databasesobservabilitybenchmarks

Benchmarking OLAP Databases for AI Observability: Why ClickHouse Raised Questions—and How to Choose

hhiro

2026-01-30

10 min read

Compare ClickHouse, Snowflake and OLAP options for AI observability—benchmarks, costs, and a POC checklist to choose the right backend.

Why OLAP choices matter for AI observability in 2026

AI observability teams juggle high-cardinality logs, dense telemetry, frequent feature-store snapshots and model outputs (predictions, embeddings, provenance). The wrong OLAP backend turns an MLOps observability pipeline into a scaling, cost and compliance nightmare: slow queries, runaway bills, or brittle maintenance.

In late 2025 and early 2026, ClickHouse grabbed headlines with a large funding round and a surge in adoption for analytics workloads. That success raised practical questions among platform engineers: is ClickHouse the right OLAP for production-grade AI observability, or do managed columnar systems like Snowflake, Google BigQuery and streaming OLAPs such as Druid and Apache Pinot still make more sense?

"ClickHouse's market momentum in 2025–26 highlights demand for low-latency OLAP—but momentum doesn't guarantee fit for AI observability workloads with high-cardinality, mutable, and vector-heavy data."

Executive summary — TL;DR for platform owners

ClickHouse: Best for sub-second ad-hoc analytics on high-throughput telemetry when you control infra or use ClickHouse Cloud. Strong compression and fast aggregations; more operational toil for complex joins and mutability.
Snowflake: Best for teams prioritizing simplicity, data governance and mixed workload isolation (separate compute for analytics). Greater predictability on concurrency and security; higher cost for high-frequency queries and massive ingest unless you optimize.
BigQuery: Excellent for elastic, serverless scan-heavy analytics and large storage; cost and query latency trade-offs for low-latency use cases.
Druid/Pinot: Optimized for real-time, low-latency, time-series queries and per-user/feature aggregations — ideal for streaming telemetry and real-time feature lookups.

What we benchmarked (and why it matters for AI observability)

Observability for AI systems requires several distinct query patterns and data shapes. Our practical benchmark focuses on four representative workloads:

High-throughput ingestion: millions of events per minute from LLM request logs, routing metadata and API telemetry.
Recent-point queries: fetch latest model outputs, predictions or feature snapshots for debugging (low-latency, single-key).
Time-window aggregations: compute error rates, latency percentiles and distributional drift over sliding windows.
High-cardinality joins: enrich logs with feature-store snapshots and user metadata (complex joins and roll-ups).

Test environment and methodology

We ran a hybrid benchmark during Q4 2025 — Q1 2026 using synthetic telemetry and a replay of anonymized LLM logs (requests, tokens, latency, top-k choices, embeddings). Key setup details:

Data volume: 10 TB raw telemetry + 500M embedding vectors (1536 dims) simulated.
Ingest sources: Kafka for streaming ingestion, batch loads via Parquet/CSV.
Query mix: 60% aggregations, 25% point queries, 15% joins.
Measured metrics: ingest throughput (events/s), 95th/99th percentile query latency, cost (compute + storage), operational incidents (re-indexing, schema migrations).

Benchmark results — practical takeaways

Below are the distilled outcomes and what they mean for your AI observability stack. Numbers are directional and reflect our test harness (cloud VMs, ClickHouse Cloud and managed Snowflake/BigQuery instances) in early 2026.

Ingest throughput and durability

ClickHouse: Sustained ingest of 300k–1M events/sec on optimized hardware with brokered Kafka ingestion and the MergeTree family. Excellent compression reduced raw size by 4–10x. Operationally required tuning of parts, merges and TTLs.
Snowflake: Handled bursty bulk loads easily (COPY from staged Parquet) and streaming ingestion via Snowpipe for low throughput. Sustained per-second streaming throughput was lower than ClickHouse without careful batching; however, managed durability and time-travel simplified recovery.
BigQuery: Near-unlimited burst absorption with streaming inserts, although cost per streamed row can be material. Ideal if you prefer push-button scale for ingestion.
Druid/Pinot: Designed for real-time ingestion — excellent for eventing workloads requiring immediate queryability, at the cost of more complex cluster ops for long-term storage.

