Why ClickHouse Is Attracting a $15B Valuation: Technical and Market Breakdown for Dev Teams

Hook: If your telemetry pipeline is slow, bloated, or costing more than your product team expects, this matters

Teams running micro apps and high-cardinality telemetry are wrestling with three persistent problems: unpredictable ingest costs, slow query times for exploratory analytics, and complexity when tearing down old data without wrecking dashboards. In early 2026 ClickHouse raised $400M at a $15B valuation, signaling something important for analytics stacks: open-source, columnar OLAP engines that optimize for high-ingest, low-latency queries are suddenly prime infrastructure bets. This article explains the technical reasons behind ClickHouse's valuation, how it compares to Snowflake, and precisely what that means for Dev teams building telemetry platforms for micro apps.

The 2026 context: Why this funding round matters to engineering teams

In late 2025 and early 2026 the analytics market continued its shift toward cloud-native, cost-sensitive telemetry stacks. ClickHouse's funding round led by Dragoneer (reported Jan 2026) and the jump to a $15B valuation reflects two trends:

• Consumption patterns: engineering teams want predictable per-node costs, efficient ingestion, and cheaper long-term storage for high-volume telemetry.
• Open-source adoption: enterprises want control—mixing managed ClickHouse Cloud or self-hosted clusters lets teams avoid lock-in and tune performance.

High-level technical strengths of ClickHouse (what engineers care about)

For telemetry—millions of tiny events per second, lots of tags and dimensions—the following ClickHouse features are critical:

• Columnar storage + vectorized execution: Data is stored by column and processed in vectors, which yields CPU efficiency for aggregate-heavy queries (time series rollups, top-k, percentiles).
• MergeTree family: The core storage engine (and its derivatives) combines fast ingest with background compaction for efficient read performance and high throughput writes.
• Rich compression codecs: Lowers storage costs for telemetry where many columns have repeated values (status codes, service names). ClickHouse supports LZ4, ZSTD, and custom codecs per column.
• Materialized views and low-latency transformations: You can do near-real-time rollups and pre-aggregations on ingest (useful for dashboards and alerting).
• Native Kafka and streaming integrations: Kafka engine + materialized views make ingest pipelines simpler and more resilient.
• Tiered and object storage integration: Cold data can be offloaded to S3-compatible stores with table TTLs, cutting long-term costs without losing queryability.

Example: Why columnar + codecs matter for telemetry

Telemetry rows are wide and sparse: timestamp, service, endpoint, user_id, session_id, tags... Columnar compression often reduces disk footprint by 5–20x compared with row storage because identical or low-cardinality values compress very well. That reduces both storage costs and I/O during queries.

How ClickHouse differentiates from Snowflake—engine mechanics and operational tradeoffs

Both ClickHouse and Snowflake are used for OLAP workloads, but they have different design tradeoffs that matter to micro app telemetry:

• Architecture
  • ClickHouse: primarily a compute-and-storage on the same nodes model for self-hosted, but with ClickHouse Cloud implementing managed separation and tiering. It offers tight control over resources and node-level tuning.
  • Snowflake: a fully-managed, multi-cluster, shared-data architecture with strict separation of storage and compute, and sophisticated multi-tenant isolation for concurrency.
• Ingest and latency
  • ClickHouse: excellent for sustained high-ingest scenarios and real-time queries when you architect with MergeTree/Kafka engines and small file compaction tuning.
  • Snowflake: ingestion via Snowpipe and bulk loading is robust, but per-row latency and very high sustained ingest rates can be more expensive and sometimes slower to appear in queries.
• Cost model
  • ClickHouse: cost is predictable when self-hosted (VM + storage) or more cost-efficiently scaled on ClickHouse Cloud because heavy writes and retention are cheaper per TB.
  • Snowflake: pay-per-second compute can be great for bursty ad-hoc analytics but becomes expensive when you have constant, high-volume queries and continuous ingestion. Keep an eye on major cloud pricing moves—see commentary on per-query cost caps in the market.
• Concurrency
  • ClickHouse: high single-query throughput; concurrency needs careful design (read replicas, query routing, and resource pools) but works well for telemetry dashboards built with pre-aggregates.
  • Snowflake: built-in multi-cluster scaling for concurrency—suitable for many BI users hitting the system simultaneously without cluster tuning.
• SQL features and ecosystem
  • ClickHouse: fast, ANSI-ish SQL with extensions for OLAP functions. Increasingly supports nested types and advanced window functions. Strong OSS ecosystem and many connectors.
  • Snowflake: richer SQL features for data engineering workloads (time travel, data sharing), strong governance and marketplace integrations—areas enterprises watch closely as rules and governance evolve (regulatory and governance guidance).

