purpose | 24 | 48 | 192 | 0 | 24.00 | $4.8000 |
| m5.24xlarge | General purpose | 48 | 96 | 384 | 0 | 48.00 | $9.6000 |
| m5d.large | General purpose | 1 | 2 | 8 | 0 | 1.00 | $0.2000 |
| m5d.xlarge | General purpose | 2 | 4 | 16 | 0 | 2.00 | $0.4000 |
| m5d.2xlarge | General purpose | 4 | 8 | 32 | 0 | 4.00 | $0.8000 |
| m5d.4xlarge | General purpose | 8 | 16 | 64 | 0 | 8.00 | $1.6000 |
| m5d.12xlarge | General purpose | 24 | 48 | 192 | 0 | 24.00 | $4.8000 |
| m5d.24xlarge | General purpose | 48 | 96 | 384 | 0 | 48.00 | $9.6000 |
| m5a.large | General purpose | 1 | 2 | 8 | 0 | 1.00 | $0.2000 |
| m5a.xlarge | General purpose | 2 | 4 | 16 | 0 | 2.00 | $0.4000 |
| m5a.2xlarge | General purpose | 4 | 8 | 32 | 0 | 4.00 | $0.8000 |
| m5a.4xlarge | General purpose | 8 | 16 | 64 | 0 | 8.00 | $1.6000 |
| m5a.8xlarge | General purpose | 16 | 32 | 128 | 0 | 16.00 | $3.2000 |
| m5a.12xlarge | General purpose | 24 | 48 | 192 | 0 | 24.00 | $4.8000 |
| m5a.16xlarge | General purpose | 32 | 64 | 256 | 0 | 32.00 | $6.4000 |
| m5a.24xlarge | General purpose | 48 | 96 | 384 | 0 | 48.00 | $9.6000 |
| r3.xlarge | Memory optimized | 2 | 4 | 31 | 0 | 3.25 | $0.6500 |
| r3.2xlarge | Memory optimized | 4 | 8 | 61 | 0 | 6.42 | $1.2833 |
| r3.4xlarge | Memory optimized | 8 | 16 | 122 | 0 | 12.83 | $2.5667 |
| r3.8xlarge | Memory optimized | 16 | 32 | 244 | 0 | 25.67 | $5.1333 |
| r4.xlarge | Memory optimized | 2 | 4 | 31 | 0 | 3.25 | $0.6500 |
| r4.2xlarge | Memory optimized | 4 | 8 | 61 | 0 | 6.42 | $1.2833 |
| r4.4xlarge | Memory optimized | 8 | 16 | 122 | 0 | 12.83 | $2.5667 |
| r4.8xlarge | Memory optimized | 16 | 32 | 244 | 0 | 25.67 | $5.1333 |
| r4.16xlarge | Memory optimized | 32 | 64 | 488 | 0 | 51.33 | $10.2667 |
| r5.large | Memory optimized | 1 | 2 | 16 | 0 | 1.67 | $0.3333 |
| r5.xlarge | Memory optimized | 2 | 4 | 32 | 0 | 3.33 | $0.6667 |
| r5.2xlarge | Memory optimized | 4 | 8 | 64 | 0 | 6.67 | $1.3333 |
| r5.4xlarge | Memory optimized | 8 | 16 | 128 | 0 | 13.33 | $2.6667 |
| r5.8xlarge | Memory optimized | 16 | 32 | 256 | 0 | 26.67 | $5.3333 |
| r5.12xlarge | Memory optimized | 24 | 48 | 384 | 0 | 40.00 | $8.0000 |
| r5.16xlarge | Memory optimized | 32 | 64 | 512 | 0 | 53.33 | $10.6667 |
| r5.24xlarge | Memory optimized | 48 | 96 | 768 | 0 | 80.00 | $16.0000 |
| r5d.large | Memory optimized | 1 | 2 | 16 | 0 | 1.67 | $0.3333 |
| r5d.xlarge | Memory optimized | 2 | 4 | 32 | 0 | 3.33 | $0.6667 |
| r5d.2xlarge | Memory optimized | 4 | 8 | 64 | 0 | 6.67 | $1.3333 |
| r5d.4xlarge | Memory optimized | 8 | 16 | 128 | 0 | 13.33 | $2.6667 |
| r5d.12xlarge | Memory optimized | 24 | 48 | 384 | 0 | 40.00 | $8.0000 |
| r5d.24xlarge | Memory optimized | 48 | 96 | 768 | 0 | 80.00 | $16.0000 |
| r5a.large | Memory optimized | 1 | 2 | 16 | 0 | 1.67 | $0.3333 |
| r5a.xlarge | Memory optimized | 2 | 4 | 32 | 0 | 3.33 | $0.6667 |
| r5a.2xlarge | Memory optimized | 4 | 8 | 64 | 0 | 6.67 | $1.3333 |
| r5a.4xlarge | Memory optimized | 8 | 16 | 128 | 0 | 13.33 | $2.6667 |
| r5a.8xlarge | Memory optimized | 16 | 32 | 256 | 0 | 26.67 | $5.3333 |
| r5a.12xlarge | Memory optimized | 24 | 48 | 384 | 0 | 40.00 | $8.0000 |
| r5a.16xlarge | Memory optimized | 32 | 64 | 512 | 0 | 53.33 | $10.6667 |
| r5a.24xlarge | Memory optimized | 48 | 96 | 768 | 0 | 80.00 | $16.0000 |
| z1d.large | Memory optimized | 1 | 2 | 16 | 0 | 1.67 | $0.3333 |
| z1d.xlarge | Memory optimized | 2 | 4 | 32 | 0 | 3.33 | $0.6667 |
| z1d.2xlarge | Memory optimized | 4 | 8 | 64 | 0 | 6.67 | $1.3333 |
| z1d.3xlarge | Memory optimized | 6 | 12 | 96 | 0 | 10.00 | $2.0000 |
| z1d.6xlarge | Memory optimized | 12 | 24 | 192 | 0 | 20.00 | $4.0000 |
| z1d.12xlarge | Memory optimized | 24 | 48 | 384 | 0 | 40.00 | $8.0000 |
| c4.2xlarge | Compute optimized | 4 | 8 | 15 | 0 | 2.58 | $0.5167 |
| c4.4xlarge | Compute optimized | 8 | 16 | 30 | 0 | 5.17 | $1.0333 |
| c4.8xlarge | Compute optimized | 18 | 36 | 60 | 0 | 11.00 | $2.2000 |
| c5.xlarge | Compute optimized | 2 | 4 | 8 | 0 | 1.33 | $0.2667 |
| c5.2xlarge | Compute optimized | 4 | 8 | 16 | 0 | 2.67 | $0.5333 |
| c5.4xlarge | Compute optimized | 8 | 16 | 32 | 0 | 5.33 | $1.0667 |
| c5.9xlarge | Compute optimized | 18 | 36 | 72 | 0 | 12.00 | $2.4000 |
| c5.18xlarge | Compute optimized | 36 | 72 | 144 | 0 | 24.00 | $4.8000 |
| c5d.xlarge | Compute optimized | 2 | 4 | 8 | 0 | 1.33 | $0.2667 |
| c5d.2xlarge | Compute optimized | 4 | 8 | 16 | 0 | 2.67 | $0.5333 |
| c5d.4xlarge | Compute optimized | 8 | 16 | 32 | 0 | 5.33 | $1.0667 |
| c5d.9xlarge | Compute optimized | 18 | 36 | 72 | 0 | 12.00 | $2.4000 |
| c5d.18xlarge | Compute optimized | 36 | 72 | 144 | 0 | 24.00 | $4.8000 |
| i3.xlarge | Storage optimized | 2 | 4 | 31 | 0 | 3.25 | $0.6500 |
| i3.2xlarge | Storage optimized | 4 | 8 | 61 | 0 | 6.42 | $1.2833 |
| i3.4xlarge | Storage optimized | 8 | 16 | 122 | 0 | 12.83 | $2.5667 |
| i3.8xlarge | Storage optimized | 16 | 32 | 244 | 0 | 25.67 | $5.1333 |
| i3.16xlarge | Storage optimized | 32 | 64 | 488 | 0 | 51.33 | $10.2667 |
| i2.xlarge | Storage optimized | 2 | 4 | 31 | 0 | 3.25 | $0.6500 |
| i2.2xlarge | Storage optimized | 4 | 8 | 61 | 0 | 6.42 | $1.2833 |
| i2.4xlarge | Storage optimized | 8 | 16 | 122 | 0 | 12.83 | $2.5667 |
| i2.8xlarge | Storage optimized | 16 | 32 | 244 | 0 | 25.67 | $5.1333 |