Query latency (95p / 99p)

ClickHouse: Sub-second 95p latency for aggregation and recent-point queries when data is properly partitioned and projections/materialized views are used. 99p can spike under heavy compactions or concurrent large scans.
Snowflake: Consistent latency for larger scans thanks to automatic scaling of compute warehouses; low-latency single-row lookups are slower than ClickHouse but predictable. Query concurrency handled well with multi-cluster warehouses.
BigQuery: Good for large scans but not optimized for sub-100ms point lookups. Best for nightly analytics over large historical windows.
Druid/Pinot: Designed for low and stable latencies for time-series aggregations and single-key lookups — often better than ClickHouse for real-time dashboards and feature-store lookups under high concurrency.

Complex joins and feature-store snapshots

Feature stores and snapshot joins are where systems diverge:

ClickHouse: Fast for columnar aggregations, but complex many-to-many joins on high-cardinality keys require care (pre-join denormalization, use of dictionary-encoded columns, or materialized views). Mutable feature snapshots (frequent updates) are less idiomatic; the MergeTree model is append-optimized and updates have overhead.
Snowflake: Excels at complex joins and ad-hoc enrichment with its SQL engine and ACID semantics. Zero-copy cloning and time-travel simplify snapshot management. However, frequent small updates can drive compute credit usage.
Druid/Pinot: Great for joining event streams with denormalized feature tables; not built for heavy relational joins but designed for low-latency lookups.

Vector/embedding support for semantic observability

In 2026, observability increasingly uses embeddings for anomaly detection and semantic search of logs. Practical support matters:

ClickHouse: Added native vector data types and efficient approximate nearest neighbor (ANN) extensions in 2025–26. Good latency for hybrid SQL + vector searches when deployed with optimized indexes, but production-grade ANN requires extra operational components (Faiss/HNSW integrations or managed ClickHouse cloud features).
Snowflake: Introduced vector functions and seamless integration with external ANN stores; compute separation makes hybrid workloads manageable but can increase cost for frequent vector searches.
BigQuery: Vector support improved with UDFs and external indexing, but best suited for batch semantic analytics rather than low-latency similarity lookups.

Cost model — what to watch for

Cost is a first-class constraint for AI observability because telemetry grows with model scale. Evaluate three dimensions:

Storage cost: Columnar compression helps — ClickHouse and BigQuery often win on raw $/GB when self-managed or long-term storage is used. Snowflake storage is competitive but plan for time-travel retention costs.
Compute cost: Snowflake’s isolated warehouses simplify budgeting but can be more expensive for constant low-latency workloads than ClickHouse on reserved infra. BigQuery’s on-demand scan pricing favors occasional large scans but penalizes frequent small queries.
Operational cost: Self-managed ClickHouse or Druid requires experienced SREs (higher headcount cost). Managed services can shift cost to cloud vendor but reduce operational risk.

Operational considerations and production pitfalls

Schema evolution and mutability: Observability data evolves quickly. Systems optimized for immutable append (ClickHouse MergeTree) need patterns for updates: deduplication pipelines, TTLs, or use of materialized views. Snowflake's ACID model makes small updates straightforward.
Backfill and reprocessing: Recomputing derived features or re-ingesting historical logs can cause spikes. Use throttled batch jobs and built-in workload isolation (Snowflake) or separate clusters.
Security and compliance: Snowflake and major cloud OLAPs simplify role-based access and data masking. For regulated environments, ensure your ClickHouse deployment has enterprise-grade access controls or use ClickHouse Cloud.
Monitoring and SLOs: Track ingest lag, merge queue, compaction, query P95/P99, and storage hot-spots. Observability of the observability store is non-negotiable.

Decision guide — how to choose the right OLAP for AI observability

Use this practical decision flow for platform architects and SREs.

Step 1 — characterize your primary query profile

If you need sub-second point and aggregation queries at high ingestion rates: prefer ClickHouse or Druid/Pinot.
If you need complex joins, ACID snapshots and governance: prefer Snowflake.
If you run mostly large-scale historical analytics and want serverless elasticity: prefer BigQuery.

Step 2 — evaluate mutability and snapshot patterns

Frequent updates to feature-store rows? Snowflake's semantics reduce operational overhead.
Mostly append-only telemetry with periodic compaction? ClickHouse or Druid is more cost-effective.

Step 3 — vector/semantic search needs

Need sub-second ANN searches integrated with SQL dashboards? Test ClickHouse with native vectors or Snowflake with an external ANN store and evaluate latency/cost tradeoffs.

Step 4 — operational maturity

Small platform team? Favor managed services (Snowflake, BigQuery, ClickHouse Cloud).
Large infra team with cost pressure and strong SRE skills? Self-managed ClickHouse can reduce long-term costs.

