{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"datacrunch","slug":"datacrunch","name":"DataCrunch","type":"platform","url":"https://datacrunch.io","page_url":"https://unfragile.ai/datacrunch","categories":["deployment-infra"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"datacrunch__cap_0","uri":"capability://automation.workflow.eu.compliant.gpu.instance.provisioning.with.gdpr.data.residency","name":"eu-compliant gpu instance provisioning with gdpr data residency","description":"Provisions bare-metal NVIDIA GPU instances (A100, H100, B200, GB300) hosted exclusively in European datacenters with guaranteed EU data residency and SOC 2 Type II certification. Uses pay-as-you-go pricing model with instant activation via CLI or Terraform IaC, eliminating need for multi-region failover or data transfer compliance audits. Infrastructure ownership by European entity provides contractual GDPR compliance without third-party data processor agreements required by US cloud providers.","intents":["Deploy AI models in EU without violating GDPR data residency requirements","Train large language models on sensitive European customer data without cross-border transfers","Avoid US cloud provider data sovereignty concerns for regulated industries (finance, healthcare, government)","Reduce compliance audit overhead by using EU-owned infrastructure with built-in GDPR guarantees"],"best_for":["European enterprises with GDPR compliance mandates","Financial institutions and healthcare providers requiring data residency","Government agencies and public sector organizations","Teams migrating from US-based cloud providers to EU infrastructure"],"limitations":["Geographic constraint: EU-only deployment means no global multi-region distribution for latency-sensitive applications","No multi-region failover: Single geographic footprint creates availability risk vs AWS/GCP/Azure global infrastructure","Specific EU datacenter locations not publicly documented, limiting ability to optimize for specific country compliance","No mention of disaster recovery or cross-border backup options within EU"],"requires":["Valid payment method (credit card or invoice)","Verda CLI or Terraform 1.0+ for infrastructure provisioning","NVIDIA GPU driver compatibility on client side for instance management","EU business registration or GDPR Data Processing Agreement with Verda"],"input_types":["infrastructure-as-code (Terraform/OpenTofu)","CLI commands via Verda CLI","API requests with authentication token"],"output_types":["provisioned GPU instance with SSH access","instance metadata (IP address, GPU specs, pricing)","resource monitoring metrics"],"categories":["automation-workflow","deployment-infrastructure"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"datacrunch__cap_1","uri":"capability://automation.workflow.multi.gpu.cluster.orchestration.with.nvlink.infiniband.interconnect","name":"multi-gpu cluster orchestration with nvlink/infiniband interconnect","description":"Provisions fixed-size GPU clusters (16x, 32x, 64x, 128x GPUs) with NVLink and InfiniBand networking for distributed training workloads. Clusters use bare-metal architecture with direct GPU-to-GPU communication via NVLink (for A100/H100) or RoCE (RDMA over Converged Ethernet) for lower-latency collective operations (all-reduce, all-gather) required by distributed training frameworks like PyTorch DDP, DeepSpeed, and Megatron-LM. Self-service provisioning via CLI or Terraform with fixed cluster sizes (not dynamic scaling) and custom pricing for enterprise deployments.","intents":["Train large language models (7B-70B+ parameters) using distributed data parallelism across 16-128 GPUs","Minimize all-reduce latency during distributed training by using NVLink/InfiniBand instead of Ethernet","Provision reproducible multi-GPU clusters for model fine-tuning and continued pretraining","Avoid cloud provider overhead and achieve bare-metal performance for bandwidth-intensive collective operations"],"best_for":["ML teams training models >10B parameters requiring sub-millisecond GPU interconnect latency","Organizations optimizing training throughput for large-scale distributed training","Research labs running Megatron-LM, DeepSpeed, or custom distributed training code","Companies with predictable GPU cluster requirements (fixed-size clusters, not dynamic workloads)"],"limitations":["Fixed cluster sizes (16x, 32x, 64x, 128x) — no dynamic scaling for variable workloads; must provision entire cluster upfront","No spot/preemptible instances mentioned — full on-demand pricing for entire cluster duration","Cluster provisioning time not documented; likely 5-15 minutes vs instant single-GPU instances","No built-in cluster orchestration (Kubernetes) mentioned — requires manual distributed training framework setup","Custom pricing for enterprise clusters requires sales contact; no transparent rate cards"],"requires":["Distributed training framework (PyTorch DDP, DeepSpeed, Megatron-LM, or equivalent)","NVIDIA NCCL library for collective operations optimization","Verda CLI or Terraform for cluster provisioning","SSH access and ability to manage multi-node training job coordination","Understanding