Exoscale vs Google Cloud Platform: a decision that affects latency, compliance and costs for organisations operating in England and across Europe. This analysis presents practical comparisons, EU data-residency consequences, real-world cost examples and a migration checklist that maps Google Cloud services to Exoscale equivalents. Evidence-based citations and links to vendor pages support technical choices and legal considerations.
Quick executive comparison
- Primary differentiation: Exoscale positions as a European-first cloud with data residency and S3-compatibility, while Google Cloud Platform (GCP) offers larger global scale, advanced AI/ML services and broader managed offerings.
- Best fit: Use Exoscale for EU-focused deployments prioritising sovereignty, predictable pricing for standard workloads and simpler object-store semantics. Use GCP for ML training, managed analytics and global multi-region services.
- Decision factors: compliance (GDPR), latency across Europe, GPU pricing and availability, managed Kubernetes feature parity, and third-party ecosystem.
Core technical comparison: compute, storage, networking
Compute and instances
- Exoscale: virtual machines with clear SKU tiers suitable for web apps and containers. Refer to official compute details: Exoscale Compute.
- Google Cloud Platform: wide range including Compute Engine, Preemptible VMs, and specialized instances for ML and HPC. Official page: GCP Compute Engine.
Practical note: For TN web and API workloads with predictable CPU/memory ratios, Exoscale instances often deliver simpler pricing and fewer surprise egress tiers. For custom ML workloads requiring TPU/optimized GPUs, GCP retains an advantage.
Storage: object, block and S3 compatibility
- Exoscale offers S3-compatible object storage and block volumes tailored for EU regions, simplifying migrations from S3 clients. See: Exoscale Object Storage.
- GCP provides Cloud Storage with multi-region options and tight integration to BigQuery and Dataflow. See: GCP Cloud Storage.
Key implication: S3 compatibility reduces application changes during migration; Exoscale's EU-only control planes can ease GDPR assessments.
Networking, latency and peering
- Exoscale has multiple European PoPs with focus on intra-EU latency. GCP provides extensive global backbone with premium tiers and Cloud Interconnect for private peering.
- For latency-sensitive services within Europe, local Exoscale regions (e.g., Frankfurt, Vienna, Zurich) can reduce round-trip times versus cross-Atlantic routes.
Benchmarks should be measured per-app. A recommended methodology appears later in the benchmarks section.
Detailed mapping: GCP service → Exoscale equivalent
Compute and orchestration
- Google Compute Engine → Exoscale VM instances
- Google Kubernetes Engine (GKE) → Exoscale Kubernetes Service (managed K8s offering) or self-managed K8s on Exoscale VMs
- Cloud Functions / Cloud Run → Deploy containerized workloads on Exoscale with container runtimes or use event-driven patterns via custom handlers
Relevant Terraform provider for Exoscale: Exoscale Terraform provider.
Storage and databases
- Cloud Storage → Exoscale Object Storage (S3-compatible)
- Persistent Disks → Exoscale Block Storage
- Cloud SQL / BigQuery → Managed databases must be evaluated case-by-case; Exoscale supports managed DB offerings through partners or self-managed instances.
Networking
- VPC, Shared VPC → Exoscale VPC equivalents with security groups and network ACLs
- Cloud Load Balancing → Exoscale load balancers and reverse-proxy appliances
Migration tip: Map each GCP resource to an Exoscale equivalent, then validate IAM and network policies before cutover.
Cost comparison with real-world examples (2025–2026 data)
Example workload: 3-node web application
Assumptions: 3 VMs (4 vCPU, 8 GB RAM), 1 TB monthly egress (EU→Internet), 1 TB object storage.
| Item |
Exoscale (monthly) |
GCP (monthly) |
Notes |
| 3 VMs (4vCPU/8GB) |
~€150 |
~€180 |
Exoscale simpler instance tiers; GCP sustained-use discounts may lower cost for 24/7 workloads |
| Block storage 1TB |
~€40 |
~€40 |
Comparable raw price; performance tiers differ |
| Object storage 1TB |
~€20 |
~€20 |
Both offer standard storage; lifecycle rules affect cost |
| Egress 1TB |
~€80 |
~€90 |
GCP has tiered egress; Exoscale often offers competitive EU egress pricing |
| Estimated monthly total |
~€290 |
~€330 |
Example only; actual prices depend on contract and sustained use |
2026 note: Pricing shown reflects vendor pages as of early 2026; always validate with Exoscale Pricing and GCP Pricing.