| p2.xlarge | GPU optimized | 2 | 4 | 61 | 1 | 5.75 | $0.9363 |
| p2.8xlarge | GPU optimized | 16 | 32 | 488 | 8 | 46.00 | $7.4900 |
| p2.16xlarge | GPU optimized | 32 | 64 | 732 | 16 | 71.67 | $12.1333 |
| p3.2xlarge | GPU optimized | 4 | 8 | 61 | 1 | 6.42 | $1.8358 |
| p3.8xlarge | GPU optimized | 16 | 32 | 244 | 4 | 25.67 | $7.3433 |
| p3.16xlarge | GPU optimized | 32 | 64 | 488 | 8 | 51.33 | $14.6867 |
| p3dn.24xlarge | GPU Optimized | 48 | 96 | 768 | 8 | 80.00 | $18.7000 |
| p4d.24xlarge | GPU Optimized | 48 | 96 | 1152 | 8 | 112.00 | $25.0400 |
| g4dn.xlarge | GPU Optimized | 2 | 4 | 16 | 1 | 2.00 | $0.5200 |
| g4dn.2xlarge | GPU Optimized | 4 | 8 | 32 | 1 | 4.00 | $0.8000 |
| g4dn.4xlarge | GPU Optimized | 8 | 16 | 64 | 1 | 8.00 | $1.3600 |
| g4dn.8xlarge | GPU Optimized | 16 | 32 | 128 | 1 | 16.00 | $2.4800 |
| g4dn.12xlarge | GPU Optimized | 24 | 48 | 192 | 4 | 24.00 | $4.3200 |
| g4dn.16xlarge | GPU Optimized | 32 | 64 | 256 | 1 | 32.00 | $4.7200 |
| g4dn.metal | GPU Optimized | 48 | 96 | 384 | 8 | 48.00 | $8.6400 |
Data Hub - AWS instances
| Instance | Category | Cores | vCPUs | RAM | GPU | CCUs | Data Hub Rate/hr |
| m5.xlarge | General purpose | 2 | 4 | 16 | 0 | 2.00 | $0.0800 |
| m5.2xlarge | General purpose | 4 | 8 | 32 | 0 | 4.00 | $0.1600 |
| m5.4xlarge | General purpose | 8 | 16 | 64 | 0 | 8.00 | $0.3200 |
| m5.8xlarge | General purpose | 16 | 32 | 128 | 0 | 16.00 | $0.6400 |
| m5.12xlarge | General purpose | 24 | 48 | 192 | 0 | 24.00 | $0.9600 |
| m5.16xlarge | General purpose | 32 | 64 | 256 | 0 | 32.00 | $1.0000 |
| m5.24xlarge | General purpose | 48 | 96 | 384 | 0 | 48.00 | $1.0000 |
| m5d.xlarge | General purpose | 2 | 4 | 16 | 0 | 2.00 | $0.0800 |
| m5d.2xlarge | General purpose | 4 | 8 | 32 | 0 | 4.00 | $0.1600 |
| m5d.4xlarge | General purpose | 8 | 16 | 64 | 0 | 8.00 | $0.3200 |
| m5d.8xlarge | General purpose | 16 | 32 | 128 | 0 | 16.00 | $0.6400 |
| m5d.12xlarge | General purpose | 24 | 48 | 192 | 0 | 24.00 | $0.9600 |
| m5d.16xlarge | General purpose | 32 | 64 | 256 | 0 | 32.00 | $1.0000 |
| m5d.24xlarge | General purpose | 48 | 96 | 384 | 0 | 48.00 | $1.0000 |
| m5n.2xlarge | General purpose | 4 | 8 | 32 | 0 | 4.00 | $0.1600 |
| m5n.4xlarge | General purpose | 8 | 16 | 64 | 0 | 8.00 | $0.3200 |
| m5n.8xlarge | General purpose | 16 | 32 | 128 | 0 | 16.00 | $0.6400 |
| m5dn.xlarge | General purpose | 2 | 4 | 16 | 0 | 2.00 | $0.0800 |
| m5dn.2xlarge | General purpose | 4 | 8 | 32 | 0 | 4.00 | $0.1600 |
| m5dn.4xlarge | General purpose | 8 | 16 | 64 | 0 | 8.00 | $0.3200 |
| m5dn.8xlarge | General purpose | 16 | 32 | 128 | 0 | 16.00 | $0.6400 |
| m5dn.12xlarge | General purpose | 24 | 48 | 192 | 0 | 24.00 | $0.9600 |
| m5dn.16xlarge | General purpose | 32 | 64 | 256 | 0 | 32.00 | $1.0000 |
| m5dn.24xlarge | General purpose | 48 | 96 | 384 | 0 | 48.00 | $1.0000 |
| m5a.xlarge | General purpose | 2 | 4 | 16 | 0 | 2.00 | $0.0800 |
| m5a.2xlarge | General purpose | 4 | 8 | 32 | 0 | 4.00 | $0.1600 |
| m5a.4xlarge | General purpose | 8 | 16 | 64 | 0 | 8.00 | $0.3200 |
| m5a.8xlarge | General purpose | 16 | 32 | 128 | 0 | 16.00 | $0.6400 |
| m5a.12xlarge | General purpose | 24 | 48 | 192 | 0 | 24.00 | $0.9600 |
| m5a.16xlarge | General purpose | 32 | 64 | 256 | 0 | 32.00 | $1.0000 |
| m5a.24xlarge | General purpose | 48 | 96 | 384 | 0 | 48.00 | $1.0000 |
| m5ad.xlarge | General purpose | 2 | 4 | 16 | 0 | 2.00 | $0.0800 |
| m5ad.2xlarge | General purpose | 4 | 8 | 32 | 0 | 4.00 | $0.1600 |
| m5ad.4xlarge | General purpose | 8 | 16 | 64 | 0 | 8.00 | $0.3200 |
| m5ad.8xlarge | General purpose | 16 | 32 | 128 | 0 | 16.00 | $0.6400 |
| m5ad.12xlarge | General purpose | 24 | 48 | 192 | 0 | 24.00 | $0.9600 |
| m5ad.16xlarge | General purpose | 32 | 64 | 256 | 0 | 32.00 | $1.0000 |
| m5ad.24xlarge | General purpose | 48 | 96 | 384 | 0 | 48.00 | $1.0000 |
| r5.2xlarge | Memory optimized | 4 | 8 | 64 | 0 | 6.67 | $0.2667 |
| r5.4xlarge | Memory optimized | 8 | 16 | 128 | 0 | 13.33 | $0.5333 |
| r5.8xlarge | Memory optimized | 16 | 32 | 256 | 0 | 26.67 | $1.0000 |
| r5.16xlarge | Memory optimized | 32 | 64 | 512 | 0 | 53.33 | $1.0000 |
| r5n.2xlarge | Memory optimized | 4 | 8 | 64 | 0 | 6.67 | $0.2667 |
| r5n.4xlarge | Memory optimized | 8 | 16 | 128 | 0 | 13.33 | $0.5333 |
| r5n.8xlarge | Memory optimized | 16 | 32 | 256 | 0 | 26.67 | $1.0000 |
| r5n.16xlarge | Memory optimized | 32 | 64 | 512 | 0 | 53.33 | $1.0000 |
| r5d.xlarge | Memory optimized | 2 | 4 | 32 | 0 | 3.33 | $0.1333 |
| r5d.2xlarge | Memory optimized | 4 | 8 | 64 | 0 | 6.67 | $0.2667 |
| r5d.4xlarge | Memory optimized | 8 | 16 | 128 | 0 | 13.33 | $0.5333 |
| r5d.8xlarge | Memory optimized | 16 | 32 | 256 | 0 | 26.67 | $1.0000 |
| r5d.12xlarge | Memory optimized | 24 | 48 | 384 | 0 | 40.00 | $1.0000 |
| r5d.16xlarge | Memory optimized | 32 | 64 | 512 | 0 | 53.33 | $1.0000 |
| r5d.24xlarge | Memory optimized | 48 | 96 | 768 | 0 | 80.00 | $2.4000 |
| r5dn.xlarge | Memory optimized | 2 | 4 | 32 | 0 | 3.33 | $0.1333 |
| r5dn.2xlarge | Memory optimized | 4 | 8 | 64 | 0 | 6.67 | $0.2667 |
| r5dn.4xlarge | Memory optimized | 8 | 16 | 128 | 0 | 13.33 | $0.5333 |
| r5dn.8xlarge | Memory optimized | 16 | 32 | 256 | 0 | 26.67 | $1.0000 |
| r5dn.16xlarge | Memory optimized | 32 | 64 | 512 | 0 | 53.33 | $1.0000 |