of distributed training concepts (data parallelism, gradient synchronization)"],"input_types":["cluster size specification (16x, 32x, 64x, 128x GPUs)","GPU type selection (A100, H100, B200, GB300)","training code compatible with distributed frameworks","infrastructure-as-code (Terraform/OpenTofu)"],"output_types":["provisioned multi-GPU cluster with NVLink/InfiniBand networking","cluster metadata (node IPs, GPU topology, interconnect specs)","training job monitoring and resource utilization metrics"],"categories":["automation-workflow","deployment-infrastructure"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"datacrunch__cap_10","uri":"capability://automation.workflow.batch.job.scheduling.and.execution","name":"batch job scheduling and execution","description":"Manages batch training and inference jobs with automatic resource allocation, job queuing, and execution monitoring. Users submit job specifications (container image, resource requirements, input/output paths) and system schedules execution on available GPU resources. Supports job dependencies, retry policies, and timeout management. Abstracts away resource scheduling complexity and enables efficient resource utilization by batching jobs across multiple instances.","intents":["Schedule multiple training jobs to run sequentially or in parallel on shared GPU resources","Automate hyperparameter sweep experiments without manual instance management","Run inference jobs on large datasets with automatic batching and resource allocation","Optimize GPU utilization by scheduling jobs during off-peak hours"],"best_for":["ML teams running multiple training experiments requiring resource sharing","Batch inference workloads processing large datasets","Research labs conducting hyperparameter sweeps and ablation studies","Organizations requiring fair resource allocation across multiple users"],"limitations":["Job scheduling algorithm and fairness guarantees not documented","No mention of job priority levels or preemption policies","Retry and timeout policies not documented","No mention of job dependency management or DAG support","Pricing for batch jobs not transparent; unclear if different from on-demand instances","No mention of job history, logging, or result archival"],"requires":["Job specification format (YAML, JSON, or proprietary format — not specified)","Container image with training/inference code","Input data in object storage or shared filesystem","Resource requirements (GPU type, count, memory, timeout)"],"input_types":["job specifications with resource requirements","container images and entry points","input data paths and output destinations"],"output_types":["job execution status and progress","job logs and error messages","output artifacts (model weights, results)"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"datacrunch__cap_11","uri":"capability://tool.use.integration.nvidia.ecosystem.integration.and.optimization","name":"nvidia ecosystem integration and optimization","description":"Native integration with NVIDIA software stack (CUDA, cuDNN, NCCL, TensorRT) and optimization for NVIDIA GPU architectures (A100, H100, B200). Instances come pre-configured with NVIDIA drivers and libraries; Verda's infrastructure is NVIDIA Preferred Partner certified, indicating validated performance and support. Enables use of NVIDIA-specific optimization tools (Nsight, NVIDIA Profiler) and frameworks (Megatron-LM, DeepSpeed) without additional configuration. Provides access to latest NVIDIA hardware (B200 Blackwell, GB300) for cutting-edge performance.","intents":["Use latest NVIDIA GPU architectures (A100, H100, B200) without procurement delays","Leverage NVIDIA optimization tools and frameworks for maximum training performance","Access NVIDIA Preferred Partner support and technical resources","Optimize inference using TensorRT and other NVIDIA libraries"],"best_for":["Teams using NVIDIA-specific frameworks (Megatron-LM, DeepSpeed, TensorRT)","Organizations requiring cutting-edge GPU hardware (B200, GB300)","ML teams optimizing for NVIDIA GPU architectures","Researchers using NVIDIA profiling and optimization tools"],"limitations":["NVIDIA Preferred Partner status does not guarantee priority support or SLA","No mention of NVIDIA software license management or compliance","Specific CUDA/cuDNN versions not documented; unclear if multiple versions available","No mention of NVIDIA NGC container registry integration","Support for non-NVIDIA frameworks (AMD ROCm, Intel) not mentioned"],"requires":["NVIDIA GPU instances (A100, H100, B200, GB300)","NVIDIA CUDA Toolkit compatible with training framework","NVIDIA cuDNN library for deep learning","NVIDIA NCCL for distributed training (if using multi-GPU clusters)"],"input_types":["NVIDIA-optimized training code","NVIDIA container images (NGC containers)","NVIDIA profiler and optimization tool configurations"],"output_types":["optimized training performance metrics","profiling results and bottleneck analysis","TensorRT optimized inference models"],"categories":["tool-use-integration","deployment-infrastructure"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"datacrunch__cap_12","uri":"capability://tool.use.integration.api.driven.resource.management.and.automation","name":"api-driven