From Snowflake to ClickHouse: A Cost & Performance Migration Decision Matrix

Hook: Why your cloud analytics bill keeps surprising you — and what to do about it in 2026

If your cloud analytics costs feel unpredictable, you're not alone. Teams in 2026 face growing data volumes, mixed query patterns (ad-hoc exploration, dashboards, and near‑real‑time feeds), and a proliferation of BI tools. That makes choosing the right OLAP platform a high‑stakes FinOps decision. The recent ClickHouse funding surge — a $400M round led by Dragoneer valuing the company at roughly $15B (Bloomberg, Jan 2026) — has changed the competitive landscape. It’s time to reassess whether Snowflake or ClickHouse (or a hybrid approach) best meets your cost, performance, and operational goals.

Executive summary — the decision in one paragraph

Use Snowflake when you prioritize a fully managed, predictable operational model with built‑in features (zero‑copy cloning, time travel, rich security controls) and you need concurrency and integrations out of the box. Use ClickHouse when you need sub‑second aggregations at scale and want to drive the lowest possible OLAP cost per query, or when you can accept more operational responsibility in exchange for higher performance and lower long‑term compute/ storage cost. In 2026, ClickHouse Cloud narrows the operational gap — and ClickHouse’s funding tailwinds accelerate ecosystem integrations and feature parity — making it a viable migration target for many analytics workloads. The right choice depends on query patterns, data lifecycle, and your FinOps targets.

2026 context: why ClickHouse’s funding matters

Large funding rounds are noisy, but they matter. ClickHouse’s 2025–2026 capital infusion has three immediate effects you should care about:

• Faster product maturity — new features and managed service investments reduce operational friction for teams that previously required self‑hosting expertise.
• Stronger ecosystem integrations — more connectors, native BI integrations, and enterprise features (RBAC, compliance) make ClickHouse a realistic enterprise candidate.
• Price & performance scrutiny — Snowflake customers and FinOps teams now have a high‑performance alternative to benchmark against.

Reference: Dina Bass, Bloomberg (Jan 2026) — ClickHouse raised $400M led by Dragoneer at a ~$15B valuation.

How to decide: the migration decision matrix (framework)

Below is a practical, weighted decision matrix you can apply to evaluate Snowflake vs ClickHouse for your analytics workloads. Each criterion maps to a FinOps or performance concern. Score each on a scale of 1–5, multiply by the weight, and sum to get a directional score.

Decision criteria and weights (recommended)

• Cost predictability and unit economics — weight 25%
• Query performance latency & concurrency — weight 20%
• Operational complexity & runbook maturity — weight 15%
• Data lifecycle & storage costs — weight 15%
• Ecosystem and integrations — weight 10%
• Security, compliance & governance — weight 10%
• Migration effort & business risk — weight 5%

How to score

1. For each criterion, assign Snowflake and ClickHouse a score 1–5 where 5 is best for your org.
2. Multiply by the criterion weight (percentage as decimal) and sum.
3. Compare totals; differences >0.25 generally indicate a clear direction. If scores are tight, run a controlled POC with representative workloads.

Sample decision matrix (hypothetical)

In this example the scores are close — Snowflake slightly ahead because of operational maturity. But if your workload is heavy on large, repeatable OLAP aggregations and sub‑second dashboard latency matters, ClickHouse will often pull ahead after accounting for reduced compute costs and better CPU efficiency.

Cost modeling — a pragmatic ROI calculator

Stop guessing. Build a simple ROI model with three sections: storage, compute (query), and operational costs. Below is a reusable template and an example set of numbers you can adapt.

Model inputs

• Raw data size (TB)
• Expected compression ratio (Snowflake & ClickHouse differ; use empirical values)
• Monthly active queries (number of queries per month)
• Average query runtime and compute units (seconds/CPU or credits)
• Concurrency / peak QPS
• Storage price per TB / month (cloud object store)
• Managed service markup (Snowflake, ClickHouse Cloud) vs raw cloud costs
• Operational team cost (FTEs) proportion assigned to DB ops

Basic formulas

• Effective storage TB = Raw TB / compression_ratio
• Monthly storage cost = Effective storage TB * storage_price_per_TB
• Monthly compute cost = SUM(query_runtime_seconds * CPU_cost_per_second)
• Operational cost = FTEs_for_ops * fully_loaded_FTE_cost / 12
• Total monthly cost = storage + compute + operational

Example (hypothetical numbers, adapt for your region)

Scenario: 10 TB raw data, 10M queries/month, average query runtime 0.4s on ClickHouse, 2s on Snowflake (due to cold warehouses & general purpose compute), storage price $20/TB‑month for S3.

