ClickHouse vs Snowflake for ML Analytics: Cost, Latency and Scale

Hook: Why infra teams can’t treat OLAP as a commodity for ML analytics

If your team is building feature pipelines, capturing model training telemetry, or running thousands of A/B experiments, the OLAP choice is not academic. You need predictable latency, predictable cost, and tooling that fits both streaming and batch ML workflows. Engineers I work with ask the same hard questions: What OLAP gives sub-second aggregations for per-user telemetry at 10k QPS? Which system produces reproducible training datasets without blowing the budget? How do we design a hybrid architecture that balances latency and TCO?

Executive summary — the short answer (2026)

• ClickHouse is the pragmatic pick when you need low-latency, high-ingest telemetry and cost-effective real-time feature materialization at scale.
• Snowflake remains the safer choice when you need broad SQL compatibility, multi-tenant concurrency, advanced governance, and a managed ecosystem for producing large, reproducible training datasets.
• Most production ML stacks in 2026 use a hybrid pattern: ClickHouse for real-time telemetry & feature serving, Snowflake for curated feature stores, batch joins, and long-term training data.

Context & trends shaping the decision in 2026

Two 2025–2026 trends reshape the vendor calculus for ML analytics:

1. Rising demand for real-time features: Product telemetry, personalization, and experimentation moved from minutes to milliseconds. Model performance now depends on live feature freshness.
2. Cost scrutiny and multi-cloud complexity: After a wave of cloud-cost optimization in late 2024–2025, infra teams put TCO and predictable billing at the top of procurement checklists.

ClickHouse’s momentum accelerated in early 2026 after a major funding round (ClickHouse Inc. raised $400M at an increased valuation), signaling strong adoption for high-throughput OLAP. Snowflake’s evolution continued toward stronger data governance, Snowpark compute, and better integration with ML toolchains — making it the platform many enterprises default to for governance-compliant training data.

Key technical criteria for ML analytics

When evaluating OLAP for ML telemetry, feature pipelines, and A/B experiments, score systems against these criteria:

• Ingest throughput & write latency — can you capture event streams without batching delays?
• Query latency at scale — percentiles (P50, P95, P99) for point and aggregation queries under concurrency.
• Fine-grained joins and transformations — supported SQL semantics for feature engineering and complex joins.
• Cost predictability & TCO — storage, compute, egress, operational overhead.
• Operational complexity — self-host vs managed, patching, backups, disaster recovery.
• Security & Governance — access control, data masking, compliance reporting.
• Integration with ML tooling — native connectors to feature stores, Spark, Python SDKs, and orchestration systems.

Feature-by-feature comparison

1) Ingest: telemetry and streaming

ClickHouse:

• Strengths: High sustained ingest (hundreds of thousands to millions of rows/sec per cluster with correct sizing), native Kafka engine and materialized views that can keep computed features near real time.
• Considerations: When self-hosting, sizing shards, managing compactions (MergeTree), and TTL policies add operational work. ClickHouse Cloud reduces ops but tradeoffs remain around multi-region replication and cross-cloud egress.

Snowflake:

• Strengths: Snowpipe streaming and Snowflake Streams + Tasks give managed CDC/streaming semantics and simpler developer experience for continuous ingestion.
• Considerations: For ultra-high ingest rates, Snowflake costs can escalate since continuous compute scales with pipes and micro-partition writes. Backpressure behavior differs — Snowflake favors managed durability and ease over lowest latency.

2) Latency & query performance

ClickHouse:

• Exceptional for single-row lookups and low-latency aggregations due to columnar storage, vectorized execution, and indexes like primary key + sparse indexes. P50/P95 aggregations on billions of rows often return sub-second on well-provisioned clusters.
• Historically JOINs were constrained; modern ClickHouse versions improved joins (hash joins, better memory handling) but complex multi-way joins still require careful schema planning.

Snowflake:

• Designed for heavy concurrency and complex SQL (window functions, multi-way joins). Latency for ad-hoc large aggregations can be multiple seconds to low tens of seconds depending on warehouse size and cache warmness.
• Concurrency scaling and result caching can mask cost/latency, but unpredictable spikes can inflate bills if warehouses scale up.

3) Scale and concurrency

ClickHouse scales horizontally via sharding and replication. It is cost-efficient at sustained high throughput but requires infra expertise for cluster orchestration. Snowflake’s separation of compute and storage makes concurrency simple: create additional warehouses or use auto-suspend/auto-resume. That convenience comes at higher per-query cost for sustained high QPS.

4) SQL ergonomics and feature engineering

Snowflake provides a more comprehensive SQL surface (ANSI-compliant behaviors, stored procedures via Snowpark, time travel, zero-copy cloning) which makes reproducible engineering of training sets easier. ClickHouse supports SQL-like expressions and materialized views, but feature engineering that depends on complex multi-table transforms often needs to be pushed to an ETL layer (Spark/Flink) or precomputed tables.

