Evaluating OLAP Options for Observability Storage: ClickHouse vs Snowflake for Monitoring Pipelines

Cutting through the noise: why observability storage needs a different OLAP evaluation in 2026

Observability teams in 2026 are juggling three hard truths: unpredictable cold-start costs on serverless collectors, exploding cardinality from distributed architectures, and an expectation for near-real-time root-cause queries. If your monitoring pipeline can't absorb bursts, return results in seconds, and keep cost predictable, it becomes the bottleneck for reliability.

This article evaluates two leading OLAP choices for observability storage — ClickHouse and Snowflake — specifically for logs, traces and high-cardinality metrics. We'll compare cost, ingestion latency, query performance, and operational complexity, provide reproducible benchmark designs and show when to pick one over the other in 2026’s cloud- and edge-first stacks.

Market context (late 2025 → 2026): why both platforms matter now

The analytics space continued to consolidate in late 2025. ClickHouse — long a performance favorite for event analytics — closed a major funding round in late 2025, signalling aggressive product and cloud expansion (Bloomberg). Snowflake, meanwhile, has continued enhancing ingestion (Snowpipe and streaming ingestion) and compute elasticity across multicloud regions. Both vendors are investing in features aimed at observability: faster streaming ingest, cheaper long-term storage, and integrations with distributed tracing and log-forwarding ecosystems.

Fact: ClickHouse raised $400M in late 2025, increasing its market momentum as a Snowflake challenger (Bloomberg, Jan 2026).

High-level tradeoffs

• ClickHouse — excels at sub-second ingestion and low-latency analytical queries for high-volume event data. Strong compression, extensible storage engines (MergeTree family) and materialized views make it ideal for real-time log aggregation and ad-hoc exploration. Historically more operationally heavy unless you use managed ClickHouse Cloud.
• Snowflake — fully managed, great separation of storage and compute, excellent for long-term retention and complex SQL analytics across mixed datasets. Snowflake's multi-cluster warehouses simplify concurrency at the cost of predictability for bursty ingest and small ad-hoc queries unless carefully configured.

What observability workloads demand

Observability data is different from batch analytics. Key characteristics:

• High write throughput — logs and traces generate millions of small records per second during spikes.
• Low ingestion latency — SREs expect near-real-time ingestion (seconds) for alerting and tracing workflows.
• High cardinality and selective queries — per-request IDs, tags, and attributes explode cardinality; queries often filter on a small subset and must return quickly.
• Retention and compaction — hot short-term data and colder long-term archives with different cost targets.

Ingestion: pipelines and latency

ClickHouse ingestion patterns

ClickHouse supports fast HTTP/Native protocol inserts, Kafka consumers, and materialized views for real-time rollups. For observability the common pattern is Kafka → ClickHouse (buffering) or an agent (vector, fluent-bit) writing directly to ClickHouse’s HTTP endpoint with batching.

# ClickHouse schema (example)
CREATE TABLE logs (
  timestamp DateTime64(3),
  service String,
  level String,
  trace_id String,
  message String
) ENGINE = MergeTree()
PARTITION BY toYYYYMM(timestamp)
ORDER BY (service, toDate(timestamp));

# Simple HTTP insert (JSONEachRow)
curl -sS 'http://clickhouse:8123/?query=INSERT%20INTO%20logs%20FORMAT%20JSONEachRow' \
  -d '{"timestamp":"2026-01-01 12:00:00.123","service":"api","level":"ERROR","trace_id":"...","message":"x"}'

Real-world results: with properly tuned MergeTree settings, buffer tables, and batched inserts we routinely see sub-second end-to-end ingestion for high-throughput agents. ClickHouse’s in-place compression also reduces network and storage pressure during bursts.

Snowflake ingestion patterns

Snowflake supports multiple pipelines: streaming ingest (Snowpipe Streaming), continuous loading via Snowpipe (file-based), and batch COPY INTO from cloud storage. For observability, teams commonly use collector services to write newline-delimited JSON to S3/GCS and let Snowpipe load data, or use Snowpipe Streaming for lower-latency flows.

# Example: Snowpipe (file-based) to a table
CREATE OR REPLACE TABLE logs (
  timestamp TIMESTAMP_NTZ,
  service STRING,
  level STRING,
  trace_id STRING,
  message STRING
);

-- COPY INTO via staged files (simplified)
COPY INTO logs FROM @my_stage/logs FILE_FORMAT = (TYPE = 'JSON');

Real-world latency: Snowpipe file-based ingestion typically shows latencies from several seconds to low minutes depending on staging and file arrival patterns. Snowpipe Streaming reduces that to seconds for small events, but at scale it requires careful sizing of streaming pipelines and attention to credit usage.

Ingestion comparison — practical takeaways

• Lowest latency for bursty, small messages: ClickHouse (native or Kafka) — often sub-second with batching.
• Near-real-time managed option: Snowflake Snowpipe Streaming — seconds but can be costlier per event and depends on tenant concurrency.
• Operational note: ClickHouse gives you more knobs to control ingestion behavior (buffers, TTL, parts merging); Snowflake offloads operational complexity but moves effort to pipeline design and cost governance.

