The only context layer built for time and scale.
Pometry's temporal graph analytics system has been developed from scratch to handle massive amounts of data over time.
Native temporality at
industrial scale.
A next generation graph system, re-written from the ground up to handle complex temporal queries at multi-terabyte scale.
Temporal-native
Other graphs treat time as a property, creating complexity that compounds and kills query performance. Pometry tracks time in the storage layer, so you get full temporality on every node & edge without any slow-downs.
Fast, disk-native storage
Zero-copy columnar memory, SIMD-accelerated execution & lock-free parallel data structures allow for selective loading of data, so queries are fast and sub-graphs can be created and edited without reloads. Full, multi-terabyte graphs load from cold in seconds (without needing to hold the whole graph in memory).
Big data, small box
Pometry brings the compute to your data, not the other way around. Our 10MB binary deploys on-premise, air-gapped, in any cloud, or embedded in an existing stack. No heavy infrastructure, no managed service lock-in - and a fraction of the cost to run.
Government-grade security.
No data movement
Pometry processes data in place. We never copy, replicate, or transmit your organisational data to external systems. The graph lives in your infrastructure.
Air-gapped deployment
Full support for offline and isolated network environments. Pometry can run with zero external network access, critical for government and classified deployments.
Flexible deployment
On-premise bare metal, private cloud (AWS, Azure, GCP), hybrid, or containerised via Kubernetes. The 10MB Pometry binary runs anywhere.
Compliance-ready
GDPR and ISO 27001 alignment. Full audit logging, role-based access control, and data lineage tracking built in from day one.
How we compare.
 Pometry | Incumbent graph solutions | |
|---|---|---|
| Functionality | ||
| Agentic decision support | ||
| Temporal motif support | ||
| Temporal context for LLMs | ||
| Cost of ownership | ||
| Personnel overheads | 1 FTE | 6 FTE |
| Compute cost at scale | ~$10k/mo Single 128GB EC2 instance (on disk) | ~$100k/mo AWS cluster (in memory) |
| Vendor requirements | Single vendor for outcome | 5 vendors for outcome** |
| Performance | ||
| Data load time* | 26 mins MacBook Pro (128GB RAM) | 1.2 hrs HPC (3.5TB RAM) |
| Query time | 4 sec MacBook Pro (128GB RAM) | 1.5 hrs HPC (3.5TB RAM) |
| Resilience | ||
| Full recovery after downtime | < 5 mins Copy of on-disk data | 1–2 hrs Full in-memory reload |
| Implementation | ||
| POC delivery | 4 weeks | 6 months |
* Benchmark analysis on large-scale cyber security data set: Pometry on MacBook Pro (128GB RAM): 26 mins load time; 4 second query time; Competitors on HPC (3.5TB RAM): 1.2 hr load time; 1.5 hr query time
** Based on the following vendors being required to replicate Pometry scale performance: Apache Spark; Neo4j; Elasticsearch; Redis; Oracle
Join the community.
Join the growing community of academics, researchers and technologists using Pometry. Our limited GPL version, called Raphtory, is available for research, testing & study purposes only.