Bottom line: Which is better for micro app telemetry?

If your workload is high-cardinality telemetry, sustained high ingest (many small events per second), and you need predictable, low costs for long retention windows with frequent queries, ClickHouse often provides better price-performance. If your org values a hands-off managed service with heavy BI concurrency and advanced data-sharing features, Snowflake can be a better fit.

Concrete architecture patterns for micro app telemetry (patterns, configs, and tradeoffs)

Here are three proven architectures for telemetry stacks and where ClickHouse or Snowflake fit.

1) Real-time telemetry pipeline (strict low-latency)

Goal: dashboards and alerts reflect events within seconds.

1. Instrument services with OpenTelemetry or lightweight loggers.
2. Use a delivery agent (Vector, Fluentd, or Telegraf) to convert events to JSON/AVRO and forward to Kafka.
3. On the analytics side, use ClickHouse Kafka engine + Materialized Views to consume, transform, and write into MergeTree tables optimized for queries.

Sample ClickHouse table and Kafka ingestion:

CREATE TABLE telemetry_raw (
  ts DateTime64(3),
  svc String,
  endpoint String,
  user_id UUID,
  session_id String,
  status UInt16,
  payload String
) ENGINE = Kafka SETTINGS kafka_broker_list = 'kafka:9092', kafka_topic = 'telemetry', kafka_group = 'ch_grp';

CREATE MATERIALIZED VIEW telemetry_mv TO telemetry (
  ts, svc, endpoint, user_id, session_id, status
) AS SELECT ts, svc, endpoint, user_id, session_id, status FROM telemetry_raw;

Then consume into a MergeTree with indexes and TTLs:

CREATE TABLE telemetry (
  ts DateTime64(3),
  svc String,
  endpoint String,
  user_id UUID,
  session_id String,
  status UInt16
) ENGINE = MergeTree()
PARTITION BY toYYYYMM(ts)
ORDER BY (svc, endpoint, ts)
TTL ts + INTERVAL 90 DAY TO DISK 's3://my-bucket/cold' SETTINGS storage_policy='hybrid';

2) Cost-sensitive long-retention analytics

Goal: Keep 365+ days of telemetry without exploding costs.

• Use ClickHouse's tiered storage and TTL to move older data to S3 or Glacier-equivalent while keeping metadata locally for fast queries (and watch cloud storage pricing and per-query caps like those discussed in industry pieces).
• Implement periodic downsampling: keep raw events for 7–30 days, then rollup to hourly aggregates for longer retention.

3) BI-first and multi-team sharing

Goal: many analysts with ad-hoc queries and complex joins across multiple datasets.

• Snowflake shines here with managed concurrency, secure data sharing, and governance features.
• Hybrid option: store raw telemetry in ClickHouse for real-time needs and push curated datasets to Snowflake for cross-domain analytics and data sharing.

Performance tuning tips for ClickHouse when handling telemetry

Actionable knobs to tune for real-world telemetry workloads:

• ORDER BY: Choose ordering columns that match your common query patterns (time + service + endpoint). That improves range scans and merges.
• Compression codecs: Apply CODEC(ZSTD(level)) for low-cardinality columns, and Delta codecs for monotonically increasing numeric columns.
• MergeTree settings: Adjust background merge throttling and parts size to balance ingest latency and query performance.
• Use materialized views for pre-aggregations (daily/hourly rollups) to avoid scanning raw data for dashboards.
• Leverage projections (ClickHouse feature) for pre-joined or pre-sorted subsets that drastically speedup common queries.
• Partitioning: Partition by month/day depending on retention and query intervals to speed up deletes and TTLs.

Example: Pre-aggregation materialized view

CREATE MATERIALIZED VIEW telemetry_rollup
TO telemetry_rollup_table
AS
SELECT
  toStartOfMinute(ts) AS minute,
  svc,
  endpoint,
  count() AS events,
  uniqExact(user_id) AS unique_users
FROM telemetry
GROUP BY minute, svc, endpoint;

Cost comparison and decision checklist (2026 realities)

Every org's numbers differ, but these guidelines reflect 2026 market realities:

• Predictable heavy ingest + long retention → ClickHouse (self-hosted or ClickHouse Cloud) tends to be cheaper per TB and more efficient for write-dominant workloads.
• Burst, ad-hoc BI queries and many concurrent analysts → Snowflake can reduce ops burden and provide predictable concurrency management.
• Hybrid approach → Put hot telemetry in ClickHouse for real-time tools; push curated datasets to Snowflake for BI and data sharing.

