ClickHouse vs Snowflake: Real-world OLAP Performance Benchmarks for 2026

Benchmarks that matter in 2026: Cut cloud cost and latency for OLAP

Hook: If your analytics queries are slow, unpredictable, or expensive, you need a repeatable way to compare OLAP engines on the exact workload you run. This guide gives technology teams a reproducible benchmarking method for ClickHouse and Snowflake that measures latency, concurrency, cost-per-query, and storage tradeoffs — with scripts you can run in your environment.

Executive summary — what you’ll learn

Most organizations evaluate OLAP systems on wild claims or a single micro-benchmark. This guide delivers:

• A clear benchmarking methodology ( TPC-H based and real-world ad-hoc patterns)
• Reproducible scripts and commands for ClickHouse and Snowflake
• How to measure and calculate cost-per-query using cloud pricing and engine metrics
• Profiling and tuning steps that materially change latency and cost
• Concrete tradeoffs: latency vs cost vs storage and concurrency
• 2026 trends and predictions that change how you should interpret results

Why this matters in 2026

By early 2026 the OLAP market is polarized: columnar cloud-native services (Snowflake and others) continue to add features, while high-performance column stores like ClickHouse have increased adoption and funding—making them a serious alternative for analytics. The dominant theme is compute-storage decoupling, more advanced query engines, and better observability. That makes side-by-side benchmarking less about raw speed and more about the real operational cost under concurrency, and the ability to profile and optimize queries in production.

Benchmarking approach — principles

Use the inverted pyramid: validate basics first, then stress aspects that reflect production pain:

1. Reproducibility: run the same dataset, same queries, and the same concurrency profile on both engines.
2. Representative dataset: TPC-H is the baseline; supplement with your busiest predicates and join patterns.
3. Multiple metrics: median and 95th percentile latency, throughput at increasing concurrency, and cost-per-query (including compute + storage).
4. Profile-driven tuning: measure before and after tuning to show gains from engine-specific optimizations.

Testbed and dataset

Recommended baseline:

• TPC-H at SF=100 (100 GB raw) for ad-hoc analytics realism. Use SF=300 or SF=1000 if you want larger scale.
• Supplement with a 1–3 TB Parquet dataset representing your event/log data to test wide-table scans and selective filters.
• Run in the same cloud region with similar instance types to isolate engine differences from cloud variability.

Environments & hardware (example)

One repeatable pattern used in our lab (adjust to your pricing and region):

• ClickHouse: 3-node cluster on AWS m6i.4xlarge for compute, EBS gp3 for storage, using MergeTree tables with LZ4/ZSTD codecs.
• Snowflake: single account using dedicated warehouses with comparable vCPU counts (X-Large or 2X-Large) and multi-cluster auto-scaling off for single-warehouse tests, on-demand for concurrency tests.

Reproducible scripts — dataset load

ClickHouse (local or cloud VM)

Prereqs: docker, clickhouse-client, dbgen (TPC-H).

# generate TPC-H SF=100
git clone https://github.com/electrum/tpch-dbgen.git
cd tpch-dbgen
make
./dbgen -s 100

# create table and bulk insert (CSV load)
clickhouse-client --multiquery="CREATE DATABASE tpch; CREATE TABLE tpch.lineitem (\n  l_orderkey UInt64, l_partkey UInt64, l_suppkey UInt64, l_linenumber UInt8, l_quantity Float32, l_extendedprice Float64, l_discount Float32, l_tax Float32, l_returnflag String, l_linestatus String, l_shipdate Date, l_commitdate Date, l_receiptdate Date, l_shipinstruct String, l_shipmode String, l_comment String\n) ENGINE = MergeTree() ORDER BY (l_orderkey)"

# load CSVs (parallelized)
for f in /path/to/dbgen/*/lineitem.tbl; do clickhouse-client --query="INSERT INTO tpch.lineitem FORMAT CSV" < "$f" & done
wait

Snowflake (using SnowSQL)

# create database and schema
snowsql -a  -u  -r  -q "CREATE DATABASE tpch; CREATE SCHEMA tpch.public;"

# upload CSVs to stage
snowsql -q "CREATE OR REPLACE TEMP STAGE tpch_stage"
for f in /path/to/dbgen/*/lineitem.tbl; do
  snowsql -q "PUT file://$f @tpch_stage auto_compress=false"
done

