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Documentation Index

Fetch the complete documentation index at: https://docs.helix-db.com/llms.txt

Use this file to discover all available pages before exploring further.

For the complete documentation index optimized for AI agents, see llms.txt.
Every system makes design choices. Helix Cloud optimizes for durable, cost-effective graph, vector, and text workloads with strong transactional guarantees. These choices have implications.

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Excels At

AreaDetails
Durable storageAll data persists in object storage. No risk of data loss from local disk failure. Storage capacity scales independently of compute.
Read scalabilityReaders auto-scale horizontally. Doubling readers doubles read throughput with no coordination overhead.
Serializable transactionsEvery query runs against a stable snapshot with ACID semantics by default.
Mixed graph, vector, and text workloadsGraph traversals, vector search, and full-text search execute in the same transaction, against the same snapshot. No need to stitch together separate systems for those workloads.
Cost efficiency at scaleObject storage is significantly cheaper per GB than local SSDs or in-memory stores. Large datasets remain affordable.
Operational simplicitySingle writer eliminates distributed consensus. No leader election, no split-brain, no quorum management.

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Not Optimal For

AreaDetails
Sub-millisecond readsCache hits are fast, but cold reads require an object storage round trip. Workloads that require guaranteed sub-millisecond latency on every read are better served by in-memory databases.
Ultra-low write latencyWrites incur object storage latency for durability. Write throughput is high, but individual write latency has a floor set by object storage round-trip time.
Exhaustive vector recallVector search is approximate (ANN). Applications that require 100% exact nearest neighbor results should use brute-force search on smaller datasets.

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Design Choices

Object storage as the system of record. Caches accelerate reads but are not required for correctness. This means cold starts are slower than systems that keep all data on local disk, but durability and cost characteristics are superior. Single writer. Serializing all writes through one process avoids distributed coordination at the cost of write throughput being bounded by a single node. In practice, batching and the high throughput of the writer process make this sufficient for most workloads. Specialized cache paths. Helix maintains separate cache paths for graph data, vector indexes, and text search artifacts. The tradeoff is less flexibility in cache allocation when only one workload dominates. Stored query model. Pre-compiling queries eliminates runtime parsing overhead and limits the API surface to explicitly deployed operations. The tradeoff is that ad-hoc queries are not supported; all queries must be authored and deployed in advance.