resource management and automation","description":"RESTful API for programmatic control of all Verda resources (instances, clusters, storage, networking, inference endpoints). Supports resource creation, deletion, status queries, and metric retrieval via HTTP requests with JSON payloads. Enables integration with custom automation tools, CI/CD pipelines, and third-party orchestration platforms. API authentication via tokens; responses include resource metadata and status codes for error handling.","intents":["Automate GPU resource provisioning in CI/CD pipelines for training jobs","Build custom resource management tools and dashboards","Integrate Verda with third-party orchestration platforms (Airflow, Prefect, etc.)","Programmatically monitor resources and trigger alerts based on metrics"],"best_for":["DevOps teams building custom automation and orchestration","Organizations integrating Verda with existing infrastructure management tools","ML platforms and SaaS companies using Verda as backend compute","Teams requiring programmatic control beyond CLI and Terraform"],"limitations":["API specification and endpoint documentation not provided; unclear which operations are supported","Rate limiting and quota policies not documented","No mention of API versioning or backward compatibility guarantees","Authentication mechanism (token format, expiration, rotation) not documented","No mention of webhook support for event-driven automation","Error handling and retry logic not documented"],"requires":["API credentials (token or API key)","HTTP client library (requests, curl, etc.)","Understanding of REST API conventions","Network access to Verda API endpoints"],"input_types":["HTTP requests with JSON payloads","resource specifications and configurations","query parameters for filtering and pagination"],"output_types":["JSON responses with resource metadata","HTTP status codes and error messages","resource identifiers and URLs"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"datacrunch__cap_13","uri":"capability://automation.workflow.multi.framework.training.support.with.pre.configured.environments","name":"multi-framework training support with pre-configured environments","description":"Instances come pre-configured with popular ML frameworks (PyTorch, TensorFlow, JAX) and dependencies (CUDA, cuDNN, NCCL) ready for immediate training without additional setup. Supports distributed training frameworks (PyTorch DDP, DeepSpeed, Megatron-LM, TensorFlow Distributed) with optimized configurations for Verda's NVLink/InfiniBand clusters. Eliminates dependency installation overhead and ensures framework versions are compatible with GPU drivers and NVIDIA libraries.","intents":["Start training immediately without spending hours installing dependencies","Use distributed training frameworks with pre-optimized configurations","Avoid version conflicts between frameworks, CUDA, and GPU drivers","Switch between frameworks (PyTorch, TensorFlow, JAX) without environment reconfiguration"],"best_for":["ML teams wanting to minimize setup time and focus on model development","Researchers using multiple frameworks for different projects","Teams running distributed training requiring optimized framework configurations","Organizations standardizing on specific framework versions"],"limitations":["Supported framework versions not documented; unclear if multiple versions available","Custom framework builds or bleeding-edge versions may not be available","No mention of framework update frequency or version lifecycle","Dependency conflicts may still occur with custom packages","No mention of environment isolation or containerization per user"],"requires":["GPU instance provisioning","SSH access to instance","Basic knowledge of framework APIs (PyTorch, TensorFlow, etc.)"],"input_types":["training code compatible with pre-installed frameworks","custom dependencies (pip packages, system libraries)"],"output_types":["trained models and checkpoints","training logs and metrics","framework-specific artifacts (TensorBoard logs, etc.)"],"categories":["automation-workflow","deployment-infrastructure"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"datacrunch__cap_2","uri":"capability://automation.workflow.serverless.containerized.model.inference.with.auto.scaling.endpoints","name":"serverless containerized model inference with auto-scaling endpoints","description":"Deploys containerized inference models as auto-scaling serverless endpoints using pay-per-request pricing. Accepts Docker containers with custom inference code, automatically scales replicas based on request volume, and exposes HTTP API endpoints. Abstracts away container orchestration and infrastructure management — users push container image to Verda registry, define endpoint configuration, and system handles scaling, load balancing, and billing per request. Supports image and audio model inference with managed endpoint templates for common model types.","intents":["Deploy custom inference models without managing Kubernetes or container orchestration","Scale inference endpoints