// Inputs
raw_tb = 10
compression_clickhouse = 6   // ClickHouse often achieves higher compression on columnar compressed data
compression_snowflake = 4
storage_price_tb = 20       // $/TB/month (cloud object store cost)
queries_month = 10_000_000
avg_runtime_ch = 0.4        // seconds
avg_runtime_sf = 2.0
cpu_cost_per_sec_ch = 0.000002  // example $/sec per effective CPU
cpu_cost_per_sec_sf = 0.000004
ops_fte_ch = 0.5
ops_fte_sf = 0.2
fte_cost_annual = 180000

// Calculations
storage_ch_tb = raw_tb / compression_clickhouse   // = 1.666 TB
storage_sf_tb = raw_tb / compression_snowflake    // = 2.5 TB
monthly_storage_ch = storage_ch_tb * storage_price_tb   // = $33.33
monthly_storage_sf = storage_sf_tb * storage_price_tb   // = $50

monthly_compute_ch = queries_month * avg_runtime_ch * cpu_cost_per_sec_ch  // = $8,000
monthly_compute_sf = queries_month * avg_runtime_sf * cpu_cost_per_sec_sf  // = $80,000

monthly_ops_ch = ops_fte_ch * fte_cost_annual / 12  // = $7,500
monthly_ops_sf = ops_fte_sf * fte_cost_annual / 12  // = $3,000

monthly_total_ch = monthly_storage_ch + monthly_compute_ch + monthly_ops_ch  // ~ $15,533
monthly_total_sf = monthly_storage_sf + monthly_compute_sf + monthly_ops_sf  // ~ $83,050

Interpretation: In this hypothetical, ClickHouse is far cheaper on monthly compute and storage despite higher ops manpower. Your real numbers will vary. Key takeaways: compute is the biggest lever, compression matters, and query runtime is the single largest multiplier.

Query patterns & how they influence the decision

Not all analytics are equal. Map your queries into patterns and pick the platform that matches the dominant patterns.

Pattern A — Ad‑hoc, high‑cardinality joins, frequent exploratory SQL

• Snowflake advantage: ANSI SQL dialect, extensive optimizer, dynamic warehouses for concurrency, strong semi‑structured data support (VARIANT), easy user provisioning.
• ClickHouse considerations: joins are improving, but high‑cardinality large joins can be more expensive unless pre‑joined or denormalized. Consider pre‑aggregation and materialized views in ClickHouse.

Pattern B — Dashboards and repeated aggregations over time series

• ClickHouse advantage: designed for sub‑second aggregation at scale, compressed columnar storage, and highly efficient vectorized execution.
• Snowflake still works but can be costlier for very high QPS or when warehouses remain warm.

Pattern C — Near real‑time ingestion and streaming analytics

• ClickHouse advantage: low‑latency inserts (especially with MergeTree or SummingMergeTree), TTLs for retention, excellent for time‑series dashboards.
• Snowflake advantage: Snowpipe and streaming ingestion with micro‑batches, simpler operational surface for many teams.

Pattern D — Heavy machine learning feature stores or ad hoc large shuffles

• Snowflake advantage: integrations with Snowpark, managed scaling, and strong governance for feature stores.
• ClickHouse considerations: can be used as a feature store for high‑throughput read paths but requires careful design for write patterns and feature freshness.

Operational trade‑offs (what your SRE/DBA will tell you)

• Managed vs self‑host: Snowflake is fully managed; ClickHouse historically required more ops skill but ClickHouse Cloud narrows this. If you want to avoid database operations entirely, Snowflake is often easier.
• Scaling model: Snowflake hides scaling complexity with virtual warehouses and auto‑suspend/resume. ClickHouse scales horizontally (shards/replicas); autoscaling requires orchestration and capacity planning unless using ClickHouse Cloud.
• Backups & recovery: Snowflake has time travel and fail‑safe features; ClickHouse supports backups via snapshots and cloud object storage but recovery procedures differ.
• Security & compliance: Both platforms support enterprise security, but Snowflake’s Data Cloud has bundled governance features. Evaluate specific certifications needed (e.g., SOC2, ISO27001, HIPAA).
• Monitoring & runbooks: ClickHouse’s performance monitoring is improving; build robust telemetry (system tables, query logs) and alerting for merges, disk pressure, and latency.

