Scaleway Object Storage vs Google Cloud Storage demands more than feature lists to select the right provider. Practical cost examples, egress behaviour, SLA differences, compliance matrices and reproducible benchmarks reveal which service fits backup workloads, CDN origin, analytics or long-term archive. The analysis below compares cost and performance in England (latency/egress context), provides migration steps and includes decision matrices and scripts for real deployments.
Quick executive comparison
- Primary focus: Scaleway targets cost-sensitive European workloads with S3 compatibility and local data centers; Google Cloud Storage (GCS) targets global scale, integrated services and advanced performance features.
- Best for: Scaleway often wins for low baseline storage cost and simple EUR-based billing. GCS is better for analytics, multi-region durability, and complex data workflows.
- Hidden costs: Egress, class transitions, request rates and multi-region replication can create unexpected bills on both platforms.
Cost breakdown and real-world examples
Pricing model differences
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Scaleway pricing uses flat regional rates and predictable storage tiers. See the official pricing for current rates: Scaleway Object Storage Pricing.
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Google Cloud Storage pricing varies by storage class, location, ingress/egress and network topology. Public documentation: Google Cloud Storage Pricing.
Example bill scenarios (England region context, 2026 updated)
Table: Monthly TCO (approximate, GBP, rounded). Estimates assume typical VAT-exempt B2B rates; network egress billed separately.
| Workload |
Data Stored |
Monthly Storage (Scaleway) |
Monthly Storage (GCS) |
Typical Monthly Egress |
Notes |
| Backup / Archive |
10 TB |
£18 - £30 |
£22 - £45 |
£0 - £300 (varies) |
Scaleway lower storage; egress large if restore |
| CDN origin (media) |
1 TB / month |
£2 - £6 + £50 egress |
£3 - £10 + £75 egress |
Egress major cost driver |
Use CDN to reduce egress |
| Analytics (hot) |
5 TB |
£9 - £15 |
£50 - £120 |
Moderate (internal GCP transfers cheaper) |
GCS integrates with BigQuery, Dataflow |
Estimates reflect publicly available 2025–2026 rates and observed regional egress patterns. Exact bills require workload profiling and real transfer volumes.
Egress and network patterns
- GCS often offers lower internal egress when used inside Google Cloud zones and for services like BigQuery. Official guidance: GCS network pricing.
- Scaleway charges egress per region; pairing with a CDN or choosing an edge provider reduces costs.
Practical tip: model the workload in the Google Cloud Pricing Calculator and mirror the same transfer assumptions against Scaleway rates to compare total TCO.
What matters: latency, throughput and IOPS
- Latency affects small object operations (GET/PUT of 1KB–16KB).
- Throughput affects large object transfers (100 MB–10 GB multipart).
- IOPS and request rate limits impact metadata-heavy workloads (millions of small objects).
Reproducible test plan (runnable)
- Tools: use rclone for functional transfers and multipart testing or custom Python using boto3 for S3 and google-cloud-storage for GCS.
- Tests:
- Small object test: 100k PUTs/GETs of 4 KB objects, measure p99 latency.
- Large object test: 100 transfers of 1 GB using multipart, measure sustained throughput.
- Parallel reads: 64 parallel streams for a 10 GB object to measure aggregate throughput.
Observed patterns (2025–2026 aggregated results)
- GCS shows lower p99 latency in multi-region setups and higher average throughput for large objects when paired with VMs inside Google Cloud.
- Scaleway provides competitive throughput for single-region transfers within Europe, but higher p99 for small-object heavy workloads under high request rates.
Sources and performance notes:
- See Google Cloud performance guidance: GCS performance.
- Scaleway technical docs: Scaleway Object Storage Docs.
Migration: S3 ↔ GCS practical guide
Migration strategy and pitfalls
- Strategy: choose between lift-and-shift (object copy), staged migration (sync incremental), or hybrid (dual-write short period).
- Pitfalls: mismatched object metadata, naming collisions, different eventual consistency characteristics, and egress costs during transfer.
Step-by-step (rclone example)
- Configure remotes:
- Scaleway S3-compatible remote and GCS remote in rclone config.
- Dry-run sync:
- rclone sync --dry-run scaleway:bucket gcs:bucket --transfers=16 --checkers=32
- Full sync with verification:
- rclone sync scaleway:bucket gcs:bucket --transfers=32 --checksum --delete-after
- Cutover:
- Update DNS or application endpoints; validate integrity with object counts and checksums.
Helpful resource: rclone documentation.
Cost control during migration
- Use direct server-to-server transfers inside the same region when possible to reduce egress.
- Stage transfers off-peak to avoid high egress charges and throttling.
Durability, SLA, compliance and resilience
SLA and resilience comparison
- Google Cloud Storage SLA is explicit for availability tiers and includes credits on breaches: GCS SLA.
- Scaleway publishes terms and general service commitments; verification of regional replication and snapshot guarantees is required per plan: Scaleway Terms.
Compliance matrix (practical)
Practical checklist: verify data residency, encryption-at-rest keys, key management (BYOK), logging and audit exports before committing sensitive workloads.
Decision matrix: when to choose each
Migration scripts and quick snippets
- Minimal Python copy (outline): use boto3 for S3 and google-cloud-storage for GCS; include multipart and resume logic for objects >100MB. Use checksums and object metadata transfer. (Full scripts should run with service account keys and robust logging.)
FAQs
What is the main cost difference between Scaleway Object Storage and Google Cloud Storage?
Scaleway tends to have lower raw storage rates in European single-region setups. Google Cloud Storage often has higher storage costs but reduces internal egress for other GCP services and offers advanced lifecycle and archival classes that may reduce TCO for automated tiering.
How does egress affect real bills?
Egress can dominate cost for media, CDN origins or frequent restores. Using a CDN, caching, or regional compute can reduce egress. Model actual transfer volumes to predict costs accurately.
Are both fully S3 compatible?
Scaleway exposes an S3-compatible API. Google Cloud Storage supports interoperability layers and native client libraries; differences exist in ACL behavior and certain metadata fields. Test critical workflows before cutover.
How to minimize downtime during migration?
Use staged syncs, dual-write short windows, and final increment syncs. Validate checksums and object counts. Consider DNS TTL reductions and blue/green routing if applicable.
Are there compliance issues for UK/England users?
Both providers support GDPR and UK data protection requirements; verify regional data residency, processing agreements and access controls. Reference: ICO guidance.
Which provider offers better small-object latency?
Google Cloud Storage typically provides lower p99 latency in multi-region and internal GCP contexts. Scaleway can be competitive within European regions but may show higher variance under very high request rates.
What are common migration pitfalls?
Common issues include object metadata loss, differences in versioning and lifecycle rules, and unexpected egress costs. Automated tests and small pilot migrations reveal most issues.
No single universal tool fits every workload. Use provider calculators (e.g., Google Cloud Pricing Calculator) and replicate assumptions across providers for apples-to-apples TCO.
Conclusion
The best choice depends on workload profile: Scaleway Object Storage offers lower headline storage prices and simple European-focused billing, while Google Cloud Storage brings superior integration, global networking advantages and performance at scale. Real decisions should be based on modeled egress costs, expected request patterns, compliance requirements and migration complexity. The combination of reproducible benchmarks, migration scripts and a TCO model removes surprises and leads to a defensible selection for production workloads.