Benchmarks and methodology (recommended independent tests)
Benchmark checklist
- Define workload (CPU bound, I/O bound, GPU training, inference).
- Use identical OS images and kernel tuning across clouds.
- Measure: single-core and multi-core CPU, disk IOPS and throughput, object-store PUT/GET latency, network RTT between EU regions.
- Repeat across multiple times and days to smooth transient network variance.
- CPU: sysbench, stress-ng
- Disk: fio
- Network: iperf3, curl for object-store tests
- GPU ML: run standardized TensorFlow training steps with identical datasets
Gap in market: Independent third-party benchmarks focused on EU latency and S3-compatibility remain rare; such evidence can strongly influence vendor selection.
Migration guide and checklist
Phase 1: Assessment
- Inventory GCP resources and identify critical data residency constraints.
- Map GCP services to Exoscale equivalents (see mapping section).
- Identify third-party dependencies (managed DBs, BigQuery pipelines).
Phase 2: Proof of Concept (PoC)
- Deploy small-scale PoC on Exoscale, including object storage and VMs.
- Validate Terraform flows using the Exoscale Terraform provider.
- Run latency and throughput tests.
Phase 3: Migration and cutover
- Export objects from GCP Cloud Storage (use gsutil) and import to Exoscale S3 endpoint with multipart uploads.
- Recreate infrastructure via IaC, test IAM and network rules, then perform incremental cutover.
- Monitor for errors and roll back plan.
Checklist items: DNS TTL reduction, backup snapshot timing, security group validation, SLA acceptance criteria.
Compliance, certifications and data residency
- Exoscale emphasises EU data residency and often lists ISO/IEC and security practices on vendor pages; confirm current certifications on the vendor site: Exoscale Security.
- GDPR remains the primary regulation for England-based organisations processing EU personal data. Guidance: GDPR guidance.
- GCP publishes compliance pages and a broader set of certifications for multi-region deployments: GCP Compliance.
Recommendation: Produce a data map and legal review; prefer EU-only providers for strict residency requirements.
Ecosystem, support and developer experience
- GCP benefits from broad managed services (BigQuery, Dataflow, Vertex AI) and large marketplace integrations.
- Exoscale offers focused APIs, S3 compatibility and Terraform support; community and partner integrations are growing but smaller than GCP.
- Evaluate SLA terms and enterprise support options directly with each vendor before final selection.
FAQs
What are the primary advantages of choosing Exoscale over GCP for EU workloads?
Exoscale gives stronger EU-focused data residency controls, S3-compatibility with simpler migration paths and often more predictable pricing for small-to-medium workloads. For regulatory-sensitive projects, these factors reduce legal complexity.
Can applications using Google Cloud Storage move to Exoscale without code changes?
If applications use standard S3 clients or can be adjusted to use S3 semantics, migration is straightforward because Exoscale provides S3-compatible object storage. For native GCS-specific APIs, minimal code changes or a compatibility layer will be required.
For many I/O and CPU-bound web workloads within Europe, performance parity is achievable. For specialized ML/GPU or global-scaling workloads, GCP typically offers superior hardware options and managed services.
How to compare GPU costs for ML between Exoscale and GCP?
Compare hourly GPU rates, included RAM, local SSDs, and data egress assumptions. Also consider managed training services (GCP) versus rented GPU VMs (Exoscale or partners). Real workload tests are essential.
Yes. The official Exoscale Terraform provider is available: Exoscale Terraform provider.
What compliance documentation is necessary for a GDPR audit when switching clouds?
Maintain data processing addenda, vendor security certifications, regional data-flow diagrams and access logs. Confirm the vendor's contractual terms and local data centres.
Does Exoscale have EU data centres in 2026?
Exoscale maintains multiple European regions; confirm the latest list on the vendor site: Exoscale official.
When should organisations prefer GCP over Exoscale?
Prefer GCP when advanced managed services (data warehousing, large-scale ML, global multi-region workloads) or specialized hardware (TPU/advanced GPU types) are critical.
Conclusion
Selecting between Exoscale vs Google Cloud Platform depends on regulatory constraints, workload profile and long-term platform requirements. For EU-focused projects requiring data residency, S3 compatibility and straightforward pricing, Exoscale is a compelling alternative. For global scale, advanced AI services and deep managed ecosystems, GCP remains a top choice.
A recommended next step is to run a 2–4 week PoC using the migration checklist and benchmarks described above, and obtain formal pricing and SLA offers from each vendor before committing.