5) Governance, security, and compliance

Snowflake typically wins here for enterprises: RBAC, object-level access controls, data masking, and a mature marketplace and partner ecosystem. For regulated data, Snowflake’s managed compliance artifacts are often easier to validate. ClickHouse has improved in cloud offerings but self-hosted deployments will need additional work to match enterprise governance controls.

Practical architectures and where each fits

Below are three pragmatic architectures your team can use depending on priority.

Pattern A — Low-latency feature serving (ClickHouse-first)

Best when per-request latency and cost per QPS are top priorities (e.g., personalization, fraud scoring).

1. Events -> Kafka -> ClickHouse (Kafka engine) for raw event capture.
2. Materialized views in ClickHouse to compute online features and aggregates (rolling windows, user-level counters).
3. Feature API reads from ClickHouse for serving models in prod.
4. Periodic snapshots exported to Snowflake (or object storage) for training set versioning.

Pattern B — Governance-first training pipeline (Snowflake-first)

Best when reproducible datasets, data contracts, and enterprise governance matter more than micro-latency.

1. Events -> Cloud storage (S3/GCS) or Snowpipe -> Snowflake.
2. Use Streams + Tasks + Snowpark to maintain feature tables, perform complex joins, and create training-ready datasets.
3. Model training runs inside a compute environment that pulls curated tables (or uses Snowpark compute for some workloads).
4. For online serving, export feature views into a low-latency store (Redis or ClickHouse) or use Snowflake External Functions for low-volume serving.

Pattern C — Hybrid (recommended for most teams)

Combine ClickHouse for serving and Snowflake for curation:

1. ClickHouse is the canonical store for event telemetry and online feature reads.
2. Snowflake is the canonical store for reproducible training datasets, governance, and long-term retention.
3. Use scheduled exports or CDC (Kafka connectors, Snowpipe) to keep Snowflake in sync for batch machine learning.
4. Orchestrate with Airflow/Argo + CI units to guarantee reproducible pipelines and data contracts.

Concrete examples & query patterns

Two short SQL examples show typical queries for feature materialization and experiment analysis.

ClickHouse: rolling user counter (materialized view)

CREATE TABLE events
(
  event_time DateTime,
  user_id UInt64,
  event_type String,
  properties String
) ENGINE = MergeTree()
PARTITION BY toYYYYMM(event_time)
ORDER BY (user_id, event_time);

CREATE MATERIALIZED VIEW user_daily_counts
ENGINE = SummingMergeTree()
PARTITION BY toYYYYMM(event_time)
ORDER BY (user_id, day)
AS
SELECT
  user_id,
  toDate(event_time) AS day,
  count() AS events
FROM events
GROUP BY user_id, day;

This pattern yields low-latency reads for per-user counters that are used as online features.

Snowflake: reproducible cohort aggregation for A/B experiments

CREATE OR REPLACE TABLE experiment_events AS
SELECT
  user_id,
  experiment_id,
  event_time::date AS day,
  event_type
FROM raw.events
WHERE event_time >= '2025-12-01';

CREATE OR REPLACE VIEW experiment_summary AS
SELECT
  experiment_id,
  day,
  count(distinct user_id) AS unique_users,
  sum(case when event_type='purchase' then 1 else 0 end) as purchases
FROM experiment_events
GROUP BY experiment_id, day;

Snowflake’s views, time travel, and cloning make it straightforward to freeze a dataset for a training job and later reproduce it.

Benchmarks and measurement plan (do this first)

Before you pick one system based on vendor claims, run a controlled benchmark. Use this checklist:

1. Dataset: Use a realistic event stream (schema, cardinality, skew). Include user_id skew, timestamp bursts, and session patterns.
2. Queries: Define 6-8 representative queries — point lookup, single-user aggregation, multi-join feature build, cohort analysis, top-k group-by.
3. Load pattern: Ramp to production QPS, include a sustained peak period, and measure recovery from cold cache.
4. Concurrency: Run with expected concurrency (e.g., 50–500 concurrent analysts) and additional production query load.
5. Metrics: Capture P50/P95/P99 latency, CPU utilization, memory usage, disk I/O, and egress. Record costs for storage and compute over a month-equivalent window.
6. Repeatability: Automate tests and run them across times of day and under failure conditions (node restart, network partition).

Cost and TCO templates — what to model

Model these line items for both ClickHouse and Snowflake:

• Storage: Active hot storage vs. cold/archival. ClickHouse with compressed columnar storage often yields lower active storage cost on self-hosted infra; Snowflake charges for cloud storage and micro-partitions.
• Compute: For ClickHouse, CPU nodes and network. For Snowflake, warehouse sizes and auto-scaling events.
• Ingress/Egress: Cross-cloud egress or replication costs if you span regions/clouds.
• Operational: SRE effort, upgrades, monitoring, backups (higher for self-hosted ClickHouse).
• Integration & Developer Productivity: Time to onboard analysts, building Snowpark jobs vs ClickHouse UDFs or external processing.

