Cost-Optimizing ClickHouse: Best Practices for Cloud Deployments

Cut ClickHouse Cloud Bills Without Sacrificing Performance: A 2026 Playbook

Hook: If your ClickHouse cluster is the biggest line item in the cloud bill, you’re not alone. Engineering teams in 2026 face ballooning compute and storage costs, fragmented observability, and pressure to deliver sub-second analytics for increasingly large datasets. This guide gives practical, battle-tested tactics to reduce both compute and storage spend on ClickHouse while keeping query SLAs intact.

Executive summary (most important first)

To control ClickHouse costs at scale, combine four levers: right-size instances, use tiered storage and TTL migrations, optimize storage with aggressive compression, and adopt query + autoscaling strategies that align resource usage with demand. Pair those with billing-aware observability and spot/interruptible compute to capture 30–70% savings depending on workload profiles.

Why this matters in 2026

ClickHouse has become a dominant OLAP choice for high-cardinality analytics and real-time pipelines. Late 2025 saw ClickHouse raise large funding and enterprise adoption accelerate — which means more mission-critical deployments running in cloud environments where costs are visible and sometimes painful.

At the same time, cloud providers introduced higher-performance NVMe tiers, deeper S3 lifecycle controls, and more mature spot markets and managed analytics services. This makes 2026 an ideal time to revisit architecture: the cloud gives you options to relocate data and compute across tiers and instance types — if you design for it.

Core cost levers (overview)

• Instance sizing: match CPU, memory, and network to the query profile.
• Tiered storage & TTL: treat hot, warm and cold data differently; automate movement to S3 or blob.
• Compression: choose codecs and column types that maximize bytes saved and maintain query speed.
• Query optimization: reduce scanned bytes with partitions, projections, and materialized views.
• Autoscaling & spot strategies: use horizontal scaling, pre-warming, and spot/interruptible instances with replica-aware deployments.
• Billing-aware monitoring: map ClickHouse metrics to cost signals and enforce budgets.

1. Instance sizing: More than CPU counts

Right-sizing ClickHouse is not just picking the biggest VM. It’s matching the architecture to the workload profile:

• CPU-bound analytic workloads (heavy aggregations, vectorized functions): prefer higher vCPU and compute-optimized instances. Use machines with predictable single-threaded performance for fast merges and mergesort operations.
• I/O-bound workloads (large scans, frequent merges): prioritize NVMe SSDs or high-throughput network-attached storage. Choose instances with high network bandwidth for S3-backed storage policies.
• Memory-sensitive workloads (large hash joins, large mark_cache or dictionary_combo): increase RAM and tune max_memory_usage to avoid spills that drive extra I/O.

Practical steps

1. Profile queries with system.query_log and system.metrics for 2–4 weeks to classify by CPU/IO/memory.
2. Start with a conservative ratio: 1 vCPU per 8–16 GB RAM for mixed workloads; adjust after measuring swap/iowait.
3. Prefer instances with local NVMe for primary storage for hot partitions; offload older partitions to object storage.
4. Use dedicated NICs or enhanced networking where network bandwidth limits distributed merges or replicated writes.

2. Storage tiers and TTL: design a hot-warm-cold lifecycle

ClickHouse supports storage policies composed of multiple disks. Implement a hot-warm-cold layout where recent, frequently queried data remains on NVMe, warm data lives on cheaper network block storage, and cold data moves to object storage (S3/Blob). For detailed approaches to long-term storage and lifecycle cost tradeoffs, see our storage cost optimization guide.

How to implement

• Define disks in users.xml/config.xml that map to local NVMe, EBS/GCE PD, and S3-compatible disks.
• Create a storage policy with volumes ordered by performance. Example policy: hot (nvme) → warm (gp3/io2) → cold (S3).
• Use TTL migrations in CREATE TABLE to MOVE TO DISK after a retention window:

<!-- Example TTL in DDL -->
CREATE TABLE events (
  event_date Date,
  user_id UInt64,
  payload String
) ENGINE = MergeTree()
PARTITION BY toYYYYMM(event_date)
ORDER BY (user_id, event_date)
TTL event_date + INTERVAL 30 DAY TO DISK 'warm',
    event_date + INTERVAL 180 DAY TO DISK 'cold'

Move-to-disk TTLs save both operational cost and query cost if you ensure queries target the right partitions.

