How to model cost for large-scale analytics: ClickHouse use cases and pricing patterns

Cut analytics cost uncertainty: model ClickHouse storage vs compute in 2026

Hook: If you run large-scale analytics and your cloud bill varies month-to-month, you’re not alone. Teams building event pipelines, observability backends, and ad-tech analytics face a hard trade-off: store everything and pay storage + egress, or compute on demand and pay CPU (and slower SLAs). This guide gives practical methodologies to forecast and control analytics costs with ClickHouse — including storage vs compute trade-offs, compression and retention levers, and query optimization tactics that materially lower TCO.

Why ClickHouse matters for cost-conscious analytics in 2026

ClickHouse’s momentum continued into late 2025 and early 2026 (including a major funding round), and enterprises increasingly pick it for high-throughput, low-latency OLAP. That growth means more pressure to control spend: columnar compression reduces storage needs, but aggressive retention and query patterns can push compute costs way up. Understanding how ClickHouse stores and queries data is the first step to modeling total cost of ownership (TCO). For recent market context see the OrionCloud IPO brief and vendor movement.

Recent trends you should factor in

• Managed ClickHouse offerings and ClickHouse Cloud adoption have accelerated — shifting cost from ops labor to vendor bills.
• Lower-cost object storage (S3, Glacier tiers) and better native tiering in ClickHouse let you put cold data on cheaper media without rewriting pipelines.
• Compression and vectorized execution continue to improve CPU efficiency — but high-cardinality queries or wide joins still dominate compute spend.

Fundamental cost model: break TCO into simple parts

Modeling is easier when you split costs into stable components. For ClickHouse analytics, use this baseline decomposition:

• Storage cost — cost to keep raw and derived data (hot and cold tiers) for your retention windows.
• Compute cost — cost of CPU, memory, and I/O used by queries, ingestion, merges, and replication.
• Operational cost — admin time, backups, monitoring, and incident resolution (often hidden but material). See thinking on the hidden costs of 'free' hosting when you compare vendor propositions.
• Network & egress — data transfer for ingestion, external exports, and cross-region replication.

High-level TCO formula

At a modeling level:

TCO_month = Storage_month + Compute_month + Ops_month + Network_month

Where:

• Storage_month = sum(volumes: bytes_stored * price_per_GB_month)
• Compute_month = sum(nodes: vCPU_hours * price_per_vCPU_hour) + cost_of_spikes

Step-by-step methodology to forecast costs

Below is a repeatable approach teams can adopt. We include sample calculations so you can adapt numbers to your environment.

1) Measure your raw inputs

Collect real data for:

• Events per day (rows/day)
• Average raw row size (bytes)
• Query volume and concurrency (queries/day, avg and p95 concurrency)
• CPU seconds and memory per query (use system.query_log + query_profiler)
• Retention windows for raw and aggregated data

Use these ClickHouse queries to get baseline metrics:

SELECT
  sum(rows) AS total_rows,
  sum(bytes) AS total_bytes
FROM system.parts
WHERE active = 1;

SELECT
  query,
  round(sum(ProfileEvents['QueryProfileEventsCpuTimeMicroseconds'])/1000000,3) AS cpu_seconds,
  count() AS runs
FROM system.query_log
WHERE event_time >= now() - INTERVAL 7 DAY
GROUP BY query
ORDER BY cpu_seconds DESC
LIMIT 50;

2) Estimate storage with compression

ClickHouse is columnar and compresses per column. Your effective compression ratio will vary by data type and cardinality. For typical event schemas:

• Numeric telemetry: 8–15x compression
• High-cardinality strings (IDs): 2–4x
• Mixed event with JSON-like payloads: 3–8x

Sample calculation (example):

• 100M events/day
• avg raw row = 200 bytes → 20,000,000,000 bytes ≈ 18.6 GB/day
• assume overall compression = 8x → compressed = 2.325 GB/day
• retention raw hot = 30 days → 69.8 GB; cold retention 12 months → move older to S3 cheaper tier

Storage cost (S3 standard at $0.023/GB-month; adjust for your pricing):

Hot_storage_month = 69.8 GB * $0.10/GB-month (fast disk) ≈ $6.98
Cold_storage_month = (2.325 * 365 - 69.8) GB * $0.023 ≈ $17.4
Total_storage ≈ $24.4/month

Note: On self-managed clusters, include replication overhead (replicated tables double or triple storage depending on replica count).

3) Model compute cost per query and per day

Rather than guessing, use profiler and system metrics to compute average CPU seconds per query. Multiply by queries/day and convert to instance hours.