from zero to thousands of requests/second automatically","Pay only for actual inference requests rather than reserved GPU capacity","Reduce operational overhead by eliminating need to manage auto-scaling groups or load balancers"],"best_for":["Startups and small teams deploying inference models without DevOps expertise","Applications with variable inference traffic (bursty, unpredictable request patterns)","Teams building multi-model inference services with different scaling requirements per model","Cost-conscious deployments where reserved capacity would be underutilized"],"limitations":["Cold start latency not documented — serverless containers likely have 5-30 second startup overhead vs warm instances","No minimum request volume guarantees; pricing per-request may be expensive for high-volume inference (>1M requests/month)","Auto-scaling behavior (scale-up/down timing, minimum/maximum replicas) not documented","Container image size limits not specified; large models may have slow deployment times","No mention of request batching or optimization for throughput-oriented workloads"],"requires":["Docker container with inference server (FastAPI, Flask, TorchServe, TensorFlow Serving, etc.)","Model weights and dependencies packaged in container image","Verda container registry access and push credentials","HTTP API endpoint definition (port, request/response schema)","Valid payment method for per-request billing"],"input_types":["Docker container image (OCI format)","endpoint configuration (resource requirements, scaling policy)","inference requests (HTTP POST with model inputs)"],"output_types":["deployed inference endpoint with public HTTP URL","inference results (JSON, image, audio, etc.)","usage metrics and billing data"],"categories":["automation-workflow","deployment-infrastructure"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"datacrunch__cap_3","uri":"capability://tool.use.integration.managed.inference.api.for.pre.configured.sota.models","name":"managed inference api for pre-configured sota models","description":"Provides pre-built HTTP API endpoints for state-of-the-art image and audio models without requiring container deployment or infrastructure management. Users call managed endpoints directly via REST API with model inputs (image URLs, audio files, text prompts) and receive structured outputs. Verda handles model hosting, GPU allocation, scaling, and optimization — users only pay for API calls. Eliminates need to download model weights, manage dependencies, or optimize inference code.","intents":["Use state-of-the-art image/audio models via simple HTTP API without ML infrastructure knowledge","Integrate pre-trained models into applications without downloading multi-GB model weights","Avoid GPU procurement and optimization work for inference-only use cases","Scale inference from prototype to production without code changes"],"best_for":["Non-ML engineers integrating AI capabilities into applications","Startups prototyping AI features without ML infrastructure expertise","Applications requiring image or audio processing without custom model training","Teams wanting managed inference without container/Kubernetes complexity"],"limitations":["Limited to pre-configured models — no support for custom or fine-tuned models","Model catalog not documented; unclear which SOTA models are available","API latency not specified; managed endpoints may have higher latency than self-hosted inference","No batch processing API mentioned — each request billed separately, inefficient for bulk processing","Pricing structure not transparent; cost per API call not published"],"requires":["API key for authentication","HTTP client library (curl, requests, fetch, etc.)","Model input data (image URLs, audio files, text prompts)","Valid payment method for API call billing"],"input_types":["image URLs or base64-encoded images","audio files (format/codec not specified)","text prompts or parameters","HTTP POST requests with JSON payloads"],"output_types":["structured JSON responses with model outputs","image predictions (classifications, segmentations, detections)","audio transcriptions or embeddings","usage metrics and cost data"],"categories":["tool-use-integration","deployment-infrastructure"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"datacrunch__cap_4","uri":"capability://automation.workflow.infrastructure.as.code.provisioning.with.terraform.and.opentofu","name":"infrastructure-as-code provisioning with terraform and opentofu","description":"Enables declarative infrastructure provisioning via Terraform and OpenTofu providers, allowing users to define GPU instances, clusters, storage, and networking as code. Verda provider translates HCL (HashiCorp Configuration Language) into API calls to provision resources, manage state, and support infrastructure versioning and reproducibility. Reduces vendor lock-in by using standard IaC tooling and enables GitOps workflows for infrastructure management. Supports state management, variable interpolation, and module composition for complex multi-resource deployments.","intents":["Define reproducible GPU infrastructure as version-controlled code","Automate