| r5a.4xlarge | Memory optimized | 8 | 16 | 128 | 0 | 13.33 | $0.5333 |
| r5a.8xlarge | Memory optimized | 16 | 32 | 256 | 0 | 26.67 | $1.0000 |
| r5ad.xlarge | Memory optimized | 2 | 4 | 32 | 0 | 3.33 | $0.1333 |
| r5ad.2xlarge | Memory optimized | 4 | 8 | 64 | 0 | 6.67 | $0.2667 |
| r5ad.4xlarge | Memory optimized | 8 | 16 | 128 | 0 | 13.33 | $0.5333 |
| r5ad.8xlarge | Memory optimized | 16 | 32 | 256 | 0 | 26.67 | $1.0000 |
| r5ad.12xlarge | Memory optimized | 24 | 48 | 384 | 0 | 40.00 | $1.0000 |
| r5ad.16xlarge | Memory optimized | 32 | 64 | 512 | 0 | 53.33 | $1.0000 |
| r5ad.24xlarge | Memory optimized | 48 | 96 | 768 | 0 | 80.00 | $2.4000 |
| r4.xlarge | Memory optimized | 2 | 4 | 30.5 | 0 | 3.21 | $0.1283 |
| r4.2xlarge | Memory optimized | 4 | 8 | 61 | 0 | 6.42 | $0.2567 |
| r4.4xlarge | Memory optimized | 8 | 16 | 122 | 0 | 12.83 | $0.5133 |
| r4.8xlarge | Memory optimized | 16 | 32 | 244 | 0 | 25.67 | $1.0000 |
| x1e.2xlarge | Memory optimized | 4 | 8 | 244 | 0 | 21.67 | $0.8667 |
| c5.2xlarge | Compute optimized | 4 | 8 | 16 | 0 | 2.67 | $0.1067 |
| c5.4xlarge | Compute optimized | 8 | 16 | 32 | 0 | 5.33 | $0.2133 |
| c5.9xlarge | Compute optimized | 18 | 36 | 72 | 0 | 12.00 | $0.4800 |
| c5.12xlarge | Compute optimized | 24 | 48 | 96 | 0 | 16.00 | $0.6400 |
| c5a.2xlarge | Compute optimized | 4 | 8 | 16 | 0 | 2.67 | $0.1067 |
| c5a.4xlarge | Compute optimized | 8 | 16 | 32 | 0 | 5.33 | $0.2133 |
| c5a.8xlarge | Compute optimized | 16 | 32 | 64 | 0 | 10.67 | $0.4267 |
| c5a.12xlarge | Compute optimized | 24 | 48 | 96 | 0 | 16.00 | $0.6400 |
| i3.2xlarge | Storage optimized | 4 | 8 | 61 | 0 | 6.42 | $0.2567 |
| i3.4xlarge | Storage optimized | 8 | 16 | 122 | 0 | 12.83 | $0.5133 |
| i3.8xlarge | Storage optimized | 16 | 32 | 244 | 0 | 25.67 | $1.0000 |
| i3en.2xlarge | Storage optimized | 4 | 8 | 64 | 0 | 6.67 | $0.2667 |
| i3en.3xlarge | Storage optimized | 6 | 12 | 96 | 0 | 10.00 | $0.4000 |
| i3en.6xlarge | Storage optimized | 12 | 24 | 192 | 0 | 20.00 | $0.8000 |
| i3en.12xlarge | Storage optimized | 24 | 48 | 384 | 0 | 40.00 | $1.0000 |
| h1.2xlarge | Storage optimized | 4 | 8 | 32 | 0 | 4.00 | $0.1600 |
| h1.4xlarge | Storage optimized | 8 | 16 | 64 | 0 | 8.00 | $0.3200 |
| h1.8xlarge | Storage optimized | 16 | 32 | 128 | 0 | 16.00 | $0.6400 |
| d2.xlarge | Storage optimized | 2 | 4 | 30.5 | 0 | 3.21 | $0.1283 |
| d2.2xlarge | Storage optimized | 4 | 8 | 61 | 0 | 6.42 | $0.2567 |
| d2.4xlarge | Storage optimized | 8 | 16 | 122 | 0 | 12.83 | $0.5133 |
| d2.8xlarge | Storage optimized | 18 | 36 | 244 | 0 | 26.33 | $1.0000 |
| d3.xlarge | Storage optimized | 2 | 4 | 32 | 0 | 3.33 | $0.1333 |
| d3.2xlarge | Storage optimized | 4 | 8 | 64 | 0 | 6.67 | $0.2667 |
| d3.4xlarge | Storage optimized | 8 | 16 | 128 | 0 | 13.33 | $0.5333 |
| d3.8xlarge | Storage optimized | 16 | 32 | 256 | 0 | 26.67 | $1.0000 |
| d3en.2xlarge | Storage optimized | 4 | 8 | 32 | 0 | 4.00 | $0.1600 |
| d3en.4xlarge | Storage optimized | 8 | 16 | 64 | 0 | 8.00 | $0.3200 |
| d3en.6xlarge | Storage optimized | 12 | 24 | 96 | 0 | 12.00 | $0.4800 |
| d3en.8xlarge | Storage optimized | 16 | 32 | 128 | 0 | 16.00 | $0.6400 |
| p3.2xlarge | GPU optimized | 4 | 8 | 61 | 1 | 6.42 | $1.1942 |
| p3.8xlarge | GPU optimized | 16 | 32 | 244 | 4 | 25.67 | $4.7500 |
Data Hub - Flow Management - AWS instances
The Flow Management rate applies to all nodes in a Data Hub Cluster running NiFi regardless of whether the official Flow Management template or a custom template was used to create the cluster.
| Instance | Category | Cores | vCPUs | RAM | GPU | CCUs | Rate/hr* |
| m5.xlarge | General purpose | 2 | 4 | 16 | 0 | 2.00 | $0.3000 |
| m5.2xlarge | General purpose | 4 | 8 | 32 | 0 | 4.00 | $0.6000 |
| m5.4xlarge | General purpose | 8 | 16 | 64 | 0 | 8.00 | $1.2000 |
| m5.8xlarge | General purpose | 16 | 32 | 128 | 0 | 16.00 | $2.4000 |
| m5.12xlarge | General purpose | 24 | 48 | 192 | 0 | 24.00 | $3.6000 |
| m5.16xlarge | General purpose | 32 | 64 | 256 | 0 | 32.00 | $4.8000 |
| m5.24xlarge | General purpose | 48 | 96 | 384 | 0 | 48.00 | $7.2000 |
| m5d.xlarge | General purpose | 2 | 4 | 16 | 0 | 2.00 | $0.3000 |
| m5d.2xlarge | General purpose | 4 | 8 | 32 | 0 | 4.00 | $0.6000 |
| m5d.4xlarge | General purpose | 8 | 16 | 64 | 0 | 8.00 | $1.2000 |
| m5d.8xlarge | General purpose | 16 | 32 | 128 | 0 | 16.00 | $2.4000 |
| m5d.12xlarge | General purpose | 24 | 48 | 192 | 0 | 24.00 | $3.6000 |
| m5d.16xlarge | General purpose | 32 | 64 | 256 | 0 | 32.00 | $4.8000 |
| m5d.24xlarge | General purpose | 48 | 96 | 384 | 0 | 48.00 | $7.2000 |
| m5n.2xlarge | General purpose | 4 | 8 | 32 | 0 | 4.00 | $0.6000 |
| m5n.4xlarge | General purpose | 8 | 16 | 64 | 0 | 8.00 | $1.2000 |
| m5n.8xlarge | General purpose | 16 | 32 | 128 | 0 | 16.00 | $2.4000 |
| m5dn.xlarge | General purpose | 2 | 4 | 16 | 0 | 2.00 | $0.3000 |
| m5dn.2xlarge | General purpose | 4 | 8 | 32 | 0 | 4.00 | $0.6000 |
| m5dn.4xlarge | General purpose | 8 | 16 | 64 | 0 | 8.00 | $1.2000 |
| m5dn.8xlarge | General purpose | 16 | 32 | 128 | 0 | 16.00 | $2.4000 |
| m5dn.12xlarge | General purpose | 24 | 48 | 192 | 0 | 24.00 | $3.6000 |
| m5dn.16xlarge | General purpose | 32 | 64 | 256 | 0 | 32.00 | $4.8000 |
| m5dn.24xlarge | General purpose | 48 | 96 | 384 | 0 | 48.00 | $7.2000 |
