No single storage format fits every workload. So KalDB picks the right one automatically — and tunes the query layer to match.
Stop choosing between Elasticsearch and ClickHouse. One API, the right format for every query.
Two databases. Two ops burdens. One data problem.
The same data lives in Elasticsearch for search and ClickHouse for analytics. Double the storage, double the cost.
Different clusters, different configs, different failure modes. Your team manages two complex distributed systems.
Shard counts, replica settings, materialized views, index templates. Every workload change means hours of tuning — and agentic workloads change patterns constantly.
Elasticsearch licensing plus ClickHouse compute. Costs scale linearly with data while budgets don't.
You run two databases because each is locked to one storage format. Here's what that costs.
Every event ingested twice, stored twice, indexed twice. You're paying double for the same data.
Two clusters to monitor, patch, scale, and debug. Two on-call rotations. Two sets of expertise.
Neither system learns from your workload. Every optimization is manual. Every scaling decision is yours.
KalDB replaces both Elasticsearch and ClickHouse — not by picking one format, but by picking the right one every time.
AI picks from open-source formats — inverted indexes (Lucene), columnar, wide column, and more. Not locked to one format.
AI tunes both layers: query planning and routing AND storage format and indexing strategy. Two optimization loops, zero manual work.
All storage uses open-source formats. Your data is never locked in. Portable, readable, yours.
ClickHouse has full-text search like your phone has a flashlight.
ClickHouse uses bloom filter indexes for text — probabilistic, imprecise, and unable to rank results. Real search requires inverted indexes with term frequencies and positions.
There's no BM25, no TF-IDF, no way to rank results by relevance. You get matches, not answers. Every "search" is really a filter.
Full-text queries in ClickHouse scan data at query time. At petabyte scale, that means seconds — not milliseconds. The architecture wasn't built for this.
The real point: no single fixed format is optimal for all workloads.
KalDB picks the right storage format automatically — inverted indexes when you need search, columnar when you need analytics, and adapts as your workload changes.
AI agents don't follow predictable query patterns. Your database shouldn't assume they do.
Storage format, partitioning strategy, embedding model, search plugin — KalDB A/B tests every decision automatically without touching production.
Storage format, partitioning strategy, vector embeddings, search plugins, ranking functions — every layer of the system is a tunable experiment. KalDB makes the entire pipeline configurable.
Every decision — from which storage format to use to how data is partitioned — runs as an A/B test against the current configuration. KalDB measures the outcome and promotes the winner or rolls back, automatically.
KalDB's built-in multi-tenancy isolates every experiment from production workloads. AI spins up a separate tenant to test changes against real traffic patterns — your live users are never affected, and only proven improvements get promoted.
Traditional systems require a team of engineers to test every change manually. KalDB lets AI experiment continuously — across storage, partitioning, indexing, and search — measuring results and improving the entire system on its own.
Every AI decision is visible. Override anything you want.
See which storage format was chosen, which query plan was used, and why — for every single query.
Built-in observability into the system's own decisions. Monitor how KalDB is tuning itself over time.
The AI suggests, you control. Pin storage formats, force query plans, or set constraints. Your call.
Open-source storage formats mean you can always inspect your data directly. No proprietary encoding, ever.
No clusters to manage. No capacity planning. Just data in S3 and queries that scale.
All data lives durably in S3 with hot data cached locally for performance. No replication to configure, no storage to manage. Cheap and infinitely scalable out of the box.
Compute scales up and down with your workload automatically. Handle traffic spikes without over-provisioning and pay only for what you use.
No shards to rebalance, no replicas to sync, no nodes to right-size. Deploy KalDB and let it run — the system manages itself.
What changes when you collapse the zoo
Battle-tested at Slack and Airbnb for years, running petabytes of data in production.
Drop-in replacement for OpenSearch. No migrations or rewrites.