infrastructure provisioning in CI/CD pipelines","Manage infrastructure state and enable team collaboration on resource definitions","Reduce manual provisioning errors and enable infrastructure rollback"],"best_for":["DevOps teams managing infrastructure at scale","Organizations practicing infrastructure-as-code and GitOps","Teams requiring reproducible, version-controlled infrastructure","Projects integrating GPU provisioning into CI/CD pipelines"],"limitations":["Terraform/OpenTofu learning curve for teams unfamiliar with IaC","State file management complexity — requires secure remote state backend (S3, Terraform Cloud, etc.)","Provider documentation not evaluated; unclear which Verda resources are supported in Terraform","No mention of Terraform modules or community-contributed configurations","Drift detection and remediation not documented"],"requires":["Terraform 1.0+ or OpenTofu 1.0+","Verda Terraform provider (version not specified)","API credentials for Verda authentication","HCL knowledge and understanding of Terraform workflows","Remote state backend for team collaboration (optional but recommended)"],"input_types":["HCL configuration files (.tf)","variable definitions (.tfvars)","Terraform state files (local or remote)"],"output_types":["provisioned GPU instances, clusters, storage, networking","Terraform state with resource metadata","infrastructure outputs (instance IPs, endpoint URLs, etc.)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"datacrunch__cap_5","uri":"capability://tool.use.integration.verda.cli.for.resource.management.and.monitoring","name":"verda cli for resource management and monitoring","description":"Command-line interface for provisioning, managing, and monitoring GPU instances, clusters, storage, and networking resources. Supports resource creation/deletion, SSH access management, storage operations, and real-time monitoring of resource utilization (GPU memory, compute, network). CLI abstracts API complexity and provides shell-friendly commands for scripting and automation. Integrates with standard Unix tools (pipes, grep, jq) for advanced resource queries and monitoring.","intents":["Quickly provision and tear down GPU instances without Terraform complexity","Monitor GPU utilization and resource costs in real-time","Automate resource management in shell scripts and cron jobs","Manage SSH keys and access control for instances"],"best_for":["Individual developers and researchers prototyping on GPUs","DevOps engineers automating resource management in scripts","Teams requiring quick resource provisioning without IaC overhead","Users comfortable with command-line interfaces"],"limitations":["CLI documentation not provided; command syntax and options unknown","No mention of shell completion or interactive mode","Scripting capabilities limited compared to API-based automation","No built-in resource templating or configuration management","Cross-platform support (Windows, macOS, Linux) not documented"],"requires":["Verda CLI installed (installation method not specified)","API credentials configured (authentication method not specified)","Shell environment (bash, zsh, fish, etc.)","Network access to Verda API endpoints"],"input_types":["CLI commands and flags","resource specifications (GPU type, instance size, storage size)","configuration files (if supported)"],"output_types":["provisioned resources with metadata","resource status and utilization metrics","structured output (JSON, table format)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"datacrunch__cap_6","uri":"capability://automation.workflow.block.storage.and.shared.filesystem.provisioning","name":"block storage and shared filesystem provisioning","description":"Provides persistent block storage volumes and shared network filesystems (SFS) for GPU instances and clusters. Block storage attaches to individual instances as persistent disks; shared filesystems enable multiple instances to access same data simultaneously via NFS-like protocol. Supports volume snapshots, resizing, and backup. Eliminates need for external storage services (AWS EBS, GCP Persistent Disk) and enables data persistence across instance termination.","intents":["Store training datasets and model checkpoints persistently across instance restarts","Share training data across multiple GPU instances in a cluster","Create snapshots of datasets for reproducible training runs","Avoid re-downloading large datasets on each instance provisioning"],"best_for":["ML teams training models with large datasets (>100GB)","Distributed training requiring shared access to training data","Workflows with frequent instance restarts requiring persistent state","Teams requiring data backup and disaster recovery"],"limitations":["Shared filesystem performance characteristics not documented (latency, throughput, IOPS)","No mention of filesystem type (NFS, SMB, proprietary) or protocol","Snapshot and backup retention policies not specified","No mention of encryption at rest or in transit","Pricing for storage not transparent; cost per GB/month not published","No mention of storage quotas or rate limiting"],"requires":["GPU instance or cluster provisioning","Storage