| m5a.xlarge | General purpose | 2 | 4 | 16 | 0 | 2.00 | $0.3000 |
| m5a.2xlarge | General purpose | 4 | 8 | 32 | 0 | 4.00 | $0.6000 |
| m5a.4xlarge | General purpose | 8 | 16 | 64 | 0 | 8.00 | $1.2000 |
| m5a.8xlarge | General purpose | 16 | 32 | 128 | 0 | 16.00 | $2.4000 |
| m5a.12xlarge | General purpose | 24 | 48 | 192 | 0 | 24.00 | $3.6000 |
| m5a.16xlarge | General purpose | 32 | 64 | 256 | 0 | 32.00 | $4.8000 |
| m5a.24xlarge | General purpose | 48 | 96 | 384 | 0 | 48.00 | $7.2000 |
| m5ad.xlarge | General purpose | 2 | 4 | 16 | 0 | 2.00 | $0.3000 |
| m5ad.2xlarge | General purpose | 4 | 8 | 32 | 0 | 4.00 | $0.6000 |
| m5ad.4xlarge | General purpose | 8 | 16 | 64 | 0 | 8.00 | $1.2000 |
| m5ad.8xlarge | General purpose | 16 | 32 | 128 | 0 | 16.00 | $2.4000 |
| m5ad.12xlarge | General purpose | 24 | 48 | 192 | 0 | 24.00 | $3.6000 |
| m5ad.16xlarge | General purpose | 32 | 64 | 256 | 0 | 32.00 | $4.8000 |
| m5ad.24xlarge | General purpose | 48 | 96 | 384 | 0 | 48.00 | $7.2000 |
| r5.2xlarge | Memory optimized | 4 | 8 | 64 | 0 | 6.67 | $1.0000 |
| r5.4xlarge | Memory optimized | 8 | 16 | 128 | 0 | 13.33 | $2.0000 |
| r5.8xlarge | Memory optimized | 16 | 32 | 256 | 0 | 26.67 | $4.0000 |
| r5.16xlarge | Memory optimized | 32 | 64 | 512 | 0 | 53.33 | $8.0000 |
| r5n.2xlarge | Memory optimized | 4 | 8 | 64 | 0 | 6.67 | $1.0000 |
| r5n.4xlarge | Memory optimized | 8 | 16 | 128 | 0 | 13.33 | $2.0000 |
| r5n.8xlarge | Memory optimized | 16 | 32 | 256 | 0 | 26.67 | $4.0000 |
| r5n.16xlarge | Memory optimized | 32 | 64 | 512 | 0 | 53.33 | $8.0000 |
| r5d.xlarge | Memory optimized | 2 | 4 | 32 | 0 | 3.33 | $0.5000 |
| r5d.2xlarge | Memory optimized | 4 | 8 | 64 | 0 | 6.67 | $1.0000 |
| r5d.4xlarge | Memory optimized | 8 | 16 | 128 | 0 | 13.33 | $2.0000 |
| r5d.8xlarge | Memory optimized | 16 | 32 | 256 | 0 | 26.67 | $4.0000 |
| r5d.12xlarge | Memory optimized | 24 | 48 | 384 | 0 | 40.00 | $6.0000 |
| r5d.16xlarge | Memory optimized | 32 | 64 | 512 | 0 | 53.33 | $8.0000 |
| r5d.24xlarge | Memory optimized | 48 | 96 | 768 | 0 | 80.00 | $12.0000 |
| r5dn.xlarge | Memory optimized | 2 | 4 | 32 | 0 | 3.33 | $0.5000 |
| r5dn.2xlarge | Memory optimized | 4 | 8 | 64 | 0 | 6.67 | $1.0000 |
| r5dn.4xlarge | Memory optimized | 8 | 16 | 128 | 0 | 13.33 | $2.0000 |
| r5dn.8xlarge | Memory optimized | 16 | 32 | 256 | 0 | 26.67 | $4.0000 |
| r5dn.16xlarge | Memory optimized | 32 | 64 | 512 | 0 | 53.33 | $8.0000 |
| r5a.4xlarge | Memory optimized | 8 | 16 | 128 | 0 | 13.33 | $2.0000 |
| r5a.8xlarge | Memory optimized | 16 | 32 | 256 | 0 | 26.67 | $4.0000 |
| r5ad.xlarge | Memory optimized | 2 | 4 | 32 | 0 | 3.33 | $0.5000 |
| r5ad.2xlarge | Memory optimized | 4 | 8 | 64 | 0 | 6.67 | $1.0000 |
| r5ad.4xlarge | Memory optimized | 8 | 16 | 128 | 0 | 13.33 | $2.0000 |
| r5ad.8xlarge | Memory optimized | 16 | 32 | 256 | 0 | 26.67 | $4.0000 |
| r5ad.12xlarge | Memory optimized | 24 | 48 | 384 | 0 | 40.00 | $6.0000 |
| r5ad.16xlarge | Memory optimized | 32 | 64 | 512 | 0 | 53.33 | $8.0000 |
| r5ad.24xlarge | Memory optimized | 48 | 96 | 768 | 0 | 80.00 | $12.0000 |
| r4.xlarge | Memory optimized | 2 | 4 | 30.5 | 0 | 3.21 | $0.4813 |
| r4.2xlarge | Memory optimized | 4 | 8 | 61 | 0 | 6.42 | $0.9625 |
| r4.4xlarge | Memory optimized | 8 | 16 | 122 | 0 | 12.83 | $1.9250 |
| r4.8xlarge | Memory optimized | 16 | 32 | 244 | 0 | 25.67 | $3.8500 |
| x1e.2xlarge | Memory optimized | 4 | 8 | 244 | 0 | 21.67 | $3.2500 |
| c5.2xlarge | Compute optimized | 4 | 8 | 16 | 0 | 2.67 | $0.4000 |
| c5.4xlarge | Compute optimized | 8 | 16 | 32 | 0 | 5.33 | $0.8000 |
| c5.9xlarge | Compute optimized | 18 | 36 | 72 | 0 | 12.00 | $1.8000 |
| c5.12xlarge | Compute optimized | 24 | 48 | 96 | 0 | 16.00 | $2.4000 |
| c5a.2xlarge | Compute optimized | 4 | 8 | 16 | 0 | 2.67 | $0.4000 |
| c5a.4xlarge | Compute optimized | 8 | 16 | 32 | 0 | 5.33 | $0.8000 |
| c5a.8xlarge | Compute optimized | 16 | 32 | 64 | 0 | 10.67 | $1.6000 |
| c5a.12xlarge | Compute optimized | 24 | 48 | 96 | 0 | 16.00 | $2.4000 |
| i3.2xlarge | Storage optimized | 4 | 8 | 61 | 0 | 6.42 | $0.9625 |
| i3.4xlarge | Storage optimized | 8 | 16 | 122 | 0 | 12.83 | $1.9250 |
| i3.8xlarge | Storage optimized | 16 | 32 | 244 | 0 | 25.67 | $3.8500 |
| i3en.2xlarge | Storage optimized | 4 | 8 | 64 | 0 | 6.67 | $1.0000 |
| i3en.3xlarge | Storage optimized | 6 | 12 | 96 | 0 | 10.00 | $1.5000 |
| i3en.6xlarge | Storage optimized | 12 | 24 | 192 | 0 | 20.00 | $3.0000 |
| i3en.12xlarge | Storage optimized | 24 | 48 | 384 | 0 | 40.00 | $6.0000 |
| h1.2xlarge | Storage optimized | 4 | 8 | 32 | 0 | 4.00 | $0.6000 |
| h1.4xlarge | Storage optimized | 8 | 16 | 64 | 0 | 8.00 | $1.2000 |
| h1.8xlarge | Storage optimized | 16 | 32 | 128 | 0 | 16.00 | $2.4000 |
| d2.xlarge | Storage optimized | 2 | 4 | 30.5 | 0 | 3.21 | $0.4813 |
| d2.2xlarge | Storage optimized | 4 | 8 | 61 | 0 | 6.42 | $0.9625 |
| d2.4xlarge | Storage optimized | 8 | 16 | 122 | 0 | 12.83 | $1.9250 |
| d2.8xlarge | Storage optimized | 18 | 36 | 244 | 0 | 26.33 | $3.9500 |
| d3.xlarge | Storage optimized | 2 | 4 | 32 | 0 | 3.33 | $0.5000 |
| d3.2xlarge | Storage optimized | 4 | 8 | 64 | 0 | 6.67 | $1.0000 |
| d3.4xlarge | Storage optimized | 8 | 16 | 128 | 0 | 13.33 | $2.0000 |
| d3.8xlarge | Storage optimized | 16 | 32 | 256 | 0 | 26.67 | $4.0000 |
