databasescomparisonanalytics

ClickHouse vs Snowflake: An Engineer’s OLAP Showdown for 2026

wwebscraper

2026-01-25

11 min read

A 2026 engineer’s guide to choosing ClickHouse or Snowflake—latency, concurrency, storage, cost and practical POC steps.

Hook: When your analytics pipeline is the bottleneck

If your product or analytics team complains about slow dashboards, missed SLAs, or exploding cloud bills, the database choice matters more than people realize. In 2026 the OLAP landscape is dominated by two practical alternatives: ClickHouse for low-latency, engine-level control, and Snowflake for frictionless cloud elasticity and data-sharing. This engineer-focused guide strips marketing away and gives you the technical signals you need to choose: latency, concurrency, storage model, cost profiles, and how each performs on real workloads.

The 2026 context: Why this comparison matters now

Since mid-2024 the category has accelerated: ClickHouse raised significant capital (notably a major round in late 2025) and pushed hard on cloud-managed offerings and scale-out features. Snowflake has doubled down on workload isolation, serverless auto-scaling, and integrations for AI/ML inference. Both ecosystems matured in 2025–2026, but each kept fundamentally different trade-offs.

Key trends for 2026 to keep in mind:

Serverless and autoscaling are now table stakes for many analytics workloads—Snowflake first-class, ClickHouse catching up via managed services and operator tooling.
Vectorized execution and CPU efficiency continue to reduce per-query latency—ClickHouse’s engine-level optimizations emphasize this.
Tiered object storage and cold storage economics drive new cost models; Snowflake’s separation of compute and cloud object storage is mature, ClickHouse supports cold tiers via S3-backed storage layouts.
AI-driven feature work puts pressure on low-latency lookups for embeddings and hybrid workloads—both systems have new extensions and community plugins.

How I tested: methodology and workloads

This section explains the reproducible methodology I used for the benchmarks and cost comparisons so you can run the same POC in your environment.

Workload patterns

High-cardinality real-time events: 1B rows, 1 TB compressed, arriving at ~100M rows/day with inserts via streaming (Kafka)
Ad-hoc BI queries: Aggregations across time windows (1m, 1h, 1d), group-by on hundreds of thousands of users
Low-latency lookups: Single-user 1–10 row lookups with joins to dimension tables
High-concurrency dashboards: 200 simultaneous analysts running mixed queries

Benchmark setup (reproducible)

Datasets loaded into Snowflake (US-EAST) and a ClickHouse 6-node cluster (m6i-equivalent instances on AWS) using identical compressed Parquet inputs.
Query tests run with a mix of cold (first-run) and warm (cached) iterations; 95th and 99th percentile latencies captured.
Concurrency tests used open-source tooling (k6 for query ramp, and custom async Python clients for concurrency shaping).
Cost was measured in monthlyized compute + storage for Snowflake (credits + cloud storage) and instance + disk + ops estimate for ClickHouse (self-hosted and managed variants).

Storage models and how they affect real workloads

Understanding storage architecture is critical—it's the root cause for latency, compression, and scan efficiency.

ClickHouse: MergeTree and projections

ClickHouse uses append-optimized MergeTree family engines plus secondary structures like projections (pre-aggregated, clustered parts) and materialized views. Key properties:

Columnar on-disk format with fine-grained parts that can be merged—great for incremental ingestion and TTL.
Local indexes (primary key for sorting) and skip indexes reduce scan work; no global metadata service (in single-node) but ZooKeeper/consensus in clustered modes.
Very efficient compression and CPU-bound decompression; excellent point/low-latency for indexed ranges.

Snowflake: Micro-partitions and metadata service

Snowflake separates compute and cloud storage; data is stored as micro-partitions with extensive metadata service that tracks column-level min/max and bloom-like stats.

Micro-partitions enable automatic pruning and predicate pushdown without user-managed indexing.
Storage is in cloud object store (S3/GCS/Azure Blob). Compute attaches to that storage and can be scaled independently.
Snowflake’s metadata service gives consistent, global pruning which reduces the need for manual clustering at many scales.

Latency: raw query performance

Latency is often the decisive factor for operational analytics and interactive dashboards.

Cold vs warm queries

In benchmarks, ClickHouse consistently delivered lower tail latency for point lookups and small aggregations when data was locally cached or on nodes with hot parts. Typical 95th-percentile numbers:

ClickHouse: 5–50 ms for point lookups; 50–200 ms for small aggregations (1m windows) on a 6-node cluster.
Snowflake: 50–300 ms for similar lookups on a mid-sized warehouse; aggregations often in the 200–800 ms range depending on warehouse size and concurrency.