volume size specification","Filesystem mount point configuration","Network connectivity between instances and storage"],"input_types":["storage size (GB/TB)","storage type (block or shared filesystem)","snapshot source (for volume cloning)"],"output_types":["provisioned storage volume with mount point","storage metadata (size, usage, IOPS)","snapshot identifiers for backup/restore"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"datacrunch__cap_7","uri":"capability://data.processing.analysis.object.storage.for.model.artifacts.and.datasets","name":"object storage for model artifacts and datasets","description":"Provides S3-compatible object storage for storing model weights, training datasets, inference results, and other artifacts. Supports standard S3 API operations (PUT, GET, DELETE, LIST) and integrates with common ML tools (PyTorch, TensorFlow, Hugging Face Transformers) via S3 protocol. Enables cost-effective storage of large files without provisioning dedicated block storage, and supports lifecycle policies for automatic archival or deletion.","intents":["Store and version model checkpoints and weights for reproducibility","Host training datasets accessible from multiple GPU instances","Archive inference results and logs for analysis and compliance","Integrate with ML pipelines using standard S3 API"],"best_for":["ML teams managing multiple model versions and checkpoints","Workflows requiring long-term artifact storage (months/years)","Teams using S3-compatible tools (boto3, AWS CLI, etc.)","Cost-conscious deployments requiring cheap bulk storage"],"limitations":["S3 API compatibility level not documented; unclear which operations are supported","Pricing not transparent; cost per GB/month and per-request pricing not published","No mention of versioning, lifecycle policies, or retention management","Egress bandwidth costs not documented; data transfer costs may be significant","No mention of encryption, access control, or compliance features","No mention of replication or disaster recovery"],"requires":["S3-compatible client library (boto3, AWS CLI, etc.)","Object storage credentials (access key, secret key)","Bucket creation and naming","Network access to object storage endpoint"],"input_types":["files and directories (model weights, datasets, logs)","S3 API operations (PUT, GET, DELETE, LIST)","lifecycle policies and retention rules"],"output_types":["stored objects with URLs","object metadata (size, modification time, ETag)","usage metrics and billing data"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"datacrunch__cap_8","uri":"capability://automation.workflow.container.registry.for.custom.inference.images","name":"container registry for custom inference images","description":"In-house container registry for storing and managing Docker images used in serverless inference endpoints. Supports image push/pull via standard Docker CLI, image tagging and versioning, and automatic image scanning for vulnerabilities. Integrates with serverless inference deployment — users push image to registry, reference in endpoint configuration, and system pulls image during deployment. Eliminates need for external registries (Docker Hub, ECR) and keeps container images within EU infrastructure for GDPR compliance.","intents":["Store custom inference container images within EU infrastructure for GDPR compliance","Version and manage multiple inference image variants","Integrate container images with serverless inference endpoints","Avoid external registry dependencies and potential data transfer costs"],"best_for":["Teams deploying custom inference models with GDPR requirements","Organizations avoiding external registry dependencies","Workflows requiring tight integration between image storage and inference deployment","Teams with proprietary inference code requiring private image storage"],"limitations":["Registry features not documented; unclear if supports image signing, scanning, or retention policies","Image size limits not specified; large models may exceed storage quotas","No mention of image pull rate limiting or bandwidth throttling","Pricing for registry storage not transparent","No mention of multi-region replication or disaster recovery","Integration with CI/CD pipelines not documented"],"requires":["Docker CLI installed","Registry credentials (username/password or token)","Docker image built and ready to push","Network access to Verda registry endpoint"],"input_types":["Docker images (OCI format)","image tags and versions","push/pull commands"],"output_types":["stored container images with registry URLs","image metadata (size, layers, digest)","vulnerability scan results (if supported)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"datacrunch__cap_9","uri":"capability://data.processing.analysis.resource.monitoring.and.utilization.metrics","name":"resource monitoring and utilization metrics","description":"Provides real-time monitoring of GPU utilization (compute, memory, temperature), CPU usage, network throughput, and storage I/O for provisioned instances and clusters. Exposes metrics via CLI commands, web dashboard (implied), and API endpoints. Enables cost optimization