Sold by: GraphistryÂ
Deployed on AWS
100X Investigative power with Graphistry's best-in-class visual graph analytics and investigation automation. Start surfacing the stories in your data with GPU-accelerated visual analytics, enterprise-grade graph support, point-and-click automation, RAPIDS GPU Jupyter notebooks, and web developer AP
Overview
Investigate and automate with best-in-class GPU visual graph analytics and automation. Whether you are an analyst, researcher, or developer, explore your data as a graph with a few nodes and edges.. to millions!
AWS MARKETPLACE
- Private: Runs in your AWS account
- Starts at $1.47/hr for individuals: g4dn.xlarge
- Pay-as-you-go: Part of your regular AWS bill; stop/start AMI to toggle utilization
- Contact for tailored discounts
- AWS-Ready: Drivers, patches, log forwarding, auto-healing, TLS, & more
2.0 ENGINE W/ RAPIDS
- Multi-GPU client/coud
- Rich visual analytics: Point-and-click time bars, search, coloring, clustering, & more
- Explore CSVs, Splunk/ELK/Kusto, SQL/Spark/Impala, Neo4j/Neptune/JanusGraph/TigerGraph/DSE Graph, Pandas/NetworkX, & more
FOR ANALYSTS
- Go from raw data to insights
- Explore data that is non-graph, large, or complex
- Save, share, and embed your sessions
- Automate without coding by turning any investigation into a template
- Jupyter notebooks setup with secure login, PyGraphistry, Nvidia RAPIDS, & examples
FOR DEVELOPERS
- Python, JS, React, & REST (all languages)
- Embed stunning and full-featured visual graph analytics
- Embed automation deep links anywhere
- Prototype and iterate same-day with PyGraphistry
For enterprise teams needing on-prem, airgapping, orchestration, and support services such as resiliency, solutions, & training, see our homepage.
Launch walkthrough: https://www.graphistry.com/blog/marketplace-tutorialÂ
Highlights
- Connect, explore, correlate, and automate without coding
- Scale with the only GPU client<>cloud engine
- Rapidly prototype with secured RAPIDS-ready Jupyter notebooks and web embedding APIs
Details
Sold by
Delivery method
Delivery option
64-bit (x86) Amazon Machine Image (AMI)
Latest version
Operating system
Ubuntu 22.04.4 LTS
Deployed on AWS
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Features and programs
Financing for AWS Marketplace purchases
AWS Marketplace now accepts line of credit payments through the PNC Vendor Finance program. This program is available to select AWS customers in the US, excluding NV, NC, ND, TN, & VT.
Pricing
Pricing is based on actual usage, with charges varying according to how much you consume. Subscriptions have no end date and may be canceled any time. Alternatively, you can pay upfront for a contract, which typically covers your anticipated usage for the contract duration. Any usage beyond contract will incur additional usage-based costs.
Additional AWS infrastructure costs may apply. Use the AWS Pricing Calculator to estimate your infrastructure costs.
Dimension | Cost/hour |
|---|---|
g4dn.2xlarge Recommended | $10.00 |
g4dn.4xlarge | $10.00 |
p3dn.24xlarge | $26.20 |
g4dn.8xlarge | $10.00 |
p3.2xlarge | $10.00 |
p3.16xlarge | $26.20 |
p4d.24xlarge | $26.20 |
g4dn.metal | $26.20 |
p3.8xlarge | $18.20 |
g4dn.xlarge | $1.47 |
Vendor refund policy
No refunds
Legal
Vendor terms and conditions
Upon subscribing to this product, you must acknowledge and agree to the terms and conditions outlined in the vendor's End User License Agreement (EULA)Â .
Content disclaimer
Vendors are responsible for their product descriptions and other product content. AWS does not warrant that vendors' product descriptions or other product content are accurate, complete, reliable, current, or error-free.
Delivery details
64-bit (x86) Amazon Machine Image (AMI)
Amazon Machine Image (AMI)
An AMI is a virtual image that provides the information required to launch an instance. Amazon EC2 (Elastic Compute Cloud) instances are virtual servers on which you can run your applications and workloads, offering varying combinations of CPU, memory, storage, and networking resources. You can launch as many instances from as many different AMIs as you need.
Version release notes
v2.45.3 - 2025.11.05
User-Facing Improvements
- Critical collections interference with visual encodings
- was: collections auto-enablement overwriting color/size/opacity/icon/label encodings - encodings appear for ~1s then fallback to defaults.