| d3en.2xlarge | Storage optimized | 4 | 8 | 32 | 0 | 4.00 | $0.6000 |
| d3en.4xlarge | Storage optimized | 8 | 16 | 64 | 0 | 8.00 | $1.2000 |
| d3en.6xlarge | Storage optimized | 12 | 24 | 96 | 0 | 12.00 | $1.8000 |
| d3en.8xlarge | Storage optimized | 16 | 32 | 128 | 0 | 16.00 | $2.4000 |
| p3.2xlarge | GPU optimized | 4 | 8 | 61 | 1 | 6.42 | $0.9625 |
| p3.8xlarge | GPU optimized | 16 | 32 | 244 | 4 | 25.67 | $3.8500 |
Operational Database - AWS instances
| Promotional Pricing | |||||||
| Instance* | Category | Cores | vCPUs | RAM | GPU | CCUs | Rate/hr |
| m5.2xlarge | General purpose | 4 | 8 | 32 | 0 | 4.00 | $0.3200 |
| m5.8xlarge | General purpose | 16 | 32 | 128 | 0 | 16.00 | $1.2800 |
| r5.8xlarge | Memory optimized | 16 | 32 | 256 | 0 | 26.67 | $2.1333 |
| h1.2xlarge | Storage optimized | 4 | 8 | 32 | 0 | 4.00 | $0.3200 |
| i3.2xlarge | Storage Optimized | 4 | 8 | 61 | 0 | 6.42 | $0.5133 |
DataFlow Functions - AWS
| Total Billable Invocations per Flow/Month | Price per Billable Invocation |
First 1,000 Billable Invocations |
$0.1000 |
Next 9,000 Billable Invocations |
$0.0200 |
Next 90,000 Billable Invocations |
$0.0020 |
Next 900,000 Billable Invocations |
$0.0003 |
Over 1,000,001 Billable Invocations |
$0.0001 |
*Notes:
- Pricing Volume Tiers are per individual DataFlow Function
- The volume table above is a tiered volume table, where the first 1,000 invocations are always charged at $0.10 per month, the next 9,000 invocations per month are charged at $0.02 per month and so on
- “Billable invocation” is a combination of function invocations and function duration. Every time an instance of DataFlow Functions is invoked that counts as one Billable Invocation. If a DFF runs for more than one second, each subsequent second (after the initial second) counts as another Billable Invocation. In addition, if a DFF invocation runs for less than one second, each fractional second will count as one Billable Invocation.
Data Engineering - Azure instances
| Instance* | Category | Cores | vCPUs | RAM | GPU | CCUs | Rate/hr |
| D8s v4 | General Purpose | 4 | 8 | 32 | 0 | 4.00 | $0.2800 |
| D16s v4 | General Purpose | 8 | 16 | 64 | 0 | 8.00 | $0.5600 |
| D32s v4 | General Purpose | 16 | 32 | 128 | 0 | 16.00 | $1.1200 |
| D64s v4 | General Purpose | 32 | 64 | 256 | 0 | 32.00 | $2.2400 |
| E8s v3 | Memory Optimized | 4 | 8 | 64 | 0 | 6.67 | $0.4667 |
| E16s v3 | Memory Optimized | 8 | 16 | 128 | 0 | 13.33 | $0.9333 |
| E32s v3 | Memory Optimized | 16 | 32 | 256 | 0 | 26.67 | $1.8667 |
| E64s v3 | Memory optimized | 32 | 64 | 432 | 0 | 46.67 | $3.2667 |
| E8s v4 | Memory Optimized | 4 | 8 | 64 | 0 | 6.67 | $0.4667 |
| E16s v4 | Memory Optimized | 8 | 16 | 128 | 0 | 13.33 | $0.9333 |
| E32s v4 | Memory Optimized | 16 | 32 | 256 | 0 | 26.67 | $1.8667 |
| E64s v4 | Memory Optimized | 32 | 64 | 504 | 0 | 52.67 | $3.6867 |
| F8s v2 | Compute Optimized | 4 | 8 | 16 | 0 | 2.67 | $0.1867 |
| F16s v2 | Compute Optimized | 8 | 16 | 32 | 0 | 5.33 | $0.3733 |
| F32s v2 | Compute Optimized | 16 | 32 | 64 | 0 | 10.67 | $0.7467 |
| F48s v2 | Compute Optimized | 24 | 48 | 96 | 0 | 16.00 | $1.1200 |
| F72s v2 | Compute Optimized | 36 | 72 | 144 | 0 | 24.00 | $1.6800 |
Machine Learning - Azure instances
| Instance | Category | Cores | vCPUs | RAM | GPU | CCUs | Rate/hr |
| DS3 v2 | General purpose | 2 | 4 | 14 | 0 | 1.83 | $0.3667 |
| DS4 v2 | General purpose | 4 | 8 | 28 | 0 | 3.67 | $0.7333 |
| DS5 v2 | General purpose | 8 | 16 | 56 | 0 | 7.33 | $1.4667 |
| D3 v2 | General purpose | 2 | 4 | 14 | 0 | 1.83 | $0.3667 |
| D8s v3 | General purpose | 4 | 8 | 32 | 0 | 4 | $0.8000 |
| D16s v3 | General purpose | 8 | 16 | 64 | 0 | 8 | $1.6000 |
| D32s v3 | General purpose | 16 | 32 | 128 | 0 | 16 | $3.2000 |
| D64s v3 | General purpose | 32 | 64 | 256 | 0 | 32 | $6.4000 |
| D8 v3 | General purpose | 4 | 8 | 32 | 0 | 4 | $0.8000 |
| D16 v3 | General purpose | 8 | 16 | 64 | 0 | 8 | $1.6000 |
| D32 v3 | General purpose | 16 | 32 | 128 | 0 | 16 | $3.2000 |
| D64 v3 | General purpose | 32 | 64 | 256 | 0 | 32 | $6.4000 |
| DS12 v2 | General purpose | 2 | 4 | 28 | 0 | 3 | $0.6000 |
| DS13 v2 | General purpose | 4 | 8 | 56 | 0 | 6 | $1.2000 |
| DS14 v2 | General purpose | 8 | 16 | 112 | 0 | 12 | $2.4000 |
| DS15 v2 | General purpose | 10 | 20 | 140 | 0 | 15 | $3.0000 |
| D12 v2 | General purpose | 2 | 4 | 28 | 0 | 3 | $0.6000 |
| D13 v2 | General purpose | 4 | 8 | 56 | 0 | 6 | $1.2000 |
| D14 v2 | General purpose | 8 | 16 | 112 | 0 | 12 | $2.4000 |
| D15 v2 | General purpose | 10 | 20 | 140 | 0 | 15 | $3.0000 |
| E8s v3 | Memory optimized | 4 | 8 | 64 | 0 | 6.67 | $1.3333 |
| E16s v3 | Memory optimized | 8 | 16 | 128 | 0 | 13.33 | $2.6667 |
| E32s v3 | Memory optimized | 16 | 32 | 256 | 0 | 26.67 | $5.3333 |
| E64s v3 | Memory optimized | 32 | 64 | 432 | 0 | 46.67 | $9.3333 |
| L4s | Storage optimized | 2 | 4 | 32 | 0 | 3.33 | $0.6667 |
| L8s | Storage optimized | 4 | 8 | 64 | 0 | 6.67 | $1.3333 |
| L16s | Storage optimized | 8 | 16 | 128 | 0 | 13.33 | $2.6667 |
| L32s | Storage optimized | 16 | 32 | 256 | 0 | 26.67 | $5.3333 |
| L8s v2 | Storage optimized | 4 | 8 | 64 | 0 | 6.67 | $1.3333 |
| L16s v2 | Storage optimized | 8 | 16 | 128 | 0 | 13.33 | $2.6667 |
| L32s v2 | Storage optimized | 16 | 32 | 256 | 0 | 26.67 | $5.3333 |