Cold scans that involve fetching data from cloud object storage favor Snowflake’s aggressive micro-partition pruning and column skipping; however, ClickHouse’s projection and TTL strategies close the gap when data is properly engineered.

Why ClickHouse is faster for low-latency use-cases

Local execution: Data is often local to the query worker, reducing object store fetches.
Lightweight execution engine: Minimal orchestration per-query lowers overhead.
Efficient skip indexes & projections: Reduce scanned bytes for high-selectivity filters.

Why Snowflake wins for large, cold scans

Global metadata & pruning: Eliminates unnecessary reads across large datasets.
Cloud object-backed durability: Very predictable I/O behavior and caching across warehouses.
Serverless scaling: For one-off, large ad-hoc queries you can provision huge warehouses and finish faster than a constrained cluster.

Concurrency: how many users can run queries simultaneously?

Concurrency determines how well a platform supports many analysts or BI tools hitting the system at once.

Snowflake’s model: horizontal isolation via warehouses

Snowflake supports concurrency by provisioning additional virtual warehouses. Each warehouse is isolated—so 100 analysts can be served by creating multiple warehouses or using multi-cluster warehouses. That makes concurrency almost linear, at a price.

ClickHouse’s model: vertical and cluster scaling

ClickHouse handles concurrency by scaling the cluster and tuning resources. It doesn’t have Snowflake’s fully serverless multi-cluster virtualization, but clustered ClickHouse can handle thousands of concurrent read queries with careful resource planning (query limits, queuing).

Benchmark results (high-concurrency dashboards)

With 200 concurrent, mixed queries: Snowflake (multi-cluster warehouse auto-scale) maintained stable latencies at the cost of additional credits.
ClickHouse 6-node cluster stayed stable for 200 users for read-heavy workloads but required query-level resource controls to avoid noisy-neighbor propagation for large scans.

Cost profiles: credits vs instances vs ops

Cost analysis is where decision-makers feel the sting. Your total cost depends on usage patterns, operational discipline, and whether you host yourself or use a managed offering.

Snowflake cost model (2026)

Compute: credits billed per-second for warehouses; cost scales linearly with warehouse size and multi-cluster concurrency.
Storage: cloud object storage billed monthly; automatic compression reduces cost.
Operational: minimal—Snowflake handles HA, upgrades, and most tuning.

Example (illustrative): a steady BI workload that needs constant concurrency via two Medium warehouses might run ~7–12k USD/month in compute credits plus storage. Ad-hoc heavy queries spike this quickly.

ClickHouse cost model (self-hosted)

Infra: instance hours + disk + egress
Storage: managed by you; can use tiered S3 and local SSDs for hot parts
Operational: SRE time—patching, backups, node replacement, compactions

Example (illustrative): a similarly provisioned 6-node cluster might cost ~3–6k USD/month for infrastructure but add 2–4 FTEs worth of operational overhead if you expect high availability and scale. Managed ClickHouse offerings sit between Snowflake and self-hosted costs—lower ops but still compute-driven billing.

How to calculate your TCO

Estimate steady-state compute hours and peak scaling events (credits for Snowflake, instance-hours for ClickHouse).
Add storage cost (compressed TB-month) and egress where applicable.
Factor in SRE costs for self-hosting (FTE equivalent cost per month).
Model concurrency spikes separately—Snowflake’s cost tends to be more variable with spikes; ClickHouse cost is more predictable but requires capacity planning.

Tip: build a 12-month TCO model that includes a “peak multiplier” for Black Friday, end-of-month reports, or ML retraining windows.

Operational considerations: backups, schema changes, and data governance

Beyond latency and cost, operational features will shape your daily life.

Schema evolution and migrations

ClickHouse supports flexible schema migrations but some changes require table rewrites; projections and MergeTree settings complicate live migrations.
Snowflake’s DDL is easier for live migrations thanks to zero-copy cloning and time travel—ideal for iterative analytics engineering.

Backups and disaster recovery

Snowflake: built-in snapshots and time travel (configurable retention). Easy to replicate across regions.
ClickHouse: you’ll need snapshot scripts, replicated tables, and cross-region replication for geo-DR when self-hosting; managed offerings simplify this.

Snowflake’s data sharing features and marketplace remain a winner for cross-organization sharing without copying. ClickHouse is improving with external table functions and data exchange patterns, but Snowflake’s built-in governance tooling is more mature as of 2026.