- now: meaningful collections detection prevents unnecessary processing, safety net prioritizes existing encodings over defaults.
- status: fixed | action: none required.
- impact: affects all client types (PyGraphistry .point_color()/.point_size(), GraphistryJS, REST API).
- Critical GPU memory leak in DataFrame caching.
- was: forge-etl-python accumulating 15.3GB+ GPU memory over time due to LRU cache holding strong references to cuDF DataFrames.
- now: weak references (weakref.ref()) with automatic cleanup of dead references, preventing memory accumulation.
- status: partial | action: none required.
- note: major leak fixed, additional autohealing layer for similar issues under development.
- Streamgl-gpu encodings race condition mitigation.
- was: collections encodings could be overwritten by timing issues in GPU worker, causing visual encoding failures.
- now: improved encodingsUpdated flag timing in encodeNodes/encodeEdges, flag clearing moved to after successful data delivery.
- status: partial | action: none required.
- note: additional mitigations continue in subsequent releases.
- Invitation role dropdown viewer option with upgrade nudge.
- was: viewer role option not visible in invitation dropdown, causing confusion.
- now: disabled viewer option shown in dropdown with upgrade nudge to inform users of plan limitations.
- status: fixed | action: none required.
- Collections edge coloring with empty edge sets.
- was: error or incorrect rendering when collections edge coloring applied to subgraph with no edges.
- now: graceful handling of empty edge sets in collections.
- status: fixed | action: none required.
- UI guard for collections edge encoding fallthrough.
- was: collections edge encoding could fallthrough causing incorrect visual state.
- now: UI guard prevents problematic fallthrough behavior.
- status: partial | action: none required.
- note: temporary workaround while underlying issue addressed.
- Edge GFQL generation from GUI editor.
- was: GUI GFQL editor generating incorrect edge GFQL queries.
- now: correct edge GFQL generated from GUI editor.
- status: fixed | action: none required.
- Degree properties in collections GFQL queries.
- was: degree, degree_in, degree_out properties not handled correctly in collections GFQL.
- now: all degree properties properly supported in collections GFQL queries.
- status: fixed | action: none required.
- Edge indexing with integer src/dest IDs via GFQL.
- was: incorrect edge indexing when datasets with int src and dest IDs pulled via GFQL.
- now: correct edge indexing for integer ID columns (uses reindexed IDs).
- status: fixed | action: none required.
- Prevent mixing collection and base graph encodings on load.
- was: collection encodings mixed with base graph encodings on load, causing visual inconsistencies.
- now: encodings kept separate, preventing mixing.
- status: fixed | action: none required.
- Filter via legend race condition mitigation.
- was: clicking legend filters during graph load could cause race conditions leading to empty table inspector or incorrect filter states.
- now: type changes prevented during load to reduce race condition likelihood.
- status: partial | action: none required.
- note: additional mitigations continue in subsequent releases.
- Data table loading indicator.
- was: blank/empty table display while data loading, unclear if loading or no data.
- now: loading ellipsis (...) displayed during data fetch.
- status: fixed | action: none required.
- Color encoding panel responsiveness for narrow screens.
- was: color encoding panel not responsive on narrow screens, content cut off or inaccessible.
- now: responsive layout down to 650px width, panel always visible and accessible.
- status: fixed | action: none required.
- Preserve complex GFQL predicates in query builder.
- was: switching between query builder and manual GFQL editing wiped out complex predicates.
- now: complex GFQL predicates preserved when switching editing modes and scrolling.
- status: fixed | action: none required.
- CSV download from datatable authentication error.
- was: error when downloading CSV from datatable view due to incomplete anonymous dataset download auth code.
- now: proper authentication handling for anonymous dataset downloads.
- status: fixed | action: toggle unauthenticated downloads in the settings page of the user or org that wants to enable that on all their items.
- Fix Data Table button race condition by disabling it during graph load with helpful tooltip "Data Table - Please wait, loading data..." that changes to "Data Table" when ready preventing premature access to unloaded data.
- Fix Data Table race condition crash on graph load by making critical timeout and retry values configurable across the visualization pipeline, combined with comprehensive OpenTelemetry tracing improvements for production debugging.
- Fix inspector data table crash on load and column picker persistence.
- Fix color and graph rendering issues in high-latency environments by increasing GPU service timeouts, ensuring graphs load correctly even under heavy load.
