Optimize OCI compute shapes, block volume tiers, and network throughput. Use when choosing instance shapes, configuring block volume performance, or benchmarking OCI infrastructure. Trigger with "oraclecloud performance", "oci shape comparison", "oci block volume iops", "oracle cloud performance tuning".
Navigate OCI's opaque shape naming, block volume performance tiers, and shape-dependent network bandwidth. OCI shapes like VM.Standard.E5.Flex, VM.Standard3.Flex, and VM.Standard.A1.Flex look similar but have wildly different performance profiles. Block volume tiers (Balanced, Higher Performance, Ultra High Performance) have different IOPS and throughput limits that are easy to get wrong. This skill maps performance characteristics to shapes and storage tiers so you can make informed infrastructure decisions.
Purpose: Choose the right compute shape and storage tier for your workload by understanding OCI's performance characteristics, and monitor those resources programmatically.
~/.oci/configpip install ociOCI shape names encode processor generation, type, and flexibility:
| Shape | Processor | OCPUs | Network Gbps per OCPU | Best For |
|---|---|---|---|---|
VM.Standard.E5.Flex | AMD EPYC 9J14 (Genoa) | 1–94 | 1 Gbps | General workloads (latest gen) |
VM.Standard.E4.Flex | AMD EPYC 7J13 (Milan) | 1–64 | 1 Gbps | General workloads |
VM.Standard3.Flex | Intel Xeon (Ice Lake) | 1–32 | 1 Gbps | Intel-optimized software |
VM.Standard.A1.Flex | Ampere Altra (ARM) | 1–80 | 1 Gbps | ARM-native, cost-efficient |
VM.Optimized3.Flex | Intel Xeon (Ice Lake) | 1–18 | 4 Gbps | HPC, network-intensive |
BM.Standard.E5.192 | AMD EPYC 9J14 | 192 | 100 Gbps total | Bare metal, full isolation |
Key insight: Flex shapes let you choose OCPU and memory independently. Memory defaults to 1 GB/OCPU min, 64 GB/OCPU max (varies by shape). Network bandwidth scales linearly with OCPUs up to the shape maximum.
Discover what shapes are available in your tenancy and region:
import oci
config = oci.config.from_file("~/.oci/config")
compute = oci.core.ComputeClient(config)
shapes = compute.list_shapes(
compartment_id="ocid1.compartment.oc1..example"
).data
for shape in shapes:
print(
f"{shape.shape}: "
f"OCPUs={shape.ocpus or 'flex'}, "
f"Memory={shape.memory_in_gbs or 'flex'} GB, "
f"Network={shape.networking_bandwidth_in_gbps} Gbps"
)
OCI block volumes have three performance tiers. IOPS and throughput scale with volume size:
| Tier | IOPS / GB | Max IOPS | Throughput / GB | Max Throughput | Cost Multiplier |
|---|---|---|---|---|---|
| Balanced | 60 | 25,000 | 480 KB/s | 480 MB/s | 1x (default) |
| Higher Performance | 75 | 35,000 | 600 KB/s | 480 MB/s | ~1.7x |
| Ultra High Performance | 90–225 | 300,000 | 720 KB/s–2.4 MB/s | 2.4 GB/s | ~3.3x+ |
Example: A 1 TB Balanced volume gets 25,000 IOPS and 480 MB/s throughput. The same 1 TB on Ultra High Performance gets up to 225,000 IOPS and 2.4 GB/s.
