Spot instances and sovereign clouds: cost-optimizing ClickHouse deployments

Hook — Your ClickHouse bill is exploding in a sovereign region. Here’s how to stop it.

Many teams I talk to in 2026 face the same triad: strict sovereignty requirements, fast-growing OLAP workloads on ClickHouse, and a cloud bill that balloons as data and queries scale. The good news: you can reclaim most of that cost without sacrificing compliance or performance by combining spot/interruptible instances, a multi-tiered caching and storage strategy, and cost-aware autoscaling. This article gives a practical playbook — code snippets, configuration patterns, and operational guardrails — tailored to sovereign cloud environments.

Why this matters now (2026 context)

Two trends make this guidance urgent:

• Enterprise adoption of ClickHouse surged after its 2025 funding and broader market momentum — more teams run heavy OLAP workloads in cloud regions where data residency matters.
• Cloud providers expanded sovereign-region offerings in late 2025 and early 2026 (for example, AWS launched an independent European Sovereign Cloud). These regions meet strict residency and legal controls but can have constrained capacity and different pricing/instance types compared with global regions.

The net effect: you must optimize for both cost and operational resilience in an environment where spot capacity and features can differ from the public cloud baseline.

High-level strategy

Design your ClickHouse deployment across three levers:

1. Place non-critical compute on spot/interruptible instances while keeping coordination and metadata services on stable on-demand resources.
2. Introduce caching and storage tiers so hot queries hit fast, inexpensive compute and cold data moves to lower-cost object volumes.
3. Autoscale with cost awareness — use signals that reflect both load and spot market dynamics, and orchestrate graceful eviction and rebalancing.

Architecture patterns that work in sovereign clouds

1) Mixed-fleet ClickHouse cluster (recommended)

Split roles by node criticality:

• Coordinator / Keeper nodes (critical): small fleet of on-demand instances (or dedicated hardware) for ClickHouse Keeper/metadata and schema operations. These must be stable and ideally in a different fault domain than spot nodes.
• Compute/Storage nodes (worker): majority of shards/replicas on spot/interruptible instances. Use ephemeral local NVMe for hot parts and object storage for cold parts.
• Border services: routers, query proxies and caches (Redis/Varnish) on mixed or on-demand depending on SLA.

Why this matters: ClickHouse metadata coordination (Keeper) and replication convergence are sensitive to sustained uptime. Put those on stable instances. You get most of your CPU and IO capacity on cheap spot instances.

2) Tiered storage: hot, warm, cold

ClickHouse supports configurable disks and volumes so you can attach an S3-compatible object store for low-cost cold data while keeping hot partitions on NVMe.

# Example clickhouse-server config (shortened)

  
    
      
        /var/lib/clickhouse/fast/
      
      
        s3
        https://s3.eu-sovereign.example
        ...
      
    
    
      
        
          
            fast_disk
          
          
            object_disk
          
        
      
    
  

Operational tips:

• Use short TTLs for parts that can be recomputed (materialized views) so they age into cold storage.
• Monitor disk I/O and part churn. High churn on object disks can increase egress and per-request costs.

3) Edge caching layer for heavy read patterns

For dashboards and BI, add a read cache (Redis or HTTP cache) in front of ClickHouse. Cache aggregated results and parameterized query signatures. This reduces query load and helps keep working set on cheaper spot nodes.

• Use materialized views to pre-aggregate data for dashboard queries.
• Use TTL and versioning in cache keys to keep freshness predictable.

Spot and interruptible instances: practical operational controls

Spot instance savings are compelling — depending on provider and instance class you can see cost reductions of 40–90% for compute. But those savings require engineering for interruptions.

Protect the control plane

Always place Keeper/coordination nodes on stable instances (on-demand, reserved, or dedicated). For high availability:

• Deploy at least 3 Keeper nodes on different fault domains (availability zones).
• Pin them to on-demand or guaranteed capacity and monitor their availability separately from worker pools.

Make worker nodes fault-tolerant

Worker nodes should be designed to lose and reform quickly:

• Use a replication factor >=3 for ReplicatedMergeTree tables.
• Prefer smaller instance sizes with faster rebuilds rather than fewer large slow-to-recover nodes.
• Enable parallel replica recovery and tune max_replicas_to_try_remove and max_concurrent_queries cautiously to avoid overload during rebalancing.

