Stop Overpaying for GP2: GP3 Cost & Performance Explained

Most people still use GP2 as the default EBS volume type in launch templates and infrastructure modules. Not because it is optimal today, but because it has been there for years.

In most production audits I review, oversized GP2 volumes are one of the easiest cost optimizations left untouched.
GP2 performance is tied to volume size and burst credits, which can lead to unstable latency and throughput during sustained workloads. For production databases, search clusters, and write-heavy services, this behavior directly impacts reliability and user experience.GP3 changes this model by separating performance from storage size, enabling consistent throughput, predictable latency, and more efficient cost control.

To understand why this difference matters in real workloads, let’s examine the architecture behind both.

How GP2 Was Designed

GP2 follows a size-based performance model:

IOPS = 3 × Volume Size (GB)

Key characteristics:

• Minimum 100 IOPS
• Burst up to 3,000 IOPS
• Maximum 16,000 IOPS
• Credit-based burst model
• Throughput indirectly tied to volume size

The design principle was straightforward: scale storage to scale performance.
That approach worked when workloads were smaller and less sustained. Today, it introduces unnecessary coupling between capacity and predictable performance.

The Credit-Based Burst Reality

GP2 uses a token bucket mechanism.
When I/O demand exceeds baseline:

1. Burst credits are consumed
2. IOPS temporarily increases
3. Credits deplete under sustained load
4. Performance drops back to baseline

In real production systems, this is where instability appears.
Performance looks stable during short load tests.
Thirty to forty minutes into steady write pressure, latency increases and queue depth begins to rise.
In CloudWatch, this typically shows up as:

• Increasing VolumeQueueLength
• Rising VolumeWriteLatency
• Higher variability in VolumeIdleTime

If your workload regularly operates above baseline IOPS for extended periods, GP2 introduces risk.
The issue is not peak performance.
It is sustained behavior under continuous pressure.

GP3: Decoupled Performance Architecture

GP3 removes the dependency between storage size and performance.
Every GP3 volume includes:

• 3,000 IOPS baseline
• 125 MB/s baseline throughput

You can provision up to:

• 16,000 IOPS
• 1,000 MB/s throughput
• 64 TiB storage

IOPS and throughput are explicitly configured and independent of storage capacity.
There is no credit system.
There is no burst window.
Performance is deterministic.

Architecture Comparison

GP2 provides proportional performance tied to size.
GP3 provides provisioned performance tied to workload requirements.
That architectural difference becomes operationally significant under sustained load.

IOPS vs Throughput: Workload Sensitivity

Performance tuning requires understanding workload patterns.

• Small block workloads (4K–16K) are typically IOPS-bound.
• Large block workloads are typically throughput-bound.

GP2 scales both indirectly through volume size.
GP3 allows each dimension to be tuned explicitly.
For production databases running steady transactions, sustained IOPS consistency is often more important than theoretical burst capacity.

Sustained Workload Benchmark

Test configuration:

• 500 GB volume
• 70% write workload
• 45-minute sustained load
• Target: 6,000 IOPS

These numbers reflect typical behavior observed under sustained write-heavy workloads, not short synthetic burst tests

GP2 performs adequately at the beginning of the workload.
Once burst credits deplete, latency variance increases and throughput stabilizes at baseline.
GP3 maintains consistent I/O performance across the entire duration.
Consistency is what production systems require.

Real Cost Comparison

Requirement:

• 6,000 sustained IOPS
• 500 GB usable storage

GP2

To achieve 6,000 baseline IOPS:

6,000 ÷ 3 = 2,000 GB

2 TB × ~$0.10 per GB ≈ ~$200/month
You are paying for capacity purely to achieve performance.

GP3

Provision:

• 500 GB storage
• 6,000 IOPS

Estimated monthly cost:

• 500 GB × ~$0.08 = ~$40
• Additional 3,000 IOPS × ~$0.005 = ~$15
• Throughput adjustment ≈ ~$5–10

Total ≈ ~$60–65/month
The cost difference is not linear. It compounds with scale across environments and accounts.
Across multiple volumes, the annual savings become material.

Operational Considerations

Migrating from GP2 to GP3 is straightforward:

aws ec2 modify-volume \
  --volume-id vol-xxxxxxxx \
  --volume-type gp3

In most cases:

• Modification is online
• No IAM changes required
• Monitoring remains identical

From an operational perspective, the change is minimal.
From a performance predictability perspective, the impact is significant.

When GP3 Should Be the Default

• Production databases
• Search and indexing systems
• Write-heavy services
• Sustained transaction workloads
• Cost optimization initiatives

GP2 remains acceptable only where legacy automation or temporary environments prevent modification.

Conclusion

GP2 ties performance to storage size and depends on burst credits.
GP3 separates capacity from performance and provides sustained deterministic I/O.

Choosing GP2 for new production systems requires justification.
For sustained workloads, GP3 is the rational default.