The only time aware graph system.
Pometry's context layer is built on Raphtory: the only graph system with temporality built into all events and entities.
Trusted to solve problems others can't
What Raphtory does.
Temporal-native
Temporality is first-class, not an add-on. Every node and edge carries its complete chronological history. Query the graph as it exists today, or as it existed at any point in the past.
Built for performance
Rust and Apache Arrow give zero-copy columnar memory and SIMD-accelerated execution. Lock-free parallel data structures mean no throughput ceiling as workloads scale.
Fast, disk-native storage
The graph is on disk and available from cold in seconds. Raphtory loads only the chunks required for each query — no need to hold the entire graph in memory.
Deployable anywhere
A 10MB binary. Deploy on-premise, air-gapped, in any cloud, or embedded in an existing stack. No heavy infrastructure, no managed service lock-in.
One graph system, multiple languages.
Raphtory is written from the ground up in Rust, with a query layer that supports Python, GraphQL, MCP and low level native algorithm extensions.
Systems-level access
Direct bindings to the Raphtory core. Zero-overhead integration for performance-critical applications and embedded deployments.
Data science native
Native Python bindings for analytics workflows. Integrates directly with all your data science and machine learning tools.
Flexible query language
Query exactly the fields you need. Supports deep graph traversal, relationship filtering, and temporal windowing in a single request.
AI agent integration
Expose your context layer directly to LLMs and AI agents via the Model Context Protocol. Every response grounded in your graph, fully auditable.
Real-time event ingestion
Webhook and event-stream connectors for continuous graph updates. Changes are reflected immediately and preserved in the temporal history.
Bulk data ingestion
Bulk load any Arrow-compatible tabular data — CSV, Parquet, Pandas, DuckDB, and more. Data can be completely unordered; Raphtory merges all history chronologically.
50+ out of the box algorithms.
Every algorithm understands time. Run PageRank on last quarter's graph. Detect communities that formed and dissolved. Find paths that were only valid within a specific window.
Point-in-time execution - run any algorithm against a historical snapshot of the graph.
Window-based analysis - restrict scope to any time window, down to the millisecond.
Change tracking - compare output across periods to detect structural shifts in your network.
Custom algorithms - extend the library with your own Rust or Python implementations.
Your data stays where it belongs.
Pometry is designed for organisations that can't move sensitive data. The platform runs inside your environment. Nothing leaves.
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 Raphtory 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.
Technical head-to-head.
 Pometry | Incumbent graph solutions | |
|---|---|---|
| Functionality | ||
| Proactive 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) |
| Implementation | ||
| POC delivery | 4 weeks (identifying $300M of value) | 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 Raphtory. Our open source (GPL) version is free for research, testing & study purposes.