Cold storage strategies (2026 context)

In 2026, object stores offer lower egress tiers and intelligent-tiering options. Use lifecycle policies and ClickHouse's S3 disk to keep rarely accessed data in the cheapest class (e.g., S3 Glacier Deep Archive), but be mindful of retrieval latencies — suitable only for true archival queries.

3. Compression: the single-biggest storage multiplier

Compression reduces storage and I/O — often the easiest win. ClickHouse supports multiple codecs (LZ4, ZSTD, Delta, DoubleDelta, Gorilla). Choose codecs by column type and expected read patterns.

Rules of thumb

• Use ZSTD for large string or JSON-like columns where higher compression ratio matters; tune level (e.g., zstd:5–9) for a balance of CPU and space.
• Use LZ4 for columns with fast decode needs (low-latency queries) — lower CPU but larger storage.
• Use Delta/DoubleDelta for numeric time-series columns — excellent for monotonically increasing values.
• Use Gorilla for floating-point time-series to save space while maintaining good decompress speed.

DDL compression example

CREATE TABLE metrics (
  ts DateTime,
  metric_id UInt32 CODEC(Delta, ZSTD(4)),
  value Float64 CODEC(Gorilla)
) ENGINE = MergeTree()
ORDER BY (metric_id, ts)

Measure before and after: run sampling queries to compute total uncompressed size vs compressed bytes. For many analytics tables, switching to optimized codecs yields a 2–6x reduction in storage.

4. Query optimization: scan less, compute smarter

Compute cost scales with scanned bytes and CPU cycles. Use query patterns and table design to minimize both.

Techniques

• Partitioning: partition by time or high-cardinality shard key to prune reads. For time-series, use monthly partitions for low-cardinality event_date.
• ORDER BY and primary key: design ORDER BY to support range queries and avoid full table scans.
• Projections: use built-in projections (materialized pre-aggregations) for heavy aggregations to reduce compute on hot reads.
• Materialized views: create materialized views for common rollups and retention-sensitive aggregates.
• Limit columns scanned: select explicit columns instead of SELECT *; use low-cardinality types for dimensions.
• Sampling: for exploratory analytics, use SAMPLE for approximate answers and much lower resource usage.

Example: reduce scanned bytes

Given a 20 TB table where queries only need two columns, selecting those columns and using an appropriate projection can reduce I/O by >90% and cut CPU proportionally.

5. Autoscaling strategies: align cost with demand

Autoscaling ClickHouse is trickier than stateless services because of data locality, merges and replica consistency. But in 2026, Kubernetes operators and cloud-native patterns make safe autoscaling feasible. For orchestration and automation patterns that drive safe scale-up/scale-down cycles, see approaches for automating cloud workflows and autoscalers.

Approaches

• Horizontal scale for read traffic: scale read replicas (or distributed query routers) to handle spikes. Use DNS/load balancer with latency-aware routing.
• Scale workers for ad-hoc analytics: run ephemeral read-only ClickHouse clusters for large ETL/BI jobs that can be torn down after job completion.
• Use spot instances: run non-critical replicas or merge workers on spot/interruptible instances to capture 50–80% compute savings. Ensure replication and recovery paths are automated.
• Kubernetes + ClickHouse Operator: use operators to manage shard/replica lifecycle and implement custom autoscalers driven by queue length, pending merges, or query concurrency. Many teams pair the operator with an advanced ops playbook to manage lifecycle automation safely.

Autoscaling pattern example

1. Monitor active queries and query queue depth via system.metrics.
2. Scale read replicas up when 95th percentile latency exceeds target for 3 consecutive minutes.
3. Scale down when sustained low utilization (e.g., < 30% CPU) and no long-lived merges are pending.

Pre-warm nodes to copy hot partitions or synchronize replicas before flapping to avoid latency spikes. In many deployments, a small base of steady-state nodes plus a variable pool of read-only workers works best.

6. Spot/interruptible compute and recovery patterns

Using spot instances is one of the most impactful cost levers if you build for eviction. Key patterns:

• Keep critical replicas on on-demand VMs; use spot VMs for additional replicas and background merge workers.
• Implement graceful shutdown scripts for spot termination notices to flush in-flight writes or redirect traffic.
• Automate rebalancing and re-replication with the ClickHouse Operator; ensure rebalance is rate-limited to avoid I/O storms. If you need guidance on incident playbooks and recovery procedures for provider evictions or outages, look at public-sector and incident-response guidance for large cloud providers (incident-response playbooks).

