Using ClickHouse as a Real-Time Feature Store for LLMs

Hook: Why your LLM pipeline needs a different feature store — and why ClickHouse makes sense in 2026

If you manage LLMs in production, you already know the pain: features and embeddings must be served with millisecond tails, telemetry must be available for drift detection, and the cost of maintaining separate systems for vectors, time-series telemetry and analytics is exploding. You want a single system that can store embeddings, join them with user/session features, run low-latency similarity queries, and also support high-cardinality analytical queries for monitoring — without paying license fees that scale linearly with throughput.

In 2026, ClickHouse is emerging as a practical answer to that challenge. With major funding momentum (a $400M round led by Dragoneer in late 2025) and steady product investments in vector support, materialized projections and cloud-managed deployments, ClickHouse is positioning itself as a low-latency OLAP-backed feature store alternative for LLM pipelines. This article is a hands-on guide for architects and engineers: when to use ClickHouse, how to implement a real-time feature store that serves embeddings and telemetry, and patterns to achieve millisecond-serving SLAs.

The case for ClickHouse as a feature store in 2026

Before we get tactical, here are the core reasons ClickHouse is worth evaluating for LLM feature storage in 2026:

• OLAP performance at scale: ClickHouse’s columnar engine (MergeTree family and successors) is optimized for high-throughput analytical scans and late-2025/early-2026 improvements reduced query latencies on point lookups and narrow scans — a good fit for feature joins and telemetry queries.
• Vector and similarity capabilities: Recent releases and community extensions have prioritized efficient storage of embeddings (Array/FixedString) and SQL-level vector operations. That lets you compute similarities inside the database or combine ClickHouse with an external ANN engine.
• Real-time ingestion and streaming integration: Native Kafka/stream engines and high-concurrency inserts make it trivial to stream events and new embeddings into tables that then feed model-serving layers.
• Operational maturity & funding signal: The Dragoneer-led investment in late 2025 accelerated cloud products and ecosystem tooling. For enterprises, that funding signals longevity and aggressive roadmap delivery.
• Cost and consolidation: Consolidating telemetry, features and some vector serving into ClickHouse can reduce integration complexity and cost vs maintaining separate vector DBs + analytics stores.

When ClickHouse shines — and when it doesn’t

• Use ClickHouse when you need low-latency feature joins, time-windowed analytics, and the ability to serve many concurrent analytical queries alongside vector lookups.
• Prefer a hybrid approach when you need sub-millisecond, billion-vector exact ANN at very high QPS — specialized vector stores (with GPU acceleration) may still be better for pure ANN serving.
• For strict regulatory isolation and custom hardware (e.g., GPU-based vector indexes on-prem), consider ClickHouse as the metadata + telemetry store and keep ANN on an optimized local service.

Practical architecture patterns

Below are three pragmatic patterns you can adopt depending on requirements for latency, vector scale and analytics complexity.

1) Single-DB feature store (embeddings + features + telemetry)

Best for: moderate vector scale (millions — low tens of millions), strong need to correlate embeddings with rich telemetry/aggregates in SQL.

1. Store embeddings as Array(Float32) or packed FixedString blobs, alongside feature columns and event timestamps.
2. Use MergeTree with an ORDER BY that optimizes your common access patterns (user_id, event_time) and partition by date for TTL.
3. Create materialized views or projections for common precomputed joins (e.g., latest user features, session aggregates) so single SQL requests return all features for a model call.

2) Hybrid feature store + ANN

Best for: large vector collections (tens/hundreds of millions) where specialized ANN (Milvus/FAISS/Weaviate) handles nearest-neighbor while ClickHouse hosts features, metadata and telemetry.

1. Keep the ANN index in a purpose-built vector engine; store vector metadata and a compact embedding in ClickHouse for re-ranking and feature joins.
2. Query ANN for nearest candidate ids, then run a single ClickHouse SQL query with an IN(...) filter to fetch features and telemetry to assemble the prompt.
3. Leverage ClickHouse's extremely fast IN-join or dictionary lookups to keep the second-stage retrieval sub-10ms for small candidate sets.

3) Analytics-first (monitoring + feature enrichment pipeline)

Best for: heavy model telemetry, drift detection and offline feature computation alongside online serving.

1. Stream model logs, latencies, and predictions into ClickHouse using the Kafka engine.
2. Build materialized views for rolling aggregates (p99 latency over last 1h, feature drift scores by cohort) and feed alerts to MLOps controllers.
3. Use SQL-based window functions to compute online feature normalizations and expose those through a read-optimized table for serving.