- Added configurable environment variables for viz to gpu service's timeouts (GRAPHISTRY_VIZ_TO_GPU_TIMEOUT_MS, GRAPHISTRY_VIZ_TO_GPU_RETRY_COUNT) and HTTP agent settings (GRAPHISTRY_HTTP_AGENT_TIMEOUT_MS, GRAPHISTRY_HTTP_AGENT_KEEPALIVE_MS). Previous hardcoded timeouts of 3-5s for table reads now default to 30s, preventing premature failures when GPU services need more time under load.
- Fix unreliable Kepler data load.
- Fix erroneous square layout with non-zero play time durations.
- Hide extra_num_free_editors field in Organization create and update form in self hosted installation(GRAPHISTRY_CLOUD=False).
- Fix inconsistent edge types causing issues with Kepler edge remapping.
- Fix toolbar buttons hanging below toolbar bar onto canvas at narrow viewports (~850px and below).
- Fix Kepler map availability detection by validating latitude/longitude columns. Exposed top-level bindings for column referencing.
- Allow empty KeplerConfiguration model.
- Fix accumulating drag delay bug in camera panning by using Observable.defer() pattern to prevent state accumulation in rxjs-gestures NormalizeOperator.
- API: Migrate from deprecated .chain() to .gfql() unified API.
- Remote GFQL: Fixed DataFrame comparison bug causing dataset loads to hang at "15% loading" spinner. Bug triggered when reloading persisted datasets with sequential numeric node indices where all nodes participated in edges. Error: ValueError: The truth value of a DataFrame is ambiguous. Fixed by changing len(src_dst_nodes) == nodes_without_nulls to len(src_dst_nodes) == len(nodes_without_nulls).
- Remote GFQL, Python: Proper 400 HTTP error codes and messages for invalid requests like unexpected call() arguments.
- Remote Python: Resolve 500 HTTP error due to process_python_query function signature mismatch causing TypeError with missing required arguments.
- Authentication: Return HTTP 403 (Forbidden) for authenticated users lacking permissions instead of HTTP 401 (Unauthorized), improving error clarity and proper status code semantics.
Features
- Added GeoViz (Kepler.gl) integration supporting Map Mode for full geospatial exploration and Hybrid Mode that seamlessly combines graph and map views-enabling interactive, GPU-accelerated location-aware graph analysis with time-series playback.
- Add environment variable controls for Kepler maps UI in enterprise deployments. Maps default ON for cloud/hub, default OFF for enterprise (controlled via GRAPHISTRY_CLOUD and GRAPHISTRY_ENABLE_KEPLER env vars).
- Augment Kepler metadata with node dataset and layer entries if none exist.
- Add screenshot button to graph toolbar
- Personal org configuration access
- was: only certain plan tiers could configure personal organization settings
- now: paid plan users and all users in self-hosted mode (GRAPHISTRY_CLOUD=False) can config personal org in profile page
- status: released | action: none required
Infra
- Dual CUDA build system (11.8/12.8) enabling support for both legacy and Blackwell GPUs.
- Comprehensive OpenTelemetry instrumentation for remote GFQL and Python endpoints with HTTP-level parent spans enabling complete distributed trace visualization in Jaeger. Traces now show full request lifecycle from HTTP entry through dataset loading, policy enforcement, execution phases, with automatic context propagation to 20+ child spans. Includes trace context logging (trace_id/span_id in logs when ENABLE_TRACE_CONTEXT_LOGGING=true), rich searchable attributes (dataset.id, org.slug, error.type), and helper scripts for debugging (bin/otel/).
- AWS AMI: Fixed NVIDIA 570 driver installation conflicts by pinning all 17 packages to version 570.158.01-0ubuntu1 in single apt transaction, preventing APT from selecting incompatible newer versions during dependency resolution. Also fixed packer template to auto-select availability zones and corrected g4dn instance type typo.
- GCP Image: Fixed NVIDIA 575 driver installation conflicts by creating dedicated GCP driver script with version pinning (575.57.08-0ubuntu1) for all 19 packages including nvidia-modprobe and nvidia-settings in single apt transaction, preventing APT from selecting incompatible newer versions (580.x) during dependency resolution. Added debconf pre-seeding to avoid interactive prompts on Ubuntu 22.04.