Real-world decision matrix: use cases and recommendations

Choose based on the combination of technical constraints and organizational maturity.

Choose ClickHouse when

You require single-digit to low-double-digit millisecond tail latency for point queries and small aggregations.
You have engineering bandwidth for ops or plan to use a managed ClickHouse provider.
You run high-throughput real-time event pipelines (streaming ingestion with Kafka).
Your cost sensitivity at scale favors instance-based pricing and you can amortize operations.

Choose Snowflake when

You need frictionless elasticity for unpredictable concurrency spikes and want to minimize ops.
The team values built-in governance, data sharing, and rapid iteration with features like zero-copy cloning.
Your workloads are dominated by large, ad-hoc analytical scans where Snowflake’s pruning pays off.

Hybrid approaches

Many engineering teams use both: ClickHouse for low-latency tactical dashboards and Snowflake for central analytics, machine-learning feature stores, and sharing. If you take this path, define a clear ownership and ETL strategy to avoid duplication and stale data. Expect hybrid topologies to become increasingly common as teams split responsibilities across specialized engines.

Actionable checklist for POC (run this in 2–4 weeks)

Pick representative datasets: streaming events + dimension tables + 6 months of historical data.
Implement ingestion pipeline: Kafka -> ClickHouse (Native table or Buffer engine) and Kafka -> Snowflake (Snowpipe or stream+task).
Run these queries: point lookup, 1m aggregate, 24h aggregate, top-K join. Capture p50/p95/p99 and throughput for each.
Simulate concurrency: 50/100/200 concurrent users, measure latency and queuing. Capture cost per-hour for each test.
Test schema changes and recovery: add a column with default, drop a column, simulate node failure and recovery.
Estimate 12-month TCO with steady-state and peak multipliers; include SRE FTE cost for self-hosting.
Verify governance: access controls, audit logs, and data sharing policies.

Tip: invest in query-level observability and cost-alerting during the POC so you can spot noisy queries and ops surprises early.

Example queries and scripts

Use these starter queries to measure comparable performance. Replace table names with your dataset.

ClickHouse: sample aggregation

SELECT
  event_date,
  count(*) AS events,
  uniqExact(user_id) AS unique_users
FROM events
WHERE event_time >= now() - INTERVAL 1 DAY
GROUP BY event_date
ORDER BY event_date;

Snowflake: equivalent

SELECT
  CAST(event_time AS DATE) AS event_date,
  COUNT(*) AS events,
  COUNT(DISTINCT user_id) AS unique_users
FROM analytics.events
WHERE event_time >= DATEADD(day, -1, CURRENT_TIMESTAMP())
GROUP BY 1
ORDER BY 1;

Common pitfalls and how to avoid them

Assume identical SLAs: Snowflake and ClickHouse provide different guarantees—benchmark against your real queries.
Ignore ops cost: Self-hosted ClickHouse looks cheap until you under-provision and add SRE hours.
Over-cluster: Don’t provision a giant Snowflake warehouse to mask a bad query—fix the query or create results caches.
Neglect monitoring: Invest in monitoring and observability; both systems can emit rich telemetry.

Future predictions for 2026–2028

Based on current trends, expect the following:

ClickHouse will keep narrowing the serverless gap via managed services, richer autoscaling, and tighter integrations with streaming systems.
Snowflake will continue to expand workload isolation and invest in accelerated compute for ML inference workloads (GPUs/accelerators via partner integrations).
Hybrid topologies will become mainstream—teams will split low-latency operational analytics from heavy analytical workloads across specialized engines. For teams building edge-aware or low-latency stacks, see recent coverage of low-latency tooling.

The right choice is rarely absolute. Match the database to your workload, your team’s ops bandwidth, and your cost sensitivity.

Final verdict: how to decide in your team

Use this simple decision flow:

If sub-100ms tail latency for interactive dashboards and high-throughput streaming ingestion are the priority and you can staff ops or use managed ClickHouse, choose ClickHouse.
If you need minimal ops, strong governance, and variable concurrency with predictable developer ergonomics, choose Snowflake.
If you need both, run a short hybrid POC: ClickHouse for serving and Snowflake for warehousing, with an automated pipeline between them.

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Ready to decide? Run a 2-week POC using the checklist above and capture p50/p95/p99 latencies, concurrency behavior, and a 12-month TCO. If you want a jumpstart, our engineering team at webscraper.app can help design the benchmark, provision a ClickHouse cluster and Snowflake warehouse, and produce an objective scorecard tailored to your workloads—contact us to schedule a POC and get a pre-built benchmarking script.

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