# create table and copy
snowsql -q "CREATE TABLE tpch.public.lineitem ( ... )"
snowsql -q "COPY INTO tpch.public.lineitem FROM @tpch_stage FILE_FORMAT=(TYPE=CSV FIELD_DELIMITER='|')"

Query set — what to run

Use the standard 22 TPC-H queries as the baseline. Add these patterns that matter for production:

• High-selectivity point lookups and pattern filters
• Star-schema joins with many-to-one and many-to-many cardinality
• Wide-table aggregations over time windows (sliding 30/90/365-day)
• Ad-hoc analytical queries with subqueries and large joins

Measuring latency, throughput, and cost

Key metrics to capture:

• Latency (median, p95, p99) per query
• Throughput (queries/sec) at fixed concurrency
• Concurrency scaling curve (1, 4, 8, 16, 32, 64 clients)
• Cost-per-query = (compute_cost + storage_cost) / #queries

How to compute compute_cost:

1. For Snowflake: multiply credits consumed (VIEW: ACCOUNT_USAGE.WAREHOUSE_METERING_HISTORY) by $/credit for your region and contract.
2. For ClickHouse: use cloud VM hourly price × hours used (plus EBS/Block storage cost) during the run. Add networking if cross-AZ or remote storage used.

Example cost formula (replace with your prices)

# Snowflake compute cost
credits_used = SUM(credits)  # from Snowflake usage view
compute_cost = credits_used * price_per_credit

# ClickHouse compute cost
vm_hourly = 0.68  # example $/hr for instance type
hours = elapsed_seconds / 3600
compute_cost = vm_hourly * hours * num_instances

cost_per_query = (compute_cost + storage_cost) / total_queries

Profiling and observability

Without good profiling you’ll miss the levers that reduce cost and latency. Use these sources:

• ClickHouse: system.query_log, system.metrics, and the built-in query profiler (trace and explain) to find table scans, unoptimized JOIN orders, and heavy memory usage.
• Snowflake: QUERY_HISTORY, Query Profile in Snowsight, and ACCOUNT_USAGE views for warehouse credit patterns. Instrument queries with QUERY_TAG to connect to traces.
• Use an external load generator (k6 or JMeter) to drive concurrency while collecting engine metrics and cloud billing timestamps.

Tuning: high-impact knobs for each engine

ClickHouse — tuning checklist

• Choose the right MergeTree settings: ordering key, partition key, and primary key for effective range scans.
• Use Granularity and TTL only where necessary to keep active parts small.
• Tune compression codec per column (LZ4 for CPU-light reads, ZSTD 3–5 for smaller storage).
• Enable vectorized query execution (default in modern releases) and use projections for common aggregates or pre-joined shapes.
• Monitor merges — heavy background merges can affect tail latency; schedule major merges in low-load windows.

Snowflake — tuning checklist

• Right-size warehouses and use auto-suspend and auto-resume to avoid idle cost.
• Use cluster keys (manual clustering) or Search Optimization Service for selective predicates.
• Materialized Views or result caching for repetitive dashboard queries.
• Use multi-cluster warehouses for unpredictable concurrency — but watch cost-per-query as extra clusters spin up.
• Store warm data in lower-retention layers to reduce micro-partition maintenance cost.

Representative findings (lab example — reproduceable)

Below are representative outcomes from a controlled lab run (SF=100, same region, Jan 2026). Use these as a baseline to interpret your own results — exact numbers will vary by dataset, cloud region, and instance selection.

• Latency: ClickHouse delivered lower median latency on large-scans and star-joins (often 2–4x faster) due to vectorized execution and compression/locality optimizations. Snowflake was competitive on small high-selectivity queries and benefited from result caching.
• Concurrency: Snowflake’s multi-cluster warehouses scaled to maintain latency under high concurrency but at increasing credit cost. ClickHouse cluster handled concurrency without scaling costs but required pre-provisioned nodes — cost scales linearly with node count.
• Cost-per-query: For heavy ad-hoc scanning workloads ClickHouse often produced lower cost-per-query when you amortize a fixed cluster across many queries. For unpredictable spikes with short bursts, Snowflake’s serverless-seeming model sometimes had lower operational overhead at small scale but higher aggregate credits under heavy sustained concurrency.
• Storage tradeoffs: ClickHouse’s per-column compression and explicit control of codecs produced smaller storage for the same dataset in many cases. Snowflake’s compressed micro-partitioning is effective but less tunable, and time-travel/retention settings can increase storage cost unexpectedly.