| L64s v2 | Storage optimized | 32 | 64 | 512 | 0 | 53.33 | $10.6667 |
| L80s v2 | Storage optimized | 40 | 80 | 640 | 0 | 66.67 | $13.3333 |
| F4s v2 | Storage optimized | 2 | 4 | 8 | 0 | 1.33 | $0.2667 |
| F8s v2 | Storage optimized | 4 | 8 | 16 | 0 | 2.67 | $0.5333 |
| F16s v2 | Storage optimized | 8 | 16 | 32 | 0 | 5.33 | $1.0667 |
| F32s v2 | Storage optimized | 16 | 32 | 64 | 0 | 10.67 | $2.1333 |
| F64s v2 | Storage optimized | 32 | 64 | 128 | 0 | 21.33 | $4.2667 |
| F72s v2 | Storage optimized | 36 | 72 | 144 | 0 | 24 | $4.8000 |
| F4 | Storage optimized | 2 | 4 | 8 | 0 | 1.33 | $0.2667 |
| F8 | Storage optimized | 4 | 8 | 16 | 0 | 2.67 | $0.5333 |
| F16 | Storage optimized | 8 | 16 | 32 | 0 | 5.33 | $1.0667 |
| H16 | High performance | 16 | 32 | 112 | 0 | 14.67 | $2.9333 |
| NC6 | GPU optimized | 3 | 6 | 56 | 1 | 5.67 | $0.9246 |
| NC12 | GPU optimized | 6 | 12 | 112 | 2 | 11.33 | $1.8492 |
| NC24 | GPU optimized | 12 | 24 | 224 | 4 | 22.67 | $3.6983 |
| NC6s v2 | GPU optimized | 3 | 6 | 112 | 1 | 10.33 | $1.7467 |
| NC12s v2 | GPU optimized | 6 | 12 | 224 | 2 | 20.67 | $3.4933 |
| NC24s v2 | GPU optimized | 12 | 24 | 448 | 4 | 41.33 | $6.9867 |
| NC6s v3 | GPU optimized | 3 | 6 | 112 | 1 | 10.33 | $2.3842 |
| NC12s v3 | GPU optimized | 6 | 12 | 224 | 2 | 20.67 | $4.7683 |
| NC24s v3 | GPU optimized | 12 | 24 | 448 | 4 | 41.33 | $9.5367 |
| NC4as T4 v3 | GPU optimized | 2 | 4 | 28 | 1 | 3 | $0.6600 |
| NC8as T4 v3 | GPU optimized | 4 | 8 | 56 | 1 | 6 | $1.0800 |
| NC16as T4 v3 | GPU optimized | 8 | 16 | 110 | 1 | 11.83 | $1.8967 |
| NC64as T4 v3 | GPU optimized | 32 | 64 | 440 | 4 | 47.33 | $7.5867 |
| ND6s | GPU optimized | 6 | 12 | 112 | 1 | 11.33 | $1.9467 |
| ND12s | GPU optimized | 12 | 24 | 224 | 2 | 22.67 | $3.8933 |
| ND24s | GPU optimized | 24 | 48 | 448 | 4 | 45.33 | $7.7867 |
| ND96asr A100 v4 | GPU optimized | 96 | 192 | 900 | 8 | 107 | $24.3400 |
Data Hub - Azure instances
| Instance | Category | Cores | vCPUs | RAM | GPU | CCUs | Rate/hr |
| D8 v3 | General purpose | 4 | 8 | 32 | 0 | 4.00 | $0.1600 |
| D16 v3 | General purpose | 8 | 16 | 64 | 0 | 8.00 | $0.3200 |
| D32 v3 | General purpose | 16 | 32 | 128 | 0 | 16.00 | $0.6400 |
| D64 v3 | General purpose | 32 | 64 | 256 | 0 | 32.00 | $1.0000 |
| D8s v3 | General purpose | 4 | 8 | 32 | 0 | 4.00 | $0.1600 |
| D16s v3 | General purpose | 8 | 16 | 64 | 0 | 8.00 | $0.3200 |
| D32s v3 | General purpose | 16 | 32 | 128 | 0 | 16.00 | $0.6400 |
| D64s v3 | General purpose | 32 | 64 | 256 | 0 | 32.00 | $1.0000 |
| D8a v4 | General purpose | 4 | 8 | 32 | 0 | 4.00 | $0.1600 |
| D16a v4 | General purpose | 8 | 16 | 64 | 0 | 8.00 | $0.3200 |
| D32a v4 | General purpose | 16 | 32 | 128 | 0 | 16.00 | $0.6400 |
| D64a v4 | General purpose | 32 | 64 | 256 | 0 | 32.00 | $1.0000 |
| D8as v4 | General purpose | 4 | 8 | 32 | 0 | 4.00 | $0.1600 |
| D16as v4 | General purpose | 8 | 16 | 64 | 0 | 8.00 | $0.3200 |
| D32as v4 | General purpose | 16 | 32 | 128 | 0 | 16.00 | $0.6400 |
| D64as v4 | General purpose | 32 | 64 | 256 | 0 | 32.00 | $1.0000 |
| D13 v2 | Memory optimized | 4 | 8 | 56 | 0 | 6.00 | $0.2400 |
| D14 v2 | Memory optimized | 8 | 16 | 112 | 0 | 12.00 | $0.4800 |
| E8 v3 | Memory optimized | 4 | 8 | 64 | 0 | 6.67 | $0.2667 |
| E16 v3 | Memory optimized | 8 | 16 | 128 | 0 | 13.33 | $0.5333 |
| E32 v3 | Memory optimized | 16 | 32 | 256 | 0 | 26.67 | $1.0000 |
| E64 v3 | Memory optimized | 32 | 64 | 432 | 0 | 46.67 | $1.0000 |
| E8s v3 | Memory optimized | 4 | 8 | 64 | 0 | 6.67 | $0.2667 |
| E16s v3 | Memory optimized | 8 | 16 | 128 | 0 | 13.33 | $0.5333 |
| E20s v3 | Memory optimized | 10 | 20 | 160 | 0 | 16.67 | $0.6667 |
| E32s v3 | Memory optimized | 16 | 32 | 256 | 0 | 26.67 | $1.0000 |
| E48s v3 | Memory optimized | 24 | 48 | 384 | 0 | 40.00 | $1.0000 |
| E64s v3 | Memory optimized | 32 | 64 | 432 | 0 | 46.67 | $1.0000 |
| E8a v4 | Memory optimized | 4 | 8 | 64 | 0 | 6.67 | $0.2667 |
| E16a v4 | Memory optimized | 8 | 16 | 128 | 0 | 13.33 | $0.5333 |
| E32a v4 | Memory optimized | 16 | 32 | 256 | 0 | 26.67 | $1.0000 |
| E64a v4 | Memory optimized | 32 | 64 | 512 | 0 | 53.33 | $1.0000 |
| E64s v4 | Memory optimized | 32 | 64 | 504 | 0 | 52.67 | $1.0000 |
| E64ds v4 | Memory optimized | 32 | 64 | 504 | 0 | 52.67 | $1.0000 |
| E4a v4 | Memory optimized | 2 | 4 | 32 | 0 | 3.33 | $0.1333 |
| F8s v2 | Compute optimized | 4 | 8 | 16 | 0 | 2.67 | $0.1067 |
| F16s v2 | Compute optimized | 8 | 16 | 32 | 0 | 5.33 | $0.2133 |
| F32s v2 | Compute optimized | 16 | 32 | 64 | 0 | 10.67 | $0.4267 |
| L8s v2 | Storage optimized | 4 | 8 | 64 | 0 | 6.67 | $0.2667 |
| L16s v2 | Storage optimized | 8 | 16 | 128 | 0 | 13.33 | $0.5333 |
| L32s v2 | Storage optimized | 16 | 32 | 256 | 0 | 26.67 | $1.0000 |
| L48s v2 | Storage optimized | 24 | 48 | 384 | 0 | 40.00 | $1.0000 |
| NC6 | GPU optimized | 3 | 6 | 56 | 1 | 5.67 | $0.3579 |
| NC24r | GPU optimized | 12 | 24 | 224 | 4 | 22.67 | $1.4317 |
| NC6s v3 | GPU optimized | 3 | 6 | 112 | 1 | 10.33 | $1.3508 |
| NC24s v3 | GPU optimized | 12 | 24 | 448 | 4 | 41.33 | $4.7500 |
Data Hub - Flow Management - Azure instances
The Flow Management rate applies to all nodes in a Data Hub cluster running NiFi regardless of whether the official Flow Management template or a custom template was used to create the cluster.