Concrete implementation: a sample schema and queries

The example below shows a minimal, practical schema for storing embeddings, user features and telemetry with MergeTree. The SQL is ClickHouse-flavored; validate exact function names for vector ops on your ClickHouse version before running in prod.

1) Create the core table

CREATE TABLE llm_features (
  id UUID,
  user_id UUID,
  embedding Array(Float32),
  features JSON, -- or separate typed columns for low-latency access
  last_updated DateTime64(3),
  version UInt32
) ENGINE = MergeTree()
PARTITION BY toYYYYMM(last_updated)
ORDER BY (user_id, last_updated);
  

Notes:

• Use typed columns for hot features (booleans, ints, numerics) instead of storing everything in JSON if you expect high QPS joins.
• Consider FixedString(512) or a compressed binary codec for embeddings if you prefer compact storage; ClickHouse offers per-column codecs.

2) Materialized view for latest user features

CREATE MATERIALIZED VIEW latest_user_features
TO latest_user_features_table AS
SELECT user_id, anyHeavy(embedding) AS embedding, anyHeavy(features) AS features, max(last_updated) AS last_updated
FROM llm_features
GROUP BY user_id;
  

This ensures a single-row-per-user read pattern for serving — ideal for low-latency lookups.

3) Candidate retrieval (simple vector re-rank in SQL)

Given a query embedding, retrieve top-N candidates and their features. The example uses a generic dot-product / norm pattern — confirm the exact vector functions available in your ClickHouse version (names vary across releases).

-- pseudo-SQL: compute cosine similarity and return top 10
SELECT id, user_id, features,
  dotProduct(embedding, query_embedding) / (norm(embedding) * norm(query_embedding)) AS sim
FROM latest_user_features_table
ORDER BY sim DESC
LIMIT 10;
  

Real deployments often use a two-stage pattern: a fast ANN call to get ~100 candidates, then a ClickHouse query to re-rank and attach stable features for the LLM prompt.

Ingestion & streaming best practices

• Use the Kafka table engine for durable, high-throughput ingestion of events and embeddings. Materialized views can consume Kafka topics and write into MergeTree tables.
• Batch inserts: Even for real-time flows, collect micro-batches (50–500 rows) to amortize compression and lock overhead.
• Timestamps & TTL: Keep precise timestamps (DateTime64) and use TTL to expire raw telemetry and old embeddings to control storage costs.
• Compression codecs: Configure column codecs for embeddings (e.g., ZSTD or specialized float compression) to save disk while keeping decompression cheap at read time.

Serving patterns & integration with model servers

ClickHouse can be queried from model-serving layers in a few common ways:

• Direct SQL via HTTP/Native clients: Use ClickHouse’s native or HTTP API to issue SELECTs from your model server. Keep queries narrow to control p99 latency.
• Cached feature API: Run a microservice that queries ClickHouse and caches the latest per-user feature rows in Redis for sub-millisecond reads. Cache invalidation is driven by Kafka change events.
• Batch enrichment: For LLM tasks that tolerate a few 100ms, fetch and assemble features synchronously using ClickHouse and re-rank candidates returned from ANN.

Minimizing tail latency

• Design your ORDER BY and primary key to avoid wide scans for point reads (e.g., ORDER BY (user_id, last_updated)).
• Use materialized views to pre-aggregate or keep latest rows for hot keys.
• Provision enough shards/replicas for parallel reads and reduce IO contention on disk-bound workloads.
• Isolate hot tables on faster storage tiers or use ClickHouse Cloud to map tiers automatically.

Telemetry, monitoring and model observability

ClickHouse excels at time-series and cohort analytics. A single ClickHouse deployment can hold model predictions, latencies, input hashes, and downstream user feedback — enabling near-real-time drift detection and A/B analysis.

• Streaming model logs: Push model input, prediction, confidence and latency to a ClickHouse telemetry table using Kafka.
• Online feature drift: Run scheduled SQL jobs to compute per-feature distribution shifts and surface cohorts that need retraining.
• Alerting: Use precomputed materialized views and external alerting on query results (or via embedded dashboards in ClickHouse Cloud) for SLA breaches.

Security, compliance and privacy considerations

When you store customer embeddings and telemetry, privacy and compliance matter.

• Encryption: Enable at-rest encryption and TLS for client connections. Use cloud-provider KMS for key management.
• Access control: Implement role-based access control, least-privilege service accounts, and network-level restrictions (VPCs, private links).
• PII handling: Apply deterministic hashing or tokenization for PII fields and store only the fields required for model serving. Use pseudonymization when possible.
• Audit logs: Keep access and query logs for auditing and GDPR/CCPA requests. ClickHouse supports logging via system tables and can forward logs into a secure archive.