Query performance: ad-hoc, OLAP scans and point lookups

Query patterns for observability include: tailing recent logs, time-range scans with filters, group-bys for metrics, and trace joins. We'll compare how each platform behaves under those patterns.

ClickHouse query strengths

• Vectorized execution and indexes: ORDER BY + primary key and data skipping indices accelerate selective scans typical in logs.
• Materialized views: Fast rollups and pre-aggregations for metrics derived from logs and traces.
• Low-latency point and time-range queries: Designed for returning results in hundreds of milliseconds at moderate concurrency.

Snowflake query strengths

• Elastic concurrency: Multi-cluster warehouses let many concurrent analysts run heavy queries without blocking.
• Broad SQL compatibility and ecosystem: BI tools and complex joins across datasets are simpler in Snowflake.
• Auto clustering & micro-partitions: Good for backfilled analytics and large scans; performance for selective queries depends on how well micro-partitions align with query filters.

Benchmark example — reproducible method

Use this as a baseline you can reproduce. Goal: compare 95th percentile query latency for typical observability queries on 1TB of compressed logs (30-day hot window).

1. Data: generate 1 billion log events (timestamp, service, trace_id, level, message). Compress to ~1 TB logical; real compression depends on schema.
2. Ingest: ClickHouse via batched HTTP/Kafka; Snowflake via Snowpipe Streaming or stage+COPY for large batches.
3. Queries:
   • Q1 (Tail): fetch last 100 log lines for service X
   • Q2 (Selective scan): count errors for service X over last 5m grouped by instance
   • Q3 (Traces join): join trace_id indexed table to find full trace events (medium cardinality join)
4. Measure: run each query under 50, 200, 1000 concurrent clients to emulate alerting + analyst workloads; capture p50/p95/p99 latencies and CPU/credits consumed.

Observed ranges in multiple public and private tests (varies by config):

• ClickHouse: Q1–Q3 p95: 150ms–850ms at 200 concurrent clients when properly sharded and with warmed caches; p99 may spike during merges.
• Snowflake: Q1–Q3 p95: 800ms–5s depending on warehouse size (X-Small vs Large) and micro-partition pruning; concurrency handled by scaling warehouses but increases credit burn.

These are illustrative. The takeaway: ClickHouse tends to have consistently lower latency for selective, small-result queries typical in observability. Snowflake shines when you need to run large joins and cross-dataset analytics at scale with minimal ops overhead.

Cost comparison: modeling observability economics

Cost is the top decision lever for production observability. Below is a simple cost model framework — use your own inputs (ingest volume, retention policy, query concurrency) to compute totals.

Cost components to model

• Storage — compressed storage for hot and cold tiers.
• Compute — ClickHouse node-hour + instance types OR Snowflake credits for warehouse runtime.
• Ingestion credits/throughput — Snowpipe streaming and Snowflake compute during loads; ClickHouse network/IO and Kafka/agent costs.
• Operational overhead — SRE time for tuning and running the system (higher for self-managed ClickHouse, lower for managed Snowflake but non-zero for pipeline engineering).

Sample cost scenario (method + example)

Scenario assumptions (monthly): ingest 30 TB raw (6 TB compressed), 30-day hot retention (6 TB), cold-archive 90 days in cheaper object storage, average concurrent query demand that requires 16 vCPU-equivalent compute.

• ClickHouse (self-managed) model:
  • Storage: 6 TB block storage + snapshots (~$X/TB/month depending on cloud)
  • Compute: 3 x 8-vCPU nodes (for redundancy and headroom) — instance-hour cost
  • Operational: SRE FTE fraction (0.25–0.5 FTE) for tuning/maintenance
• Snowflake model:
  • Storage: managed compressed storage on Snowflake pricing (~$Y/TB/month)
  • Compute: warehouse credits consumed by queries and continuous ingestion; auto-scaling multi-cluster to handle spikes
  • Operational: lower FTE for infra, but higher for pipeline/credit governance

Expected outcome: for steady-state large volumes with predictable query patterns, ClickHouse self-hosted often yields lower raw monthly costs but higher ops burden. Snowflake typically costs more on raw compute for high-ingest, high-concurrency observability workloads but reduces operational headcount and risk, and may be cheaper if you rely heavily on ad-hoc, cross-dataset analytics.

Operational complexity and risk

Operational complexity matters as much as raw performance. Consider:

• Schema and retention management: ClickHouse requires explicit TTLs, partitioning and compaction tuning. Snowflake automates micro-partitioning and time-travel, but you must design pipelines to avoid small-file inefficiencies.
• Scaling: ClickHouse sharding and replication require capacity planning. Snowflake scales horizontally for concurrency but scaling decisions affect credits used.
• Resilience and backups: Managed ClickHouse Cloud abstracts much of the complexity; self-managed deployments must design for repair after merges/replicas loss. Snowflake’s managed service gives strong SLAs and easy failover across regions.
• Observability-specific features: ClickHouse supports TTLs, materialized views and low-latency merges suited to logs. Snowflake supports data governance, row/access policies and direct integration with BI and ML tooling.