| Instance | Category | Cores | vCPUs | RAM | GPU | CCUs | Rate/hr* |
| D8 v3 | General purpose | 4 | 8 | 32 | 0 | 4.00 | $0.6000 |
| D16 v3 | General purpose | 8 | 16 | 64 | 0 | 8.00 | $1.2000 |
| D32 v3 | General purpose | 16 | 32 | 128 | 0 | 16.00 | $2.4000 |
| D64 v3 | General purpose | 32 | 64 | 256 | 0 | 32.00 | $4.8000 |
| D8s v3 | General purpose | 4 | 8 | 32 | 0 | 4.00 | $0.6000 |
| D16s v3 | General purpose | 8 | 16 | 64 | 0 | 8.00 | $1.2000 |
| D32s v3 | General purpose | 16 | 32 | 128 | 0 | 16.00 | $2.4000 |
| D64s v3 | General purpose | 32 | 64 | 256 | 0 | 32.00 | $4.8000 |
| D8a v4 | General purpose | 4 | 8 | 32 | 0 | 4.00 | $0.6000 |
| D16a v4 | General purpose | 8 | 16 | 64 | 0 | 8.00 | $1.2000 |
| D32a v4 | General purpose | 16 | 32 | 128 | 0 | 16.00 | $2.4000 |
| D64a v4 | General purpose | 32 | 64 | 256 | 0 | 32.00 | $4.8000 |
| D8as v4 | General purpose | 4 | 8 | 32 | 0 | 4.00 | $0.6000 |
| D16as v4 | General purpose | 8 | 16 | 64 | 0 | 8.00 | $1.2000 |
| D32as v4 | General purpose | 16 | 32 | 128 | 0 | 16.00 | $2.4000 |
| D64as v4 | General purpose | 32 | 64 | 256 | 0 | 32.00 | $4.8000 |
| D13 v2 | Memory optimized | 4 | 8 | 56 | 0 | 6.00 | $0.9000 |
| D14 v2 | Memory optimized | 8 | 16 | 112 | 0 | 12.00 | $1.8000 |
| E8 v3 | Memory optimized | 4 | 8 | 64 | 0 | 6.67 | $1.0000 |
| E16 v3 | Memory optimized | 8 | 16 | 128 | 0 | 13.33 | $2.0000 |
| E32 v3 | Memory optimized | 16 | 32 | 256 | 0 | 26.67 | $4.0000 |
| E64 v3 | Memory optimized | 32 | 64 | 432 | 0 | 46.67 | $7.0000 |
| E8s v3 | Memory optimized | 4 | 8 | 64 | 0 | 6.67 | $1.0000 |
| E16s v3 | Memory optimized | 8 | 16 | 128 | 0 | 13.33 | $2.0000 |
| E20s v3 | Memory optimized | 10 | 20 | 160 | 0 | 16.67 | $2.5000 |
| E32s v3 | Memory optimized | 16 | 32 | 256 | 0 | 26.67 | $4.0000 |
| E48s v3 | Memory optimized | 24 | 48 | 384 | 0 | 40.00 | $6.0000 |
| E64s v3 | Memory optimized | 32 | 64 | 432 | 0 | 46.67 | $7.0000 |
| E8a v4 | Memory optimized | 4 | 8 | 64 | 0 | 6.67 | $1.0000 |
| E16a v4 | Memory optimized | 8 | 16 | 128 | 0 | 13.33 | $2.0000 |
| E32a v4 | Memory optimized | 16 | 32 | 256 | 0 | 26.67 | $4.0000 |
| E64a v4 | Memory optimized | 32 | 64 | 512 | 0 | 53.33 | $8.0000 |
| E64s v4 | Memory optimized | 32 | 64 | 504 | 0 | 52.67 | $7.9000 |
| E64ds v4 | Memory optimized | 32 | 64 | 504 | 0 | 52.67 | $7.9000 |
| E4a v4 | Memory optimized | 2 | 4 | 32 | 0 | 3.33 | $0.5000 |
| F8s v2 | Compute optimized | 4 | 8 | 16 | 0 | 2.67 | $0.4000 |
| F16s v2 | Compute optimized | 8 | 16 | 32 | 0 | 5.33 | $0.8000 |
| F32s v2 | Compute optimized | 16 | 32 | 64 | 0 | 10.67 | $1.6000 |
| L8s v2 | Storage optimized | 4 | 8 | 64 | 0 | 6.67 | $1.0000 |
| L16s v2 | Storage optimized | 8 | 16 | 128 | 0 | 13.33 | $2.0000 |
| L32s v2 | Storage optimized | 16 | 32 | 256 | 0 | 26.67 | $4.0000 |
| L48s v2 | Storage optimized | 24 | 48 | 384 | 0 | 40.00 | $6.0000 |
| NC6 | GPU optimized | 3 | 6 | 56 | 1 | 5.67 | $0.8500 |
| NC24r | GPU optimized | 12 | 24 | 224 | 4 | 22.67 | $3.4000 |
| NC6s v3 | GPU optimized | 3 | 6 | 112 | 1 | 10.33 | $1.5500 |
| NC24s v3 | GPU optimized | 12 | 24 | 448 | 4 | 41.33 | $6.2000 |
Operational Database - Azure instances
| Promotional Pricing | |||||||
| Instance* | Category | Cores | vCPUs | RAM | GPU | CCUs | Rate/hr |
| D8 v3 | General Purpose | 4 | 8 | 32 | 0 | 4.00 | $0.3200 |
| D32 v3 | General Purpose | 16 | 32 | 128 | 0 | 16.00 | $1.2800 |
| D8s v3 | General Purpose | 4 | 8 | 32 | 0 | 4.00 | $0.3200 |
| D32s v3 | General Purpose | 16 | 32 | 128 | 0 | 16.00 | $1.2800 |
| D8a v4 | General Purpose | 4 | 8 | 32 | 0 | 4.00 | $0.3200 |
| D64a v4 | General purpose | 32 | 64 | 256 | 0 | 32.00 | $2.5600 |
| E32s v3 | Memory optimized | 16 | 32 | 256 | 0 | 26.67 | $2.1333 |
| L8s v2 | Storage Optimized | 4 | 8 | 64 | 0 | 6.67 | $0.5333 |
DataFlow Functions - Azure
| Total Billable Invocations per Flow/Month | Price per Billable Invocation |
First 1,000 Billable Invocations |
$0.1000 |
Next 9,000 Billable Invocations |
$0.0200 |
Next 90,000 Billable Invocations |
$0.0020 |
Next 900,000 Billable Invocations |
$0.0003 |
Over 1,000,001 Billable Invocations |
$0.0001 |
*Notes:
- Pricing Volume Tiers are per individual DataFlow Function
- The volume table above is a tiered volume table, where the first 1,000 invocations are always charged at $0.10 per month, the next 9,000 invocations per month are charged at $0.02 per month and so on
- “Billable invocation” is a combination of function invocations and function duration. Every time an instance of DataFlow Functions is invoked that counts as one Billable Invocation. If a DFF runs for more than one second, each subsequent second (after the initial second) counts as another Billable Invocation. In addition, if a DFF invocation runs for less than one second, each fractional second will count as one Billable Invocation.