Compute_seconds_per_day = sum(all_query_cpu_seconds_last_24h)
vCPU_hours_needed = Compute_seconds_per_day / 3600
Compute_cost = vCPU_hours_needed * price_per_vCPU_hour

Example:

• Aggregate queries/day consume 50,000 CPU-seconds
• vCPU-hour price = $0.04 → compute_cost ≈ (50,000 / 3600) * 0.04 ≈ $0.56/day ≈ $16.8/month

Remember: spikes matter. Provision for p95 concurrency or use autoscaling + throttling to avoid runaway bills.

Control levers: trade-offs that move the needle

After measuring, apply levers and re-run the model to see impact. Below are the highest ROI controls.

1) Compression and codecs

ClickHouse supports codecs (LZ4, ZSTD, Brotli, etc.) at column level. Use heavier codecs for cold columns where CPU during reads is less frequent.

CREATE TABLE events (
  ts DateTime,
  user_id UInt64 CODEC(ZSTD(3)),
  payload String CODEC(ZSTD(5)),
  value Float64
) ENGINE = MergeTree()
PARTITION BY toYYYYMM(ts)
ORDER BY (user_id, ts)
SETTINGS index_granularity = 8192;

Guidelines:

• Use LZ4 for low-latency hot queries; ZSTD for better density on cold data.
• Apply codecs to high-cardinality strings and JSON payloads; numeric columns often compress well by default.
• Benchmark: measure read CPU vs storage delta — a 2x storage savings is often worth a modest CPU increase.

2) Retention policies and tiering

Retention is where teams get the biggest savings. ClickHouse supports TTLs that can MOVE TO VOLUME (cheaper disks or object storage) or DELETE automatically.

ALTER TABLE events
MODIFY COLUMN ts DateTime
TTL ts + INTERVAL 90 DAY
MOVE TO VOLUME 'cold' SETTINGS storage_policy='hot-cold'
DELETE WHERE ts < now() - INTERVAL 365 DAY;

Strategy:

• Keep hot, frequently queried days (7–30 days) on NVMe or fast SSDs.
• Move older data to object storage or cheaper volumes with higher compression.
• Summarize older data into rollups (daily/hourly) and then delete raw events to reduce both storage and compute for long-range queries.

3) Pre-aggregation vs compute-on-read

Pre-aggregating (materialized views, AggregatingMergeTree, or projections) trades storage for query-time CPU. Use the break-even formula to decide:

Cost_preagg = Storage_preagg_month + Reduced_compute_month
Cost_no_preagg = Storage_raw_month + Compute_queries_month
Choose pre-agg if Cost_preagg < Cost_no_preagg over your evaluation horizon

Example: If a rollup adds 25% storage but reduces query CPU by 60% for heavy dashboards, pre-aggregation usually wins. See a practical case study of instrumentation and guardrails that cut query spend.

CREATE MATERIALIZED VIEW mv_daily
ENGINE = AggregatingMergeTree()
PARTITION BY toYYYYMM(day)
ORDER BY (metric, day)
AS SELECT
  toDate(ts) AS day,
  metric,
  sumState(value) AS sum_v
FROM events
GROUP BY day, metric;

4) Partitioning and primary key design

Good partitioning reduces the amount of data scanned and lowers CPU. Common patterns:

• Partition by time (day, month) for append-only event streams.
• Order by fields used in WHERE clauses to leverage primary key skipping indices.
• Keep index_granularity balanced — higher granularity = fewer marks and faster reads but more memory for mark cache.

5) Query-level optimizations

Small query changes can reduce CPU dramatically:

• Use PREWHERE for filtering on indexed columns to avoid unnecessary data decompression.
• Avoid wide SELECT * scans; project only required columns.
• Replace large IN lists with temporary joins or dictionary lookups.
• Use sampling and approximate functions (approxCountDistinct) for dashboards where exactness is not required.

SELECT
  metric,
  avg(value)
FROM events
PREWHERE ts >= yesterday()
WHERE user_id = 1234
GROUP BY metric;

6) Resource governance and autoscaling

Control concurrency and cap runaway queries:

• Use user profiles, quotas, and settings (max_memory_usage, max_threads) to limit per-query resources.
• Autoscale query nodes for predictable load and use pre-warming for scheduled heavy jobs — consider serverless patterns when modeling transient compute.
• Implement query timeouts and cost-based routing — route expensive reports to analytic clusters or scheduled jobs.

Putting it together: a worked example

Scenario: You run an ad-tech pipeline with 200M events/day, avg row 150 bytes, retention 365 days, typical dashboard queries heavy between 00:00–06:00 UTC.