Migration playbook — practical, low‑risk approach

Follow these steps to evaluate and migrate with controlled risk.

1. Inventory queries — capture a 30–90 day snapshot of queries, QPS, runtime distribution, and data touched per query.
2. Classify workloads — map queries into the patterns above and assign candidates for migration (e.g., dashboards to ClickHouse, ad‑hoc to Snowflake).
3. Proof‑of‑value — run a POC with a representative dataset (10–20% of data), and benchmark 1 week of production traffic replayed to both platforms.
4. Build transformation rules — create SQL translation rules for ClickHouse (MergeTree, ORDER BY, TTL, INSERT semantics) and validate semantic equivalence with Snowflake outputs.
5. Measure costs — use the ROI model above with real POC metrics. Include engineering time for migration and runbook creation.
6. Staged cutover — route read traffic of low‑risk dashboards to ClickHouse first, monitor for regressions, then expand.
7. Iterate & optimize — implement materialized views, pre‑aggregations, and compression tuning after cutover to reduce operational cost further.

Sample ClickHouse DDL (migration tip)

-- Snowflake source: wide immutable table
CREATE TABLE events (
  user_id UInt64,
  ts DateTime,
  event_type String,
  properties String
) ENGINE = MergeTree()
PARTITION BY toYYYYMM(ts)
ORDER BY (user_id, ts)
TTL ts + INTERVAL 90 DAY
SETTINGS index_granularity = 8192;

Notes: choose partitioning and ORDER BY to match common query predicates. Use TTLs to reclaim storage for older data.

FinOps best practices for both platforms

• Break down costs by query and team — attach cost metadata to queries using tags where possible.
• Use scheduled compute for periodic workloads and autosuspend for interactive workloads.
• Enforce quota & monitoring — resource monitors (Snowflake) or query limits (ClickHouse proxies) prevent runaway cost spikes.
• Optimize data format and compression — smaller storage lowers both storage and IO costs across platforms.
• Implement pre‑aggregation for high QPS dashboards — trade storage for compute savings.
• Run regular cost experiments — run identical workloads on both platforms for one month and compare per‑query cost.

When to choose a hybrid strategy

Many organizations land on a hybrid answer: Snowflake for ETL, governance, ad‑hoc analytics, and external data sharing; ClickHouse for high‑throughput dashboards and feature‑serving read paths. A hybrid approach lets you get the best of both worlds while providing a migration runway for workloads where ClickHouse delivers clear ROI.

Real‑world example (anonymized)

A mid‑sized adtech company moved 40% of its dashboard queries from Snowflake to ClickHouse Cloud. They reported a 6x reduction in cost per dashboard query and 70% faster median response time after three months. The migration used pre‑aggregations and a staged cutover, and the team invested one full‑time engineer for 6 weeks in migration automation and runbooks.

Advanced strategies and 2026 trends to watch

• Distributed vectorized engines — both platforms are evolving vectorized execution and better memory management; benchmark with your UDFs and machine learning features.
• Serverless OLAP and autoscaling clusters — expect improved serverless primitives in ClickHouse Cloud and further Snowflake features that reduce cold start costs.
• Composable analytics — more teams combine purpose‑built engines (ClickHouse) with governed stores (Snowflake) to balance speed and compliance.
• FinOps observability — better query‑level chargeback and cost attribution tools are emerging (2025–2026), making ROI comparisons more accurate.

Checklist: Are you ready to migrate?

• Do you have representative query telemetry for 30–90 days?
• Can you classify >70% of queries into patterns suitable for ClickHouse?
• Do you have a POC environment and a plan to measure per‑query cost?
• Are stakeholders aligned on acceptable operational trade‑offs?
• Have you budgeted for 1–2 FTE months for migration and classroom training?

Final thoughts — a data‑driven approach wins

ClickHouse’s 2025–2026 funding has lowered the barrier for teams to consider it as a primary analytics engine. But decisions should be rooted in data — your query telemetry, cost model, and risk tolerance. Use the decision matrix and ROI template above as your first pass. Then validate with a POC that replays real traffic and measures actual per‑query cost and latency.

Call to action

Ready to evaluate a migration with hard numbers? Download our free ROI spreadsheet and migration checklist, or schedule a 30‑minute FinOps review with our cloud analytics team. We’ll run a lightweight POC using your query traces and deliver a quantified recommendation: Snowflake, ClickHouse, or hybrid — plus a prioritized migration roadmap.

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