Handle interruption notices

Many clouds provide a 30–120 second interruption notice. Use it:

• Run an agent (node-termination-handler) that marks the node unschedulable and triggers graceful draining of queries.
• Evict or redirect new queries to replicas, and let running queries finish or be checkpointed if possible.

# Pseudocode: On interruption notice
onInterrupt() {
  kubectl cordon 
  kubectl drain  --ignore-daemonsets --delete-emptydir-data --grace-period=60
  notify_clickhouse_to_stop_writes()
}

Cost-aware autoscaling: tie scale to cost signals, not just CPU

Autoscale based solely on CPU or memory and you’ll miss IO pressure, cold-part retrieval, and spot market volatility. Build autoscalers using a mix of signals:

• ClickHouse internal metrics: ActiveQueries, QueryDuration, PartsCount, BackgroundPoolTaskCount.
• Storage signals: disk usage of hot volumes and object read rates.
• Cost/market signals: current spot price, spot capacity health, and preemption rate.
• Business SLAs: query tail latency targets for BI vs. ad hoc queries.

Implementing a cost-aware autoscaler

Use a combination of Cluster Autoscaler (or provider autoscaling) and a custom controller that balances cost and availability.

1. Set a base guaranteed capacity of on-demand instances that can handle steady-state queries and metadata operations.
2. Allow spot node pools to scale up for burst capacity. Tune the autoscaler to prefer spot nodes when spot price delta is favorable and fall back to on-demand if spot preemption rises.
3. Feed spot market metrics and ClickHouse metrics into the decision engine (Prometheus + Alertmanager rules or KEDA scaledObject tied to custom metrics).

# Example: Prometheus alert rule to scale up spot pool when queries spike
- alert: ClickHouseBurst
  expr: (sum(clickhouse_active_queries) by (cluster) > 100) and (sum(node_spot_preemptions) by (zone) < 0.05)
  for: 30s
  labels:
    action: scale_spot_up

When autoscaling down, use conservative and staged drains to avoid heavy re-replication and egress costs.

Cost control guardrails and billing visibility

Visibility is critical in sovereign regions where billing and features can differ. Implement these guardrails:

• Tag all resources by environment, team, and cluster — enforce tag-based budgets in the billing console.
• Export cloud billing to an analysis pipeline stored in the same sovereign region to avoid data export compliance risks.
• Monitor egress and S3 request rates — tiered storage can reduce compute cost but increase object requests if not batched.

Security and compliance considerations in sovereign clouds

Sovereign clouds add legal and technical controls. Keep these in mind:

• Data locality: Ensure object storage, backups and audit logs remain in the sovereign region.
• Third-party agents: Some cloud node agents or spot handlers may call external endpoints. Validate software and host it within the sovereign tenant when necessary.
• Access controls: Use least-privilege IAM roles for autoscaling agents and for ClickHouse nodes accessing object storage.
• Auditability: Maintain immutable logs of scaling events and KEeeper changes for compliance audits.

Tip: For regulated workloads run periodic intrusion and configuration scans inside the sovereign tenancy to prove compliance without exporting data.

Operational playbook — step-by-step

Follow this checklist on any ClickHouse deployment into a sovereign region:

1. Inventory the available instance types in the sovereign region and validate spot/interruptible availability.
2. Design a mixed-fleet cluster: N=3 Keeper on on-demand, workers primarily spot with replication factor 3.
3. Configure tiered storage with an S3-compatible object disk inside the sovereign region.
4. Deploy node termination handlers and a custom autoscaler that consumes both ClickHouse and spot market metrics.
5. Implement an edge cache and materialized views for dashboard queries.
6. Set conservative downscale cooldowns and staged drains to avoid a rebuild storm and egress spikes.
7. Enable billing export and tag enforcement for departmental cost accountability.

Example: Hypothetical case study (EU sovereign region)

Scenario: An analytics team runs ClickHouse for e-commerce dashboards in a newly available EU sovereign cloud. Base load needs 8 vCPU-equivalent workers, peak needs spike to 32.