1. Raw daily bytes = 200M * 150 ≈ 30 GB/day
2. Assume compression 6x → 5 GB/day; 365-day retention → ~1.8 TB compressed
3. Store hot 30 days on SSD: 150 GB; rest 1.65 TB on S3 or cold volumes

Storage cost estimate:

Hot: 150 GB * $0.10/GB-month = $15/month
Cold: 1650 GB * $0.023 = $37.95/month
Total_storage ≈ $53/month (plus replication factor)

Compute estimate (measured): queries/day consume 200,000 CPU-seconds. vCPU_price = $0.04/hr

vCPU_hours = 200,000 / 3600 ≈ 55.6 hrs/day → 1,667 vCPU-hrs/month
Compute_cost ≈ 1,667 * $0.04 ≈ $66.7/month

Ops overhead: add $3–6k/month to run self-managed cluster (admins, backups). If you adopt managed ClickHouse Cloud, this shifts to the vendor but increases monthly bill; compare 12–24 month TCO.

Now apply levers:

• Enable rollups for dashboards (adds 20% storage = +360 GB) but reduces CPU by 50% → compute cost drops to ~$33/month. Net monthly delta: +$8 storage - $33 compute = -$25 (savings).
• Move 90% of older data to S3 Deep Archive and apply ZSTD(7) to payloads; compression increases to 8x → storage down by ~10%.

Operational checklist: implement and validate

Follow this to turn model into reality:

1. Inventory: capture schema, events/day, query profiles (7/30/90 day windows).
2. Baseline: run queries to measure CPU seconds and storage per table.
3. Hypothesize: choose compression, TTLs, and pre-agg thresholds.
4. Test in staging: enable codec changes, TTL moves, and materialized views; measure query latency and reconstruction costs.
5. Roll out with metrics: track cost, query latency, and stakeholder satisfaction.

Advanced strategies and future-proofing (2026+)

As of 2026, expect these to be important:

• Serverless analytics patterns: transient compute for ad-hoc analytics reduces baseline compute but increases per-query costs. Model both profiles. See a primer on serverless edge patterns for architecture examples.
• Cross-cluster tiering: move cold data to dedicated analytics clusters in cheaper regions; factor egress and replication latency. Consider region-aware controls like those discussed for European sovereign cloud designs when you decide data placement.
• Hybrid pricing: managed ClickHouse often bundles infra + software; compare by isolating infrastructure cost from value-add features like autoscaling and SLA guarantees.

Common pitfalls and how to avoid them

• Ignoring replication overhead: Replicated MergeTrees multiply storage. Always multiply storage by replication factor when modeling.
• Estimating without profiling: Don’t guess CPU; use system.query_log and ProfileEvents.
• Underestimating spikes: Heavy batch jobs can dominate monthly compute hour usage. Model p95 workloads and consider scheduled windows for heavy jobs.
• Over-compressing hot data: Aggressive codecs increase query latency. Balance compression vs SLA.

Practical scripts and monitoring sources

Key system tables and metrics to build automated models:

• system.parts — for data sizes and active parts
• system.metric and system.events — merge queue, memory pressure, disk usage
• system.query_log and system.processes — CPU per query, duration, and concurrency

Set up dashboards that show:

• Compressed storage by table and by volume
• Daily CPU seconds and vCPU-hour equivalents
• Cost per dashboard or job (assign queries to owners)

Rule of thumb: if a single report consumes >10% of daily CPU seconds, schedule it or pre-aggregate it.

Decision matrix: when to choose storage vs compute

Use this simplified decision tree:

• If queries are repeated and predictable → prefer pre-aggregation.
• If queries are ad-hoc and low-volume → prefer compute-on-read with efficient partitioning.
• If object storage is cheap and query SLAs allow latency → use tiering and move cold data away from hot nodes.
• If your team spends heavily on ops → evaluate managed services and include labor in TCO.

Final checklist before you commit

• Have you measured CPU seconds via ProfileEvents for a representative period?
• Did you estimate replication multiplier for storage needs?
• Do you have an SLA-driven retention strategy (hot, warm, cold) implemented with TTLs?
• Have you tested codec changes on realistic queries?
• Is there governance to stop runaway queries and enforce quotas?

Summary: model, apply levers, iterate

In 2026, ClickHouse gives analytics teams powerful levers — columnar compression, TTL-based tiering, projections, and materialized views — to control TCO. The practical method is simple: measure baseline, model storage and compute separately, apply cost levers (compression, retention, pre-agg), and validate. Small changes in schema, partitioning, or query patterns often create outsized savings.

Call to action

Ready to reduce your analytics bill without sacrificing SLA? Start with a 30-day cost audit: export your system.query_log and system.parts, run the profiling queries in this guide, and project two scenarios (current and optimized). If you want, share the anonymized outputs and we’ll produce a tailored cost forecast and a prioritized optimization plan for your ClickHouse deployment. For tooling and docs that work well offline or in constrained environments, consider using offline-first document and diagram tools to capture baselines.