Operational concerns & cost control

ClickHouse often reduces TCO by consolidating analytics and feature storage, but watch out for hidden costs:

• Storage vs compute: Columnar compression reduces storage but can increase CPU during decompression. Balance storage codecs and CPU provisioning.
• Replication & cross-region: Multi-region replication increases durability but costs more. Use targeted replication for critical tables.
• Benchmarks: Measure p50/p95/p99 for your production query shapes (point reads, small IN queries, candidate re-rank). Funding and cloud improvements in 2025–2026 improved performance, but each workload is different.

Evaluation checklist — do a proper POC

Run this validation checklist in a 2–4 week POC before committing:

1. Load a representative embedding corpus (1M, 10M, 100M — choose target scale). Test ingest throughput with Kafka and micro-batches.
2. Measure end-to-end latency for your read path: ANN candidate retrieval (if used) + ClickHouse re-rank + feature attach + model call. Track p50/p95/p99.
3. Validate resource usage: CPU, disk IOPS, network egress, and cloud cost per million queries.
4. Test drift detection queries and compute cost of hourly/daily telemetry aggregations.
5. Confirm security posture: at-rest encryption, RBAC, VPC access, and auditability with sample compliance requirements.

2026 trends and future predictions

Looking forward from early 2026, several trends shape how ClickHouse fits into ML infrastructure:

• Vector-first OLAP: The convergence of vector operations and columnar analytics is accelerating. Expect ClickHouse releases to add richer SIMD/vectorized primitives and faster internal ANN primitives through 2026.
• Cloud-managed feature stores: Vendors are packaging ClickHouse Cloud with ML-tailored features (auto-scaling for ANN + telemetry pipelines), making POC friction lower.
• Hybrid topologies: The dominant production pattern will be hybrid: ANN engines for ultra-large candidate retrieval, ClickHouse for re-ranking, telemetry and feature engineering.
• Stronger MLOps integrations: Expect out-of-the-box connectors between Feast-style APIs and ClickHouse, enabling the same feature engineering code to back offline training and online serving.

“ClickHouse’s late-2025 funding and product investments are accelerating its maturation into an OLAP-backed feature store that can handle both embeddings and telemetry at production scale.”

Quick reference: Best practices cheat sheet

• Store embeddings as Array(Float32) with per-column compression.
• Use MergeTree/Partitioning and ORDER BY to optimize typical read patterns.
• Materialize latest rows for hot keys with materialized views or projections.
• Batch inserts and use Kafka engine for reliable streaming ingestion.
• Offload large-scale ANN to a specialized engine and keep ClickHouse for feature joins & re-rank.
• Instrument p99 latency, CPU and IO in your POC; watch codec trade-offs.

Actionable next steps (30/60/90 day plan)

Days 0–30: Run a micro-POC

• Deploy ClickHouse (Cloud or self-hosted single node).
• Ingest 1–10M embeddings and user features; create a materialized view for latest rows.
• Measure simple point-read latency and test the SELECT re-rank path.

Days 30–60: Add streaming and monitoring

• Integrate Kafka for ingestion and build telemetry tables for model logs.
• Implement TTLs and data lifecycle policies.
• Instrument queries and set alert thresholds for p99 latency and throughput.

Days 60–90: Scale & integrate with model serving

• Move to a multi-node ClickHouse cluster or ClickHouse Cloud; add replicas and adjust shard keys.
• Implement a two-stage ANN + ClickHouse re-rank flow if needed.
• Automate deployment and observability; validate compliance controls.

Final thoughts

ClickHouse’s OLAP strengths — fast analytic scans, efficient columnar compression and strong streaming integrations — combined with recent product investments and capital backing (including the Dragoneer-led round in late 2025) make it a compelling platform for a consolidated feature store that handles embeddings, features and model telemetry. It is not a universal replacement for every vector workload, but for teams that need tight coupling between analytics and model serving, ClickHouse is worth serious consideration in 2026.

Call-to-action

Ready to test ClickHouse as your LLM feature store? Start a focused POC: provision ClickHouse Cloud, load a representative slice of your embeddings and telemetry, and benchmark an ANN+re-rank flow. If you want a POC checklist, a sample ingestion script, and a 90‑day rollout plan tailored to your scale, request the TrainMyAI ClickHouse Feature Store kit (operational checklist and SQL templates) from our resources page or contact our engineers for a hands-on workshop.

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