config = oci.config.from_file("~/.oci/config")
block_storage = oci.core.BlockstorageClient(config)
# Create a Higher Performance tier volume
volume = block_storage.create_volume(
oci.core.models.CreateVolumeDetails(
compartment_id="ocid1.compartment.oc1..example",
availability_domain="Uocm:US-ASHBURN-AD-1",
display_name="high-perf-data-vol",
size_in_gbs=500,
vpus_per_gb=20 # 10=Balanced, 20=Higher, 30-120=Ultra High
)
).data
print(f"Volume created: {volume.id}")
print(f"Performance: {volume.vpus_per_gb} VPUs/GB")
The vpus_per_gb parameter controls the tier:
10 = Balanced (default)20 = Higher Performance30–120 = Ultra High Performance (scales IOPS linearly)Query actual performance data from running instances and volumes:
from datetime import datetime, timedelta
monitoring = oci.monitoring.MonitoringClient(config)
# Query disk IOPS for a specific instance
response = monitoring.summarize_metrics_data(
compartment_id="ocid1.compartment.oc1..example",
summarize_metrics_data_details=oci.monitoring.models.SummarizeMetricsDataDetails(
namespace="oci_computeagent",
query='DiskIopsRead[5m].mean() + DiskIopsWritten[5m].mean()',
start_time=(datetime.utcnow() - timedelta(hours=1)).isoformat() + "Z",
end_time=datetime.utcnow().isoformat() + "Z"
)
)
for metric in response.data:
for dp in metric.aggregated_datapoints:
print(f"{dp.timestamp}: {dp.value:.0f} total IOPS")
Verify you're getting expected network performance:
# Query network bytes for bandwidth validation
response = monitoring.summarize_metrics_data(
compartment_id="ocid1.compartment.oc1..example",
summarize_metrics_data_details=oci.monitoring.models.SummarizeMetricsDataDetails(
namespace="oci_computeagent",
query='NetworksBytesIn[5m].rate() + NetworksBytesOut[5m].rate()',
start_time=(datetime.utcnow() - timedelta(hours=1)).isoformat() + "Z",
end_time=datetime.utcnow().isoformat() + "Z"
)
)
for metric in response.data:
for dp in metric.aggregated_datapoints:
gbps = (dp.value * 8) / 1_000_000_000
print(f"{dp.timestamp}: {gbps:.2f} Gbps")
Successful completion produces:
| Error | Code | Cause | Solution |
|---|---|---|---|
| NotAuthorizedOrNotFound | 404 | Shape not available in your region/AD | Check availability with list_shapes; try a different AD |
| LimitExceeded | 400 | Tenancy service limit reached | Request limit increase in Console > Governance > Limits |
| InvalidParameter | 400 | Invalid vpus_per_gb value | Use 10, 20, or 30–120 (multiples of 10) |
| TooManyRequests | 429 | Rate limited on metric queries | Reduce query frequency; widen time intervals |
| InternalError | 500 | OCI service issue | Check OCI Status |
| NotAuthenticated | 401 | Bad config or expired key | Verify ~/.oci/config and regenerate API key if needed |
Quick shape lookup with OCI CLI:
# List all flex shapes available in your compartment
oci compute shape list \
--compartment-id ocid1.compartment.oc1..example \
--query "data[?contains(shape, 'Flex')].{Shape:shape, OCPUs:ocpus, Memory:\"memory-in-gbs\"}" \
--output table
# Check block volume performance tier
oci bv volume get \
--volume-id ocid1.volume.oc1..example \
--query "data.{Name:\"display-name\", SizeGB:\"size-in-gbs\", VPUsPerGB:\"vpus-per-gb\"}"
Right-size an instance based on CPU metrics:
import oci
from datetime import datetime, timedelta
config = oci.config.from_file("~/.oci/config")
monitoring = oci.monitoring.MonitoringClient(config)
# Get 7-day average CPU to check if over-provisioned
response = monitoring.summarize_metrics_data(
compartment_id="ocid1.compartment.oc1..example",
summarize_metrics_data_details=oci.monitoring.models.SummarizeMetricsDataDetails(
namespace="oci_computeagent",
query='CpuUtilization[1h].mean()',
start_time=(datetime.utcnow() - timedelta(days=7)).isoformat() + "Z",
end_time=datetime.utcnow().isoformat() + "Z"
)
)
for metric in response.data:
avg_cpu = sum(dp.value for dp in metric.aggregated_datapoints) / len(metric.aggregated_datapoints)
if avg_cpu < 20:
print(f"Instance {metric.dimensions.get('resourceId', 'unknown')}: "
f"avg CPU {avg_cpu:.1f}% — consider downsizing")
After optimizing shapes and storage, proceed to oraclecloud-cost-tuning to track spend and set budget alerts, or see oraclecloud-observability to set up ongoing performance monitoring with alarms.