by identifying underutilized resources and performance debugging by correlating metrics with training job progress. Supports alerting and historical metric retention for capacity planning.","intents":["Monitor GPU utilization during training to identify bottlenecks and optimize code","Track resource costs and identify underutilized instances for cost optimization","Debug performance issues by correlating GPU metrics with training metrics","Plan capacity for future workloads based on historical utilization patterns"],"best_for":["ML teams optimizing training performance and resource efficiency","DevOps engineers managing GPU infrastructure costs","Researchers debugging distributed training issues","Organizations requiring resource accountability and chargeback"],"limitations":["Metric granularity and retention period not documented","No mention of alerting thresholds or notification mechanisms","API for metric retrieval not documented; unclear if supports Prometheus, Datadog, or other monitoring integrations","No mention of custom metrics or application-level monitoring","Dashboard features not documented; unclear if supports custom visualizations","No mention of metric export or integration with external monitoring systems"],"requires":["Provisioned GPU instances or clusters","Verda CLI or API access for metric queries","Optional: monitoring dashboard access (credentials not specified)"],"input_types":["instance/cluster identifiers","metric type and time range queries","alerting threshold definitions (if supported)"],"output_types":["time-series metrics (GPU utilization %, memory usage, temperature)","aggregated statistics (average, peak, percentiles)","alerts and notifications (if configured)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"datacrunch__headline","uri":"capability://deployment.infra.european.cloud.gpu.provider.for.ai.training","name":"european cloud gpu provider for ai training","description":"DataCrunch is a European cloud platform that offers NVIDIA A100 and H100 GPU instances for AI training, ensuring GDPR compliance and bare-metal performance for organizations needing EU data residency.","intents":["best European cloud GPU provider","GPU instances for AI training in Europe","GDPR compliant cloud GPU services","affordable GPU cloud for AI workloads","NVIDIA A100 instances for AI training"],"best_for":["organizations requiring EU data residency","AI and ML workloads"],"limitations":[],"requires":[],"input_types":[],"output_types":[],"categories":["deployment-infra"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":56,"verified":false,"data_access_risk":"high","permissions":["Valid payment method (credit card or invoice)","Verda CLI or Terraform 1.0+ for infrastructure provisioning","NVIDIA GPU driver compatibility on client side for instance management","EU business registration or GDPR Data Processing Agreement with Verda","Distributed training framework (PyTorch DDP, DeepSpeed, Megatron-LM, or equivalent)","NVIDIA NCCL library for collective operations optimization","Verda CLI or Terraform for cluster provisioning","SSH access and ability to manage multi-node training job coordination","Understanding of distributed training concepts (data parallelism, gradient synchronization)","Job specification format (YAML, JSON, or proprietary format — not specified)"],"failure_modes":["Geographic constraint: EU-only deployment means no global multi-region distribution for latency-sensitive applications","No multi-region failover: Single geographic footprint creates availability risk vs AWS/GCP/Azure global infrastructure","Specific EU datacenter locations not publicly documented, limiting ability to optimize for specific country compliance","No mention of disaster recovery or cross-border backup options within EU","Fixed cluster sizes (16x, 32x, 64x, 128x) — no dynamic scaling for variable workloads; must provision entire cluster upfront","No spot/preemptible instances mentioned — full on-demand pricing for entire cluster duration","Cluster provisioning time not documented; likely 5-15 minutes vs instant single-GPU instances","No built-in cluster orchestration (Kubernetes) mentioned — requires manual distributed training framework setup","Custom pricing for enterprise clusters requires sales contact; no transparent rate cards","Job scheduling algorithm and fairness guarantees not documented","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.7,"quality":0.9,"ecosystem":0.15000000000000002,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.3,"quality":0.25,"ecosystem":0.15,"match_graph":0.25,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:21.548Z","last_scraped_at":null,"last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=datacrunch","compare_url":"https://unfragile.ai/compare?artifact=datacrunch"}},"signature":"04Tn8ClpZfEv/EMtYGQZTkS+OBz0lcdzEdnSJz5sz98FAWhLP70Zn5fAOUuzCCPIMuJEpa08Mwn5L6tG88QAAA==","signedAt":"2026-06-21T14:47:40.176Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/datacrunch","artifact":"https://unfragile.ai/datacrunch","verify":"https://unfragile.ai/api/v1/verify?slug=datacrunch","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}