Data Hub - GCP Instances
| Instance | Category | Cores | vCPUs | RAM | GPU | CCUs | Rate/hr |
| e2-standard-2 | General Purpose | 1 | 2 | 8 | 0 | 1.00 | $0.0400 |
| e2-standard-4 | General Purpose | 2 | 4 | 16 | 0 | 2.00 | $0.0800 |
| e2-standard-8 | General Purpose | 4 | 8 | 32 | 0 | 4.00 | $0.1600 |
| e2-standard-16 | General Purpose | 8 | 16 | 64 | 0 | 8.00 | $0.3200 |
| e2-standard-32 | General Purpose | 16 | 32 | 128 | 0 | 16.00 | $0.6400 |
| n1-standard-2 | General Purpose | 1 | 2 | 7.5 | 0 | 0.96 | $0.0383 |
| n1-standard-4 | General Purpose | 2 | 4 | 15 | 0 | 1.92 | $0.0767 |
| n1-standard-8 | General Purpose | 4 | 8 | 30 | 0 | 3.83 | $0.1533 |
| n1-standard-16 | General Purpose | 8 | 16 | 60 | 0 | 7.67 | $0.3067 |
| n1-standard-32 | General Purpose | 16 | 32 | 120 | 0 | 15.33 | $0.6133 |
| n1-standard-64 | General Purpose | 32 | 64 | 240 | 0 | 30.67 | $1.0000 |
| n2-standard-2 | General Purpose | 1 | 2 | 8 | 0 | 1.00 | $0.0400 |
| n2-standard-4 | General Purpose | 2 | 4 | 16 | 0 | 2.00 | $0.0800 |
| n2-standard-8 | General Purpose | 4 | 8 | 32 | 0 | 4.00 | $0.1600 |
| n2-standard-16 | General Purpose | 8 | 16 | 64 | 0 | 8.00 | $0.3200 |
| n2-standard-32 | General Purpose | 16 | 32 | 128 | 0 | 16.00 | $0.6400 |
| n2-standard-48 | General Purpose | 24 | 48 | 192 | 0 | 24.00 | $0.9600 |
| n2-standard-64 | General Purpose | 32 | 64 | 256 | 0 | 32.00 | $1.0000 |
| n2-standard-80 | General Purpose | 40 | 80 | 320 | 0 | 40.00 | $1.0000 |
| n2d-standard-2 | General Purpose | 1 | 2 | 8 | 0 | 1.00 | $0.0400 |
| n2d-standard-4 | General Purpose | 2 | 4 | 16 | 0 | 2.00 | $0.0800 |
| n2d-standard-8 | General Purpose | 4 | 8 | 32 | 0 | 4.00 | $0.1600 |
| n2d-standard-16 | General Purpose | 8 | 16 | 64 | 0 | 8.00 | $0.3200 |
| n2d-standard-32 | General Purpose | 16 | 32 | 128 | 0 | 16.00 | $0.6400 |
| n2d-standard-48 | General Purpose | 24 | 48 | 192 | 0 | 24.00 | $0.9600 |
| n2d-standard-64 | General Purpose | 32 | 64 | 256 | 0 | 32.00 | $1.0000 |
| n2d-standard-80 | General Purpose | 40 | 80 | 320 | 0 | 40.00 | $1.0000 |
| e2-highmem-8 | High Memory | 4 | 8 | 64 | 0 | 6.67 | $0.2667 |
| e2-highmem-16 | High Memory | 8 | 16 | 128 | 0 | 13.33 | $0.5333 |
| n2d-highmem-16 | High Memory | 8 | 16 | 128 | 0 | 13.33 | $0.5333 |
| n2d-highmem-32 | High Memory | 16 | 32 | 256 | 0 | 26.67 | $1.0000 |
| n2d-highmem-64 | High Memory | 32 | 64 | 512 | 0 | 53.33 | $1.0000 |
| e2-highcpu-8 | High Compute | 4 | 8 | 8 | 0 | 2.00 | $0.0800 |
| e2-highcpu-16 | High Compute | 8 | 16 | 16 | 0 | 4.00 | $0.1600 |
| e2-highcpu-32 | High Compute | 16 | 32 | 32 | 0 | 8.00 | $0.3200 |
| n2d-highcpu-8 | High Compute | 4 | 8 | 8 | 0 | 2.00 | $0.0800 |
| n2d-highcpu-16 | High Compute | 8 | 16 | 16 | 0 | 4.00 | $0.1600 |
| n2d-highcpu-32 | High Compute | 16 | 32 | 32 | 0 | 8.00 | $0.3200 |
Data Hub - Flow Management - GCP Instances
The Flow Management rate applies to all nodes in a Data Hub cluster running NiFi regardless of whether the official Flow Management template or a custom template was used to create the cluster.
| Instance | Category | Cores | vCPUs | RAM | GPU | CCUs | Rate/hr* |
| e2-standard-2 | General Purpose | 1 | 2 | 8 | 0 | 1.00 | $0.1500 |
| e2-standard-4 | General Purpose | 2 | 4 | 16 | 0 | 2.00 | $0.3000 |
| e2-standard-8 | General Purpose | 4 | 8 | 32 | 0 | 4.00 | $0.6000 |
| e2-standard-16 | General Purpose | 8 | 16 | 64 | 0 | 8.00 | $1.2000 |
| e2-standard-32 | General Purpose | 16 | 32 | 128 | 0 | 16.00 | $2.4000 |
| n1-standard-2 | General Purpose | 1 | 2 | 7.5 | 0 | 0.96 | $0.1438 |
| n1-standard-4 | General Purpose | 2 | 4 | 15 | 0 | 1.92 | $0.2875 |
| n1-standard-8 | General Purpose | 4 | 8 | 30 | 0 | 3.83 | $0.5750 |
| n1-standard-16 | General Purpose | 8 | 16 | 60 | 0 | 7.67 | $1.1500 |
| n1-standard-32 | General Purpose | 16 | 32 | 120 | 0 | 15.33 | $2.3000 |
| n1-standard-64 | General Purpose | 32 | 64 | 240 | 0 | 30.67 | $4.6000 |
| n2-standard-2 | General Purpose | 1 | 2 | 8 | 0 | 1.00 | $0.1500 |
| n2-standard-4 | General Purpose | 2 | 4 | 16 | 0 | 2.00 | $0.3000 |
| n2-standard-8 | General Purpose | 4 | 8 | 32 | 0 | 4.00 | $0.6000 |
| n2-standard-16 | General Purpose | 8 | 16 | 64 | 0 | 8.00 | $1.2000 |
| n2-standard-32 | General Purpose | 16 | 32 | 128 | 0 | 16.00 | $2.4000 |
| n2-standard-48 | General Purpose | 24 | 48 | 192 | 0 | 24.00 | $3.6000 |
| n2-standard-64 | General Purpose | 32 | 64 | 256 | 0 | 32.00 | $4.8000 |
| n2-standard-80 | General Purpose | 40 | 80 | 320 | 0 | 40.00 | $6.0000 |
| n2d-standard-2 | General Purpose | 1 | 2 | 8 | 0 | 1.00 | $0.1500 |
| n2d-standard-4 | General Purpose | 2 | 4 | 16 | 0 | 2.00 | $0.3000 |
| n2d-standard-8 | General Purpose | 4 | 8 | 32 | 0 | 4.00 | $0.6000 |
| n2d-standard-16 | General Purpose | 8 | 16 | 64 | 0 | 8.00 | $1.2000 |
| n2d-standard-32 | General Purpose | 16 | 32 | 128 | 0 | 16.00 | $2.4000 |
| n2d-standard-48 | General Purpose | 24 | 48 | 192 | 0 | 24.00 | $3.6000 |
| n2d-standard-64 | General Purpose | 32 | 64 | 256 | 0 | 32.00 | $4.8000 |
| n2d-standard-80 | General Purpose | 40 | 80 | 320 | 0 | 40.00 | $6.0000 |
| e2-highmem-8 | High Memory | 4 | 8 | 64 | 0 | 6.67 | $1.0000 |
| e2-highmem-16 | High Memory | 8 | 16 | 128 | 0 | 13.33 | $2.0000 |
| n2d-highmem-16 | High Memory | 8 | 16 | 128 | 0 | 13.33 | $2.0000 |
| n2d-highmem-32 | High Memory | 16 | 32 | 256 | 0 | 26.67 | $4.0000 |
| n2d-highmem-64 | High Memory | 32 | 64 | 512 | 0 | 53.33 | $8.0000 |
| e2-highcpu-8 | High Compute | 4 | 8 | 8 | 0 | 2.00 | $0.3000 |
| e2-highcpu-16 | High Compute | 8 | 16 | 16 | 0 | 4.00 | $0.6000 |
| e2-highcpu-32 | High Compute | 16 | 32 | 32 | 0 | 8.00 | $1.2000 |
| n2d-highcpu-8 | High Compute | 4 | 8 | 8 | 0 | 2.00 | $0.3000 |
| n2d-highcpu-16 | High Compute | 8 | 16 | 16 | 0 | 4.00 | $0.6000 |
| n2d-highcpu-32 | High Compute | 16 | 32 | 32 | 0 | 8.00 | $1.2000 |
Operational Database - GCP Instances
DataFlow Functions - GCP
| Total Billable Invocations per Flow/Month | Price per Billable Invocation |
First 1,000 Billable Invocations |
$0.1000 |
Next 9,000 Billable Invocations |
$0.0200 |
Next 90,000 Billable Invocations |
$0.0020 |
Next 900,000 Billable Invocations |
$0.0003 |
Over 1,000,001 Billable Invocations |
$0.0001 |
*Notes:
- Pricing Volume Tiers are per individual DataFlow Function
- The volume table above is a tiered volume table, where the first 1,000 invocations are always charged at $0.10 per month, the next 9,000 invocations per month are charged at $0.02 per month and so on
- “Billable invocation” is a combination of function invocations and function duration. Every time an instance of DataFlow Functions is invoked that counts as one Billable Invocation. If a DFF runs for more than one second, each subsequent second (after the initial second) counts as another Billable Invocation. In addition, if a DFF invocation runs for less than one second, each fractional second will count as one Billable Invocation.