When to choose ClickHouse for observability

• You need sub-second ingestion and query latency for sharded, high-cardinality logs/traces.
• You want tight control over compression, partitioning and TTLs to optimize storage cost at scale.
• You have SRE capacity to manage a distributed OLAP cluster, or you can use ClickHouse Cloud to reduce ops work.
• Your workload includes many small, selective queries (tailing, trace lookups) where low p95 latency matters.

When to choose Snowflake for observability

• You prefer a fully managed service that reduces infrastructure toil and provides easy cross-dataset analytics.
• Your observability data is combined with business/third-party data for complex joins and machine learning workflows.
• You can tolerate seconds of ingestion latency (or you design Snowpipe Streaming carefully) in exchange for lower hands-on ops.
• You value global replication, strong data governance, and a mature partner ecosystem for downstream analytics.

Hybrid approaches and pragmatic patterns

Many modern observability stacks use a hybrid approach that gets the best of both worlds:

• Hot path in ClickHouse: Keep the last 7–30 days of hot logs and traces in ClickHouse for sub-second troubleshooting and alerting.
• Cold path in Snowflake: Archive older data to Snowflake (or cloud object storage warmed by Snowflake) for long-term trend analysis, compliance and ML training.
• Rollups and export: Use ClickHouse materialized views to generate daily rollups that are exported to Snowflake for cross-team analytics and BI access.

Example pipeline diagram (ASCII)

Collectors (fluent-bit/vector) --> Kafka --> ClickHouse (hot)  --daily-export-->  S3 --> Snowpipe --> Snowflake (cold)
                                                                \-- real-time alerts --> Alerting systems

Observability-specific tips for 2026

• Leverage streaming micro-batches: Batching small events (10–100ms windows) reduces per-event overhead and lowers cost on both platforms while keeping latency acceptable.
• Index your high-cardinality keys wisely: Use data-skipping indices or dedicated lookup tables for trace_id and request_id instead of naive full scans.
• Use adaptive retention: Keep raw events short-term, roll up to metrics and sample traces for long-term storage.
• Automate cost governance: For Snowflake, use Resource Monitors and automated warehouse suspension. For ClickHouse Cloud or self-managed, implement autoscaling scripts and alerting for runaway merges/queries.
• Integrate observability with CI/CD: Test your ingest and query pipelines in pre-prod against realistic synthetic traffic to catch schema drift and performance regressions early.

Real-world case study (condensed)

A mid-sized SaaS company in 2025 moved from a file→Snowpipe only workflow to a hybrid approach. Problem: alert investigators experienced 5–20s delays on tail queries and Snowflake credits spiked during incident response. Solution: deploy ClickHouse for a 14-day hot window and keep Snowflake for long-term analysis. Result: mean time to resolution (MTTR) dropped 40% for production incidents and monthly bill predictability improved due to lower ad-hoc warehouse usage. (Company data anonymized.)

Implementation checklist: evaluate in your environment

1. Define realistic ingest and query workloads (events/sec, retention, concurrency).
2. Run a reproducible benchmark: ingest a representative dataset and run tail/select/join queries under concurrency.
3. Model costs with conservative assumptions: add operational FTE and pipeline costs.
4. Prototype a hybrid path: hot ClickHouse + cold Snowflake export and measure MTTR impact.
5. Plan governance: automated TTLs, access controls, and cost monitors per environment.

Final verdict: choose based on SLA, ops appetite and analytics scope

There is no one-size-fits-all winner. If your top priority is ultra-low-latency ingestion and selective query performance for high-cardinality observability data, ClickHouse (self-hosted or managed) is the performance-first choice in 2026. If your priority is operational simplicity, elastic concurrency for cross-dataset analytics, and integrated governance, Snowflake is the safer pick — especially if you already use Snowflake for analytics.

The pragmatic path for many teams is hybrid: use ClickHouse for immediate incident response and Snowflake for historical analysis, ML and BI. That combination addresses the dominant observability pain points in modern distributed systems: latency, cost spikes during incidents, and the need for long-term analytics.

Actionable next steps

• Run a small-scale benchmark this week: ingest 10M events and run the three queries described earlier.
• Estimate monthly cost using your cloud pricing and the cost components above; include a 25–50% buffer for incident spikes.
• If you operate with limited SRE bandwidth, pilot ClickHouse Cloud to reduce ops while keeping performance gains.

Resources and further reading

• ClickHouse docs: ingestion, MergeTree tuning and materialized views
• Snowflake docs: Snowpipe, Snowpipe Streaming and resource monitors
• Benchmark scripts and synthetic generator (open-source reference projects)
• Bloomberg coverage on ClickHouse funding (late 2025) for market context

Call to action

Ready to validate which platform fits your observability goals? Start with our reproducible benchmark checklist: spin up a 1M-event ingest test, run tail/select/join queries at 200 concurrent clients, and share the results with your SRE and finance teams. If you want a template or help designing the benchmark for your environment, reach out — we help engineering teams run side-by-side evaluations and convert results into procurement-grade cost models.

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