7. Billing-aware observability: make cost visible

Technical optimizations without cost visibility produce marginal wins. Map ClickHouse metrics to currency using these tactics:

• Tag resources consistently (cluster, environment, team) and export cloud billing data into a data warehouse for chargebacks.
• Create dashboards that combine cloud billing (AWS/Azure/GCP) with ClickHouse metrics — e.g., cost per TB scanned, cost per query, cost per user action. For deeper observability approaches, see guidance on embedding observability into serverless analytics (observability patterns).
• Use query-level tracing (system.query_log) to compute the top 5% of costly queries by resource usage and then optimize or route them.

Cost metrics to track

• Bytes scanned per query and cost-per-GB-scan
• Storage $/GB/month by tier
• CPU $/vCPU-hour by instance family
• Spot eviction rate and recovery cost

8. Policies, governance and guardrails

Put policies in place to prevent runaway cost growth:

• Enforce query limits (e.g., max_concurrent_queries, max_memory_usage_for_user).
• Require cost reviews for long-retention tables or high-cardinality partitions.
• Automate table TTLs and archival; block SELECT * on large tables in production without review.

9. Real-world example: 3 cost-saving plays

Here are concrete changes a SaaS analytics team applied to a 50 TB ClickHouse deployment in late 2025–early 2026:

1. Move cold data to S3 object storage: configured a three-disk storage policy and TTLs to move data >90 days to S3 — immediate 40% reduction in EBS spend. (See storage cost strategy notes in our storage cost optimization guide.)
2. Codec tuning: switched long string fields to ZSTD(6) and numeric series to Delta plus Gorilla — achieved 3x compression and reduced network egress for cross-shard queries.
3. Spot workers for merges: offloaded background merges and compaction to spot instances during off-peak hours; cut compute spend for non-critical work by 65%.

Combined, these moves reduced monthly cloud spend by roughly half while improving median query latency through projections and partition pruning.

10. Predictions & trends for 2026–2027

• Managed ClickHouse and serverless OLAP will grow. Expect more managed offerings with built-in tiering and autoscaling — good for teams who prefer ops-lite models.
• Hardware-accelerated analytics: tighter GPU and smart NIC integration (driven by NVLink-like interconnects) will make offload for heavy vector operations cheaper — but only when your workload benefits from it.
• More sophisticated cost-aware query planners: vendors will add cost-estimation hooks to push down expensive operations or rewrite queries for cheaper alternatives.

11. Quick checklist: Implement today

• Run a 2–4 week profile of queries and storage usage.
• Define a storage policy: hot (NVMe) → warm (block) → cold (S3) and add TTL migrations.
• Tune DDL compression on a per-column basis; measure compression ratios.
• Introduce projections/materialized views for top N heavy aggregations.
• Enable spot instances for non-critical replicas; automate graceful termination handling.
• Surface cost-per-query in dashboards and set alerts for anomalies. Consider automating parts of your cloud workflow using prompt-driven chains or orchestration patterns (automation chains).

12. Advanced tuning tips

• Tune background_merge_threads and max_bytes_to_merge_at_min_space_in_pool to balance CPU and disk bandwidth during peak loads.
• Use low_cardinality() types for string dictionary compression results to reduce both memory and on-disk footprint.
• Disable unneeded system tables replication and snapshots that generate extra I/O and storage costs.
• For distributed JOINs, consider using local pre-aggregations to avoid cross-node reshuffles.

Final takeaways

Controlling ClickHouse cloud spend is a multi-dimensional effort: pick the right instance types, tier your storage with TTL-driven migrations, squeeze more out of compression, minimize scanned bytes with table and query design, and adopt smart autoscaling and spot strategies. In 2026, cloud and ClickHouse innovations make it straightforward to separate hot compute from cold storage — your job is to codify lifecycle policies, instrument cost signals, and automate reaction paths. For broader incident and SLA reconciliation patterns, consult guidance on reconciling SLAs and outages.

Actionable outcome: Implement the checklist above across a single critical table and measure cost impact for 30 days. Most teams see measurable savings within a billing cycle.

Call to action

If you want a custom cost-optimization plan for your ClickHouse deployment, we can run a targeted 30-day audit: query profiling, storage-policy design, and an autoscaling blueprint tied to your cloud provider. Contact our engineering team to get a free assessment and a projected savings report tailored to your workloads.

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