Versions
- pygraphistry: 0.45.7 (was 0.39.1) - Remote GFQL execution with server-side persistence eliminating client round-trips, automatic engine detection (GPU/CPU), and metadata hydration. Critical fixes for authentication workflows, pandas/cuDF/dask engine compatibility crashes, and None dereference errors. Enhanced: GFQL validation/policy system, UMAP GPU fallback, hypergraph flexibility, comprehensive type safety.
- Upgraded to CONDA_IPYWIDGETS 8.1.8 (from version 8.1.7).
- jupyterlab_server: Changed to range 2.27.3 -> 2.29 (was 2.27.3).
Docs
- REST API: Documentation updates for pandas delim_whitespace deprecation with migration guidance.
Additional details
Usage instructions
LAUNCH Note: if you have an issue logging into the Graphistry instance after launching the AMI, it may be because the IMDSv1 metadata service has been disabled, and we have created a patch which will be available in the next release. You can either enable IMDSv1 metadata service prior to creating the instance, or if that's not an option, please contact support@graphistry.com for instructions to reset the admin password. https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/configuring-IMDS-new-instances.htmlÂ
- Launch and go to the homepage at the AWS instance's public IP (use http, not https). The services may take 2-5 minutes to launch and will display loading warnings in the meanwhile. Make sure you are using a GPU server (g4dn., p3.). Worst-case, reboot.
- Log in with 'admin' / 'i-your_instance_id'
- Continue on to the notebook tutorials or file uploader; create accounts for the rest of your team; explore the documentation
Quick links:
- AWS launch walkthrough tutorial and videos: https://www.graphistry.com/blog/marketplace-tutorialÂ
- Your first visualization - File Uploader tutorial: https://www.graphistry.com/blog/graphistry-importer-visually-explore-the-relationships-in-any-csv-xls-with-gpu-graph-analytics-and-no-coding-demo-analyzing-honeypot-device-logsÂ
- Documentation: https://hub.graphistry.com/docsÂ
- AWS Marketplace administration: https://github.com/graphistry/graphistry-cli/blob/master/docs/aws_marketplace.mdÂ
- General advanced administration: https://github.com/graphistry/graphistry-cliÂ
RESTART Use AWS console to stop/start/restart, or SSH in and run cd graphistry && sudo docker-compose restart
CONFIGURE
- Recommended: Associate a stable domain name + static Elastic IP with your instance
- See more, including custom domain names, at https://github.com/graphistry/graphistry-cliÂ
Contact options for features & support: https://www.graphistry.com/support - we'd love to help!
Resources
Vendor resources
Support
Vendor support
Online Support support@graphistry.comÂ
AWS infrastructure support
AWS Support is a one-on-one, fast-response support channel that is staffed 24x7x365 with experienced and technical support engineers. The service helps customers of all sizes and technical abilities to successfully utilize the products and features provided by Amazon Web Services.
Updated weekly
AI generated from product descriptions
Graph Analytics Engine
GPU-accelerated visual graph analytics platform with multi-GPU support
Data Source Compatibility
Supports diverse data sources including CSVs, SQL, Spark, Neo4j, Pandas, and graph databases
Visualization Capabilities
Interactive visual analytics with point-and-click time bars, search, coloring, and clustering features
Development Integration
Provides Python, JavaScript, React, and REST APIs for embedding graph analytics
Automation Framework
No-code investigation automation with ability to transform analytics sessions into reusable templates
Data Federated Search
Enables searching across distributed security data sources including SIEMs, EDRs, cloud storage, data lakes, identity, and network tools without data duplication
AI-Powered Analytics
Utilizes AI agents to automate context gathering and reduce investigation time from hours to minutes during security operations
Security Data Pipeline
Supports efficient data movement to cloud storage with ZSTD-compressed Parquet format and automatic partitioning for performance optimization
Multi-Source Integration
Provides pre-built integrations with multiple security and cloud platforms including Amazon Athena, Amazon S3, Amazon Security Lake, Crowdstrike, and others
Security Data Compression
Achieves up to 80% reduction in storage footprint through advanced compression and efficient data management techniques
Computer Vision Defect Detection
Advanced AI-driven system for identifying and analyzing visual defects in production environments
Production Line Monitoring
Real-time AI-powered command center for continuous tracking of manufacturing processes and compliance
Data Preparation Tools
Advanced data curation, annotation, and synthetic data generation capabilities for computer vision model development
Image Capturing System
Programmable image capture technology with AI-enhanced visual analysis and inspection capabilities
Model Training Platform
Intelligent AI platform that simplifies computer vision model development through interactive training approaches
Standard contract
No
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