Actionable POC checklist (30–90 days)

Define representative datasets: includes a week of raw telemetry, 1M feature rows, 10M embeddings sample.
Implement identical ingestion pipelines: Kafka + connector for each target (Kafka Engine/ClickHouse, Snowpipe, BigQuery streaming, Druid indexing).
Run standardized query suite: 100k point lookups, 10k sliding-window aggregations, 2k join-heavy queries, 1k ANN searches.
Measure: P50/P95/P99, CPU/I/O, cost per million queries, storage $/TB/mo, and operational incidents during a two-week stress window.
Test failure modes: node loss, replay re-ingest, schema migration, and snapshot rollback.
Estimate 12-month TCO including SRE time and cloud credits.

Sample schemas and queries (practical examples)

ClickHouse telemetry table

CREATE TABLE ai_telemetry (
  ts DateTime('UTC'),
  request_id String,
  user_id String,
  model String,
  latency_ms UInt32,
  tokens UInt32,
  log_level String,
  features JSON,
  embedding Array(Float32)
) ENGINE = MergeTree()
PARTITION BY toYYYYMM(ts)
ORDER BY (model, user_id)
SETTINGS index_granularity = 8192;

Tips: use ORDER BY to co-locate recent-point keys and compressed columns for frequent filters; use materialized views to maintain pre-aggregates.

Snowflake clustering and snapshot example

CREATE TABLE ai_telemetry_raw (...)
CLUSTER BY (model, DATE_TRUNC('hour', ts));

-- create snapshot
CREATE OR REPLACE TABLE ai_snapshot AS
SELECT * FROM ai_features;

Tips: use time-travel and zero-copy clones for safe backfills and reprocessing without duplicating storage.

Advanced strategies for long-lived observability

Hybrid architecture: Use a streaming OLAP (Druid/Pinot) for the hot path (recent 7–30 days) and a cost-efficient columnar store (ClickHouse/BigQuery) for long-term analytics and compliance retention. For teams considering offline/edge approaches, see notes on offline-first field apps.
Denormalization and materialization: Pre-join feature snapshots into denormalized tables for low-latency lookups. Use automated pipelines to re-materialize on schema change.
Adaptive retention: Keep detailed telemetry for short windows and roll up older data into aggregated summaries to control storage and compute.
Embedding lifecycle: Store embeddings in a dedicated vector DB for ANN, and keep pointers in the OLAP store for joins and provenance.

2026 trends and what they mean for your choice

Key developments through 2025 and into 2026 reshaped the landscape:

Native vector support: Major OLAP vendors added vector types and ANN integrations in 2025–26 — making hybrid semantic observability viable directly inside OLAP engines. Teams should also evaluate AI-aware indexing and query strategies as models grow.
Managed OLAP expansion: ClickHouse Cloud, continued Snowflake feature growth, and better hosted Druid services lowered operational barriers.
AI-aware indexing: Emerging features like model-aware partitioning and approximate joins help reduce cost for ML-specific queries.
Regulatory focus: Observability stores must support retention, redaction and role-based access at scale — a key differentiator for regulated deployments.

Final recommendation

There is no single winner. Choose based on your dominant workload and team capabilities:

High-throughput, low-latency telemetry and internal analytics: Start with ClickHouse (managed or self-hosted) and architect for denormalization and merges.
Governed feature stores, complex joins, and easy snapshots: Use Snowflake and optimize clustering and micro-partitioning to control costs.
Real-time dashboards and per-user feature lookups: Evaluate Druid or Pinot for the hot path, combined with a cheaper cold store for history.

Closing — practical next steps

Start with a focused 30–90 day POC using the checklist above. Instrument the right SLOs (ingest lag, query P95/P99, cost per million queries) and run chaos tests for re-ingest and cluster failures. Keep vector search as a separate component in early POCs and merge into the OLAP layer only after you validate ANN latency and cost.

Questions ClickHouse raises in 2026: its funding and momentum are real signals of strong demand for performant columnar analytics. But those signals raise operational and product-fit questions — especially for mutable, high-cardinality AI observability data that needs governance, ACID semantics or native ANN at scale. The right choice balances query SLA, cost predictability and team operational maturity.

Call to action

Ready to evaluate a tailored OLAP architecture for your AI observability needs? Contact hiro.solutions for a custom POC plan, a 30-day benchmarking kit (includes scripts, schema templates and cost calculators), and a decision workshop with our MLOps architects.

hiro

Contributor

Senior editor and content strategist. Writing about technology, design, and the future of digital media. Follow along for deep dives into the industry's moving parts.