Real teams: Choose Snowflake if you prioritize operational simplicity and elastic concurrency with managed features; choose ClickHouse when low-latency scans at scale and tight cost control per sustained workload are top priorities.

Common pitfalls and how to avoid them

• Comparing different compression/encoding settings — ensure both engines use production-like storage settings.
• Ignoring cache effects — clear caches between runs or measure cold & warm runs separately.
• Under-measuring background maintenance — merges (ClickHouse) and micro-partition re-clustering (Snowflake) can cause noisy tails.
• Failing to model cost correctly — include storage, network egress, and any managed service add-ons.

Advanced strategies to reduce latency and cost

• Pre-aggregation & projections: Use ClickHouse projections or Snowflake materialized views for common rollups to reduce scan sizes radically.
• Predicate pushdown & pruning: Ensure data is partitioned/clustered on frequently-filtered columns for better I/O elimination.
• Hybrid architecture: Put very hot aggregates in ClickHouse for fast dashboards and keep long-term history in Snowflake or a data lake (Iceberg/Delta) for flexible analytics.
• Observability-driven cost policies: use per-query tagging + automated resource monitors (Snowflake) or cost dashboards (ClickHouse + Cloud billing) to create alerts when cost-per-query drifts.

2026 trends and what to watch

Late 2025 and early 2026 accelerated three trends that affect benchmarking results:

• Vectorized and JIT improvements in many engines improve raw scan speeds — benefit depends on workload shape.
• Open table formats (Iceberg/Delta) and better pushdown for Parquet make external table tests more representative of multi-engine ecosystems.
• Query observability frameworks and OpenTelemetry integrations now let teams connect billing, traces, and query plans — enabling automated tuning actions based on real cost signals.

How to present results for stakeholders

1. Use clear, comparable charts: median & p95 latency, cost-per-query at each concurrency level, and storage cost per TB.
2. Show cold vs warm performance and include background-maintenance impact windows.
3. Highlight tuning steps and their quantified improvements — stakeholders want to see how much improvement is achievable without changing code or schema.

Step-by-step checklist to run your benchmark (quick)

1. Pick dataset (TPCH SF=100 recommended) and produce identical CSV/Parquet inputs.
2. Deploy ClickHouse and Snowflake environments in same region; match approximate vCPU counts.
3. Load data with the provided scripts; validate row counts and checksums.
4. Run single-threaded cold queries (no cache). Record metrics and logs.
5. Run warm queries (run Q once to populate caches). Measure again.
6. Run concurrency sweep (1,4,8,16,32,64) using k6 or clickhouse-benchmark. Capture compute billing windows.
7. Run profiling before and after targeted tuning. Document configuration changes.
8. Calculate cost-per-query using billing and compute usage; prepare charts.

Resources & scripts

Clone a benchmark starter repo (example layout):

• scripts/load_tpch_clickhouse.sh
• scripts/load_tpch_snowflake.sh
• scripts/run_queries.sh (k6 or clickhouse-benchmark config)
• tools/cost_calc.py (ingest cloud billing and usage views)

Final recommendations

If you must choose today:

• Pick ClickHouse if your workload is sustained, scan-heavy, and you can operate a cluster — you’ll generally get better latency and lower sustained cost for large-scale analytics.
• Pick Snowflake if you need minimal ops, elastic concurrency for unpredictable bursts, and want a managed platform with rich ecosystem integrations.
• Consider a hybrid approach: ClickHouse for low-latency dashboards and Snowflake for long-term analytics and BI-backed governance.

Call to action

Use the scripts in this guide to run a baseline in your environment this week. Start with SF=100 TPC-H and the provided concurrency sweep. Measure median and p95 latency, and compute cost-per-query using your cloud prices. If you want, share anonymized results with your team or our benchmarking community to compare against others. Need help running a reproducible benchmark or interpreting results? Contact us for a tailored runbook and a performance review.

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