Standard_E96ds_v5 on Databricks
Standard_E96ds_v5 is a memory optimized Azure worker type with 96 vCPUs and 672 GB of memory. On Databricks it uses 33 DBUs an hour, so 1 node on Jobs Compute in East US costs $16.81 an hour: $9.90 for the DBUs and $6.91 to Azure for the machine.
Azure · Memory optimized · Edsv5
Standard_E96ds_v5 at a glance
- vCPUs
- 96
- Memory
- 672 GB
- Own SSD
- 3,600 GB
- DBUs an hour
- 33
- Classic compute
- With Photon
- 82.5
- DBUs an hour, Jobs Compute
- 1 node an hour
- $16.81
- Jobs Compute, East US
1 node an hour on each kind of compute
In East US on the Premium plan. Databricks charges the DBUs, Azure the machine. A cluster pays this for every worker and for the driver.
| Compute | DBUs an hour | A DBU | Databricks | Machine | An hour |
|---|---|---|---|---|---|
| Jobs Compute | 33 | $0.30 | $9.90 | $6.91 | $16.81 |
| Jobs Compute + Photon | 82.5 | $0.30 | $24.75 | $6.91 | $31.66 |
| All-Purpose Compute | 33 | $0.55 | $18.15 | $6.91 | $25.06 |
| All-Purpose Compute + Photon | 66 | $0.55 | $36.30 | $6.91 | $43.21 |
| Lakeflow pipelines Core | 33 | $0.30 | $9.90 | $6.91 | $16.81 |
| Lakeflow pipelines Pro | 33 | $0.38 | $12.54 | $6.91 | $19.45 |
| Lakeflow pipelines Advanced | 33 | $0.54 | $17.82 | $6.91 | $24.73 |
In each region
1 node an hour on Jobs Compute, Premium, cheapest first.
| Region | Machine | An hour |
|---|---|---|
| East US | $6.91 | $16.81 |
| East US 2 | $6.91 | $16.81 |
| West US 2 | $6.91 | $16.81 |
| West US 3 | $6.91 | $16.81 |
| Central India | $7.25 | $17.15 |
| Sweden Central | $7.30 | $17.20 |
| Canada Central | $7.58 | $17.48 |
| North Europe | $7.68 | $17.58 |
| Central US | $7.81 | $17.71 |
| UK South | $8.11 | $18.01 |
| France Central | $8.11 | $18.01 |
| West Europe | $8.30 | $18.20 |
| Germany West Central | $8.30 | $18.20 |
| Southeast Asia | $8.35 | $18.25 |
| Japan East | $8.35 | $18.25 |
| Australia East | $8.35 | $18.25 |
| Brazil South | $10.94 | $20.84 |
Other memory optimized sizes
- Standard_E4ds_v54 vCPUs, 32 GB, 1.5 DBUs an hour
- Standard_E8ds_v58 vCPUs, 64 GB, 2.75 DBUs an hour
- Standard_E16ds_v516 vCPUs, 128 GB, 5.5 DBUs an hour
- Standard_E32ds_v532 vCPUs, 256 GB, 11 DBUs an hour
- Standard_E48ds_v548 vCPUs, 384 GB, 16.5 DBUs an hour
- Standard_E64ds_v564 vCPUs, 512 GB, 22 DBUs an hour
- Standard_E96ds_v596 vCPUs, 672 GB, 33 DBUs an hour
Questions
- How many DBUs does Standard_E96ds_v5 use on Databricks?
- Standard_E96ds_v5 uses 33 DBUs an hour on Databricks classic compute without Photon. With Photon it uses 82.5 DBUs an hour on Jobs Compute and 66 on All-Purpose Compute, as Databricks' price list gives them.
- How much does Standard_E96ds_v5 cost an hour on Databricks?
- In East US, 1 Standard_E96ds_v5 node on Jobs Compute costs $16.81 an hour: $9.90 to Databricks for 33 DBUs at $0.30 each, and $6.91 to Azure for the machine. On All-Purpose Compute the same node costs $25.06 an hour, and on Lakeflow pipelines Core $16.81. A cluster pays this for every worker and for the driver.
- Does Photon cost more on Standard_E96ds_v5?
- Yes, by the hour: with Photon, 1 Standard_E96ds_v5 node on Jobs Compute in East US costs $31.66 an hour instead of $16.81, because it uses 82.5 DBUs an hour instead of 33. It saves money only when the job finishes fast enough to make up the difference.
- Which region is cheapest for Standard_E96ds_v5 on Databricks?
- East US, at $16.81 an hour for 1 node on Jobs Compute, against $20.84 in Brazil South, the dearest of the 17 regions listed here that offer it. Databricks charges the same for a DBU of Jobs Compute in every Azure region listed, so the gap is the machine.
- What is the same size as Standard_E96ds_v5 on the other clouds?
- The nearest worker types of the same kind are r6id.24xlarge on AWS and n2-highmem-80 on Google Cloud. Each cloud gives its own DBUs an hour for its types, so compare what a cluster costs in the calculator, not the DBUs alone.
- What is the next size down from Standard_E96ds_v5?
- Standard_E96ds_v5 is the largest size of its family listed here. One size down is Standard_E64ds_v5, at 22 DBUs an hour.
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