Sample TCO formula (monthly):

TCO_month = Storage_cost + Compute_cost + Network_cost + Ops_cost + Integration_cost

Fill in rough numbers using your internal mbean (e.g., vCPU hourly cost, TB storage cost, SRE FTE cost prorated to the system). Run sensitivity analysis: how does TCO change if event ingestion doubles or if you need 3x concurrency?

Operational trade-offs and pitfalls

• ClickHouse pitfall: Underestimating compaction and memory pressure during heavy JOINs. Mitigation: pre-aggregate, use denormalized schemas, and autoscale worker pools.
• Snowflake pitfall: Uncontrolled auto-scaling for ad-hoc analytics leads to surprise bills. Mitigation: cost governance, resource monitors, and query queues.
• Common pitfall: Treating one OLAP system as the canonical store for both online serving and curated training data. Mitigation: use a hybrid pattern and clear data contracts.

Case studies & ROI (anonymized, practical)

Case — Real-time personalization (adtech-like workloads)

Situation: A personalization team had a 10–50 ms SLA for per-request feature reads and ~200k events/sec ingest. They migrated online serving to ClickHouse and kept Snowflake for analytics.

Outcome: Query P95 fell from ~800ms to ~45ms for per-user aggregates. Monthly infra cost for online queries dropped ~45% after adjusting instance types and compression. Snowflake remained the substrate for offline experiments and model training.

Case — Enterprise feature store & training datasets

Situation: A regulated fintech firm required auditable, reproducible training datasets and role-based access. They standardized on Snowflake for feature engineering and audit trails, while exporting a subset of features to ClickHouse for low-latency scoring.

Outcome: Time-to-audit decreased by 60%, and reproducible pipelines reduced model-regression incidents. The hybrid setup kept total TCO within budget because the high-cost Snowflake compute was reserved for batch pipelines.

"In 2026, most teams will use more than one OLAP system — the question is how to partition responsibilities so each system plays to its strengths."

Decision framework & recommended checklist

Use this weighted checklist to make a decision in a month-long evaluation:

1. Score latency requirements: If P95 < 200ms for online features → ClickHouse + caching.
2. Score concurrency/governance: If you need enterprise RBAC/PCI/SOX > 0 → Snowflake is favorable.
3. Score ingest vs batch tradeoff: Sustained high ingest → ClickHouse; bursty batch ingestion with heavy joins → Snowflake.
4. Run the benchmark plan above and compare TCO sensitivity to a 2x ingress and 3x concurrency scenario.
5. Prototype the hybrid pattern — a two-week PoC: live telemetry to ClickHouse and nightly sync to Snowflake. Measure ops effort.

Advanced strategies for 2026 and beyond

Look ahead and build flexibility into your architecture:

• Feature mesh: Publish features as namespaced artifacts with contracts. Use feature access logs to validate production drift.
• Cold path + hot path: Keep a hot ClickHouse layer for 30–90 days and cold storage (object store + Snowflake) for long-term retention and model explainability.
• Vector features: If you embed vectors and need nearest-neighbor, evaluate whether ClickHouse vector extensions or Snowflake + vector search services better fit latency and cost requirements.
• Observability-first: Pipeline SLAs, SLOs for feature freshness, and lineage are non-negotiable as ML systems go to production at scale.

Quick reference: When to pick which

• Choose ClickHouse if: low-latency reads, very high ingest, cost-per-QPS, and you’re comfortable operating clusters or using managed ClickHouse Cloud.
• Choose Snowflake if: governance, complex SQL & joins, reproducibility, and low operational overhead matter more than micro-latency.
• Choose Hybrid if: you need both — real-time serving and enterprise-grade training datasets.

Actionable next steps — a 30-day plan

1. Run the benchmark checklist against a representative dataset (week 1).
2. Deploy a 2-week PoC hybrid pipeline: Kafka -> ClickHouse (online) + daily export -> Snowflake (week 2–3).
3. Measure P50/P95/P99, compute costs, ops effort, and produce a TCO sensitivity table (week 4).
4. Decide with stakeholders and document the data contract for feature syncs and retention policies.

Final recommendations

There’s no single winner — the best choice depends on your workload mix and organizational constraints. For most ML telemetry and experiment-heavy stacks I advise starting with a hybrid approach: ClickHouse for the hot path and Snowflake for the curated, auditable training path. This splits responsibility cleanly and lets each system be used where it provides the best ROI.

Call to action

Ready to test this in your environment? Download the benchmark template, TCO spreadsheet, and PoC checklist I use with engineering teams — or book a short technical review to map your telemetry, feature pipeline, and A/B experiment needs to an execution plan. If you want, paste your ingestion rate, target latencies, and concurrency numbers and I’ll return a tailored architecture recommendation.

Related Reading

• Turning MMO Items into NFTs: What Players Should Know Before New World Goes Offline
• Designing Resilient Web Architecture: Multi‑Cloud Patterns to Survive Provider Outages
• What Darden’s ‘socially responsible’ tag means for food sourcing: A shopper’s guide
• Geography Project Ideas Inspired by the 17 Best Places to Visit in 2026
• What the Italian Raid on the DPA Means for Consumers’ Data Rights