Real-world case: Micro app telemetry at scale (fictional, representative)

Imagine a SaaS vendor with 120 microservices producing 600K events/sec and 10TB/day of raw telemetry. They need 30-day raw retention and 1-year downsampled retention. The team evaluated Snowflake and ClickHouse. Results after a 30-day benchmark:

• ClickHouse (self-hosted, optimized): sustained ingest of 600K/s across a 12-node cluster, median query latency for common dashboards under 300ms, predictable monthly infra spend from reserved instances + S3, and 70% lower storage cost after tiering.
• Snowflake: ingestion required multiple warehouses and Snowpipe configuration; interactive ad-hoc queries were fast but monthly cost scaled quickly due to constant micro-billing for compute during continuous ingestion and frequent refreshes.

The team's outcome: ClickHouse became the real-time backend; they exported daily rollups to Snowflake for cross-team analytics and governance.

Operational considerations and vendor risk

Valuation aside, teams must plan for operational tradeoffs:

• Self-hosted ClickHouse provides control and cost wins but requires operations expertise (cluster management, upgrades, failure modes).
• ClickHouse Cloud reduces ops load while preserving cost efficiency for heavy telemetry—this is a fast-growing market trend in 2026 and part of why investors value ClickHouse highly.
• Snowflake managed service shifts operational responsibility but can create cost unpredictability under constant ingest.
• Hybrid lock-in risk: moving data between systems (ClickHouse ↔ Snowflake) adds pipeline complexity and egress costs—design an extract/transform schedule, not continuous replication, unless justified by business needs.

Benchmarks and testing: How to validate for your use case

Don't trust vendor slides. Run these tests before deciding:

1. Capture a representative day's telemetry and replay it (Vector/Fluentd → Kafka) at production rates against a test cluster. See software verification and Vector coverage for operational notes: Vector verification guidance.
2. Measure ingestion latency, query tail latency (p95, p99), and storage growth after compaction for 7–30 days.
3. Run concurrency tests for real dashboard loads and synthetic analyst queries.
4. Estimate 12-month costs for infrastructure, egress, and staff time under both ClickHouse (self-hosted/Cloud) and Snowflake models.

2026 Future predictions and strategic recommendations

Based on 2025–2026 product trends and vendor investments, here’s what to expect and what to do:

• ClickHouse will continue to invest in managed cloud features (automated tiering, serverless query options, and tighter integrations with observability tooling). Expect new features that reduce ops overhead while preserving cost advantages.
• Snowflake will expand native ingestion and unstructured data tooling, doubling down on data governance, marketplace, and ecosystem partnerships.
• Recommendation: Start with data-driven proof-of-concept. For micro app telemetry, benchmark ClickHouse first for raw-store use and use Snowflake only for cross-domain BI if needed.

Actionable checklist for teams (start implementing today)

• Prototype: Run a 2-week ingest+query benchmark with your real telemetry sample.
• Schema: Use MergeTree with ORDER BY (service, endpoint, ts). Add projections and materialized views for common queries.
• Retention: Implement TTL + tiered storage; downsample after N days into an hourly or daily rollup table.
• Monitoring: Monitor ClickHouse system tables (system.metrics, system.parts) and track p95/p99 query time.
• Cost model: Compare monthly VM/S3 cost vs Snowflake compute estimates using your query mix and ingest patterns.

Final assessment: Why investors and engineering teams are betting on ClickHouse

ClickHouse's 2026 valuation reflects strong adoption across companies needing fast, cheap analytics at scale. For micro app telemetry, ClickHouse aligns with core engineering priorities: high write throughput, low-latency OLAP queries, tight control over cost and retention, and an increasingly mature cloud offering to reduce operational overhead. Snowflake remains a powerful option where multi-tenant BI, governance, and hands-off management are top priorities—often the two tools are complementary.

Practical takeaway: For real-time telemetry and predictable long-term costs, benchmark ClickHouse first. Use Snowflake where cross-domain data sharing and multi-analyst concurrency are the dominant needs.

Call to action

If you're planning your telemetry stack for 2026, start with a focused benchmark: ingest a representative sample, run your top 10 dashboard queries, and compare latency and monthly TCO between ClickHouse (self-hosted or ClickHouse Cloud) and Snowflake. Need a jumpstart? Contact webdevs.cloud for a tailored benchmark plan, or spin up a ClickHouse proof-of-concept using the example schemas above and measure real results in 48 hours.

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