Baseline (all on-demand): 32 vCPU on-demand = $X/day. Mixed-fleet approach:

• Keeper: 3 on-demand small instances — guaranteed.
• Base workers: 4 on-demand to cover steady-state queries.
• Spot workers: up to 28 spot instances for peaks.

Results after two months of tuning:

• Average compute cost dropped ~65% compared to all on-demand baseline (savings vary by spot discounts in the region).
• Query P95 tail latency stayed within SLA by using caches and materialized views for top queries.
• Operational incidents due to preemption dropped to near-zero after implementing graceful drain and staged re-replication policies.

Lesson: Achieving this requires upfront effort — capacity planning, replication tuning, and careful autoscaler design — but the TCO gains are real.

Advanced strategies and future-proofing (2026+)

• Cost-aware query routing: Route non-latency-sensitive analytical jobs to spot-only clusters and reserve on-demand clusters for interactive BI.
• Serverless-ish ingestion: Use short-lived, event-driven workers (FaaS or spot VMs) to pre-process and write into ClickHouse. This keeps steady-state capacity low.
• Predictive autoscaling: Leverage historical query patterns with ML to pre-provision spot capacity ahead of expected peaks while respecting spot market dynamics.
• Hybrid sovereign design: Keep a minimal global control plane for tooling and observability while ensuring data paths and storage remain in the sovereign tenancy to satisfy regulators.

Common pitfalls and how to avoid them

• Under-provisioning Keeper: If you run Keeper on spot, you risk cluster-wide flakiness. Always choose stable nodes for coordination.
• Over-eager downscale: Aggressive scaling down can trigger a rebuild storm and high egress. Use staged drains, graceful eviction, and keep a small buffer of spare on-demand resources.
• Ignoring object request costs: S3-like storage reduces storage TCO but can increase per-request costs. Batch reads/writes and measure request rates in tests.
• Tool sprawl: Don’t adopt more autoscaling tools than you operate. Standardize on a minimal set (autoscaler + termination handler + Prometheus + orchestration) and document runbooks.

Quick reference: Recommended defaults

• Replication factor: start with 3 replicas for production tables.
• Keeper nodes: 3 on-demand instances, AZ-spread.
• Spot vs on-demand ratio: start 70/30 (adjust after measuring preemption in sovereign region).
• Autoscale cooldown: scale-in cooldown >= 10 minutes and staged node termination (evict 10–20% at a time).
• Storage policy: hot on NVMe (local), cold on sovereign S3-compatible object store.

Actionable checklist — next 30 days

1. Audit instance availability and spot pricing in the target sovereign region.
2. Deploy a small mixed-fleet ClickHouse proof-of-concept with Keeper on on-demand and workers on spot.
3. Set up node termination handling and a basic Prometheus + Alertmanager pipeline with ClickHouse exporters.
4. Implement tiered storage for a subset of tables and run traffic-replay tests to measure costs and latency.
5. Roll out cost-aware autoscaling with conservative thresholds and observe for two weeks before aggressive tuning.

Final takeaways

In 2026, running ClickHouse in sovereign clouds is increasingly common and practical — but naive lift-and-shift will cost you. The optimal approach is pragmatic: protect the control plane with on-demand capacity, push bulk compute to spot or interruptible instances, and reduce active working set by using caching and tiered storage. Combine these with a cost-aware autoscaler that respects spot market signals and storage egress patterns, and you’ll cut ClickHouse TCO dramatically while preserving compliance.

Call to action

Ready to optimize your ClickHouse TCO in a sovereign region? Start with a focused proof-of-concept: deploy a 3-Keeper on-demand + spot worker cluster with tiered storage and node termination handling. If you want a template, code snippets, or a 2-week assessment plan tailored to your provider and region, request the hosted playbook and scripts I use with teams migrating analytics to sovereign clouds.

Related Reading

• Sensitive-Topic Video Templates That Stay Fully Monetized on YouTube
• Streaming Strategies, Local Screenings: How New Content Deals Create More Community Events
• Boutique Tech: Lighting, Sound and Automation That Elevate a Jewelry Studio
• JioStar’s Record Quarter: What Streaming Growth in India Means for Global Media Investors
• Fail‑Safe Patching: Avoiding the 'Fail To Shut Down' Windows Update Pitfall