EC2 pricing metrics

pricing_metrics.py

"""
Disclaimer:
These projects are not a part of Datadog's subscription services and are provided for example purposes only
They are NOT guaranteed to be bug free and are not production quality
If you choose to use to adapt them for use in a production environment, you do so at your own risk.
"""

from datadog import statsd

costs = [['a1.medium',0.03], ['a1.large',0.05], ['a1.xlarge',0.1], ['a1.2xlarge',0.2], ['a1.4xlarge',0.41],
         ['a1.metal',0.41], ['t3.nano',0.01], ['t3.micro',0.01], ['t3.small',0.02], ['t3.medium',0.04],
         ['t3.large',0.08], ['t3.xlarge',0.17], ['t3.2xlarge',0.33], ['t3a.nano',0], ['t3a.micro',0.01],
         ['t3a.small',0.02], ['t3a.medium',0.04], ['t3a.large',0.08], ['t3a.xlarge',0.15], ['t3a.2xlarge',0.3],
         ['t2.nano',0.01], ['t2.micro',0.01], ['t2.small',0.02], ['t2.medium',0.05], ['t2.large',0.09],
         ['t2.xlarge',0.19], ['t2.2xlarge',0.37], ['m5.large',0.1], ['m5.xlarge',0.19], ['m5.2xlarge',0.38],
         ['m5.4xlarge',0.77], ['m5.8xlarge',1.54], ['m5.12xlarge',2.3], ['m5.16xlarge',3.07], ['m5.24xlarge',4.61],
         ['m5.metal',4.61], ['m5a.large',0.09], ['m5a.xlarge',0.17], ['m5a.2xlarge',0.34], ['m5a.4xlarge',0.69],
         ['m5a.8xlarge',1.38], ['m5a.12xlarge',2.06], ['m5a.16xlarge',2.75], ['m5a.24xlarge',4.13], ['m5ad.large',0.1],
         ['m5ad.xlarge',0.21], ['m5ad.2xlarge',0.41], ['m5ad.4xlarge',0.82], ['m5ad.12xlarge',2.47],
         ['m5ad.24xlarge',4.94], ['m5d.large',0.11], ['m5d.xlarge',0.23], ['m5d.2xlarge',0.45], ['m5d.4xlarge',0.9],
         ['m5d.8xlarge',1.81], ['m5d.12xlarge',2.71], ['m5d.16xlarge',3.62], ['m5d.24xlarge',5.42], ['m5d.metal',5.42],
         ['m5dn.large',0.14], ['m5dn.xlarge',0.27], ['m5dn.2xlarge',0.54], ['m5dn.4xlarge',1.09], ['m5dn.8xlarge',2.18],
         ['m5dn.12xlarge',3.26], ['m5dn.16xlarge',4.35], ['m5dn.24xlarge',6.53], ['m5n.large',0.12], ['m5n.xlarge',0.24],
         ['m5n.2xlarge',0.48], ['m5n.4xlarge',0.95], ['m5n.8xlarge',1.9], ['m5n.12xlarge',2.86], ['m5n.16xlarge',3.81],
         ['m5n.24xlarge',5.71], ['m4.large',0.1], ['m4.xlarge',0.2], ['m4.2xlarge',0.4], ['m4.4xlarge',0.8],
         ['m4.10xlarge',2], ['m4.16xlarge',3.2], ['c5.large',0.09], ['c5.xlarge',0.17], ['c5.2xlarge',0.34],
         ['c5.4xlarge',0.68], ['c5.9xlarge',1.53], ['c5.12xlarge',2.04], ['c5.18xlarge',3.06], ['c5.24xlarge',4.08],
         ['c5.metal',4.08], ['c5d.large',0.1], ['c5d.xlarge',0.19], ['c5d.2xlarge',0.38], ['c5d.4xlarge',0.77],
         ['c5d.9xlarge',1.73], ['c5d.12xlarge',2.3], ['c5d.18xlarge',3.46], ['c5d.24xlarge',4.61], ['c5d.metal',4.61],
         ['c5n.large',0.11], ['c5n.xlarge',0.22], ['c5n.2xlarge',0.43], ['c5n.4xlarge',0.86], ['c5n.9xlarge',1.94],
         ['c5n.18xlarge',3.89], ['c5n.metal',3.89], ['c4.large',0.1], ['c4.xlarge',0.2], ['c4.2xlarge',0.4],
         ['c4.4xlarge',0.8], ['c4.8xlarge',1.59], ['p3.2xlarge',3.06], ['p3.8xlarge',12.24], ['p3.16xlarge',24.48],
         ['p2.xlarge',0.9], ['p2.8xlarge',7.2], ['p2.16xlarge',14.4], ['g4dn.xlarge',0.53], ['g4dn.2xlarge',0.75],
         ['g4dn.4xlarge',1.2], ['g4dn.8xlarge',2.18], ['g4dn.12xlarge',3.91], ['g4dn.16xlarge',4.35],
         ['g3.4xlarge',1.14], ['g3.8xlarge',2.28], ['g3.16xlarge',4.56], ['g3s.xlarge',0.75], ['x1.16xlarge',6.67],
         ['x1.32xlarge',13.34], ['x1e.xlarge',0.83], ['x1e.2xlarge',1.67], ['x1e.4xlarge',3.34], ['x1e.8xlarge',6.67],
         ['x1e.16xlarge',13.34], ['x1e.32xlarge',26.69], ['r5.large',0.13], ['r5.xlarge',0.25], ['r5.2xlarge',0.5],
         ['r5.4xlarge',1.01], ['r5.8xlarge',2.02], ['r5.12xlarge',3.02], ['r5.16xlarge',4.03], ['r5.24xlarge',6.05],
         ['r5.metal',6.05], ['r5a.large',0.11], ['r5a.xlarge',0.23], ['r5a.2xlarge',0.45], ['r5a.4xlarge',0.9],
         ['r5a.8xlarge',1.81], ['r5a.12xlarge',2.71], ['r5a.16xlarge',3.62], ['r5a.24xlarge',5.42], ['r5ad.large',0.13],
         ['r5ad.xlarge',0.26], ['r5ad.2xlarge',0.52], ['r5ad.4xlarge',1.05], ['r5ad.12xlarge',3.14],
         ['r5ad.24xlarge',6.29], ['r5d.large',0.14], ['r5d.xlarge',0.29], ['r5d.2xlarge',0.58], ['r5d.4xlarge',1.15],
         ['r5d.8xlarge',2.3], ['r5d.12xlarge',3.46], ['r5d.16xlarge',4.61], ['r5d.24xlarge',6.91], ['r5d.metal',6.91],
         ['r5dn.large',0.17], ['r5dn.xlarge',0.33], ['r5dn.2xlarge',0.67], ['r5dn.4xlarge',1.34], ['r5dn.8xlarge',2.67], 
         ['r5dn.12xlarge',4.01], ['r5dn.16xlarge',5.34], ['r5dn.24xlarge',8.02], ['r5n.large',0.15], ['r5n.xlarge',0.3],
         ['r5n.2xlarge',0.6], ['r5n.4xlarge',1.19], ['r5n.8xlarge',2.38], ['r5n.12xlarge',3.58], ['r5n.16xlarge',4.77],
         ['r5n.24xlarge',7.15], ['r4.large',0.13], ['r4.xlarge',0.27], ['r4.2xlarge',0.53], ['r4.4xlarge',1.06],
         ['r4.8xlarge',2.13], ['r4.16xlarge',4.26], ['z1d.large',0.19], ['z1d.xlarge',0.37], ['z1d.2xlarge',0.74],
         ['z1d.3xlarge',1.12], ['z1d.6xlarge',2.23], ['z1d.12xlarge',4.46], ['z1d.metal',4.46], ['i3.large',0.16],
         ['i3.xlarge',0.31], ['i3.2xlarge',0.62], ['i3.4xlarge',1.25], ['i3.8xlarge',2.5], ['i3.16xlarge',4.99],
         ['i3.metal',4.99], ['i3en.large',0.23], ['i3en.xlarge',0.45], ['i3en.2xlarge',0.9], ['i3en.3xlarge',1.36],
         ['i3en.6xlarge',2.71], ['i3en.12xlarge',5.42], ['i3en.24xlarge',10.85], ['i3en.metal',10.85], ['h1.2xlarge',0.47],
         ['h1.4xlarge',0.94], ['h1.8xlarge',1.87], ['h1.16xlarge',3.74], ['d2.xlarge',0.69], ['d2.2xlarge',1.38],
         ['d2.4xlarge',2.76], ['d2.8xlarge',5.52]]

print('submitting pricing metrics')
for x in costs:
    itype = 'instance-type:' + x[0]
    hname = 'host: '  # uses blank hostname
    statsd.gauge('datadog.demo.ec2_cost_per_hour', x[1], tags=[itype, hname])