{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"coreweave","slug":"coreweave","name":"CoreWeave","type":"platform","url":"https://www.coreweave.com","page_url":"https://unfragile.ai/coreweave","categories":["deployment-infra"],"tags":[],"pricing":{"model":"usage-based","free":false,"starting_price":"$1.21/hr"},"status":"active","verified":false},"capabilities":[{"id":"coreweave__cap_0","uri":"capability://automation.workflow.bare.metal.gpu.instance.provisioning.with.on.demand.hourly.billing","name":"bare-metal gpu instance provisioning with on-demand hourly billing","description":"Provisions dedicated bare-metal GPU instances across multiple NVIDIA architectures (H100, H200, B200, B300, L40, RTX PRO 6000) with per-hour billing granularity and immediate allocation. Uses a hyperscaler-style inventory management system to match customer requests to available hardware pools across North America regions, with no shared tenancy or noisy-neighbor effects typical of virtualized GPU clouds.","intents":["I need to rent 8x H100 GPUs for a 48-hour training job without long-term commitment","I want to test inference performance on different GPU architectures (H100 vs B200) at hourly rates","I need guaranteed bare-metal isolation for proprietary model training without hypervisor overhead"],"best_for":["AI research teams running large-scale training experiments","ML engineers prototyping on multiple GPU generations","enterprises requiring bare-metal isolation for security/compliance"],"limitations":["Hourly billing granularity means short jobs (< 1 hour) incur full hour charges; no per-minute or per-second billing","No automatic scaling or reservation system mentioned — capacity may be unavailable during peak demand","Spot pricing only available for RTX PRO 6000 (54% discount); premium GPUs (B200, B300) have no spot option","Minimum allocation unit is typically a full 8-GPU node; cannot rent individual GPUs from multi-GPU systems","No published SLA uptime guarantees or instance availability percentages"],"requires":["CoreWeave account with payment method","Kubernetes cluster or container orchestration capability to manage workloads","Network connectivity to North America region (latency/bandwidth requirements unknown)"],"input_types":["container image (Docker/OCI format)","workload specification (Kubernetes YAML or equivalent)"],"output_types":["allocated GPU instance with IP/hostname","billing record with hourly rate applied"],"categories":["automation-workflow","infrastructure-provisioning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"coreweave__cap_1","uri":"capability://automation.workflow.kubernetes.native.cluster.orchestration.with.automated.lifecycle.management","name":"kubernetes-native cluster orchestration with automated lifecycle management","description":"Deploys and manages Kubernetes clusters natively on CoreWeave infrastructure, using standard Kubernetes APIs for workload scheduling, resource management, and container orchestration. Abstracts away bare-metal provisioning complexity by exposing Kubernetes-standard interfaces (kubectl, YAML manifests, Helm charts) while handling underlying GPU node allocation, networking, and health management automatically.","intents":["I want to deploy my existing Kubernetes workloads (using Helm charts or kustomize) to GPU infrastructure without rewriting orchestration logic","I need to scale a multi-node training job across 16 H100 GPUs using standard Kubernetes resource requests","I want to use familiar kubectl commands to manage GPU workloads without learning a proprietary API"],"best_for":["teams already invested in Kubernetes (EKS, GKE, AKS experience)","MLOps engineers building CI/CD pipelines with Kubernetes-native tools","organizations seeking to avoid vendor-specific orchestration APIs"],"limitations":["Kubernetes API compatibility does not guarantee full feature parity with managed Kubernetes services (EKS/GKE); specific API versions and CRDs not documented","Auto-scaling policies and scaling speed not specified — unclear if HPA (Horizontal Pod Autoscaler) or custom scaling mechanisms are used","No multi-region failover or cross-region Kubernetes federation mentioned","Proprietary 'Mission Control' platform may require non-standard extensions to Kubernetes API","Documentation does not specify Kubernetes version support or upgrade cadence"],"requires":["Kubernetes 1.20+ (assumed; exact version not specified)","kubectl CLI installed locally","Docker/OCI container images for workloads","Familiarity with Kubernetes YAML manifests and resource definitions"],"input_types":["Kubernetes manifests (YAML)","Helm charts","container images (OCI format)"],"output_types":["running Kubernetes pods/deployments","cluster status and resource utilization metrics"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"coreweave__cap_10","uri":"capability://automation.workflow.regional.gpu.availability.with.north.america.infrastructure","name":"regional gpu availability with north america infrastructure","description":"Provides GPU infrastructure in North America region with published pricing and availability. Enables low-latency access for North American customers and compliance with data residency requirements for US-based organizations. Specific availability zones, redundancy, and failover mechanisms not documented.","intents":["I need GPU infrastructure in North America to meet data residency requirements for US customers","I want low-latency access to GPU infrastructure from my US-based development team","I need to understand regional availability and pricing to plan multi-region deployments"],"best_for":["US-based teams with data residency or compliance requirements","organizations seeking low-latency GPU access from North America","enterprises planning regional deployments"],"limitations":["Only North America region explicitly documented; additional regions unknown","No published availability zones or redundancy information within North America","No multi-region failover or disaster recovery capabilities mentioned","No published latency metrics from different US locations to CoreWeave infrastructure","No guidance on data residency compliance (HIPAA, GDPR, SOC 2) for North America region"],"requires":["CoreWeave account with North America region access","network connectivity to North America (latency/bandwidth requirements unknown)"],"input_types":["region selection (North America)","workload specification"],"output_types":["GPU instance allocated in North America","regional pricing and availability information"],"categories":["automation-workflow","infrastructure-provisioning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"coreweave__cap_11","uri":"capability://automation.workflow.96.cluster.goodput.optimization.for.gpu.utilization","name":"96% cluster goodput optimization for gpu utilization","description":"Achieves 96% cluster goodput (GPU utilization efficiency) through optimized scheduling, reduced context switching, and minimized idle time. This metric reflects the percentage of time GPUs are actively computing vs. idle or waiting for data, indicating efficient resource utilization and reduced wasted capacity. Implementation details (scheduling algorithms, resource management) not documented.","intents":["I want to understand GPU utilization efficiency on CoreWeave vs. other providers to validate cost-effectiveness","I need to optimize my workload scheduling to achieve high cluster goodput and reduce wasted GPU capacity","I want to benchmark cluster efficiency metrics to make informed infrastructure decisions"],"best_for":["cost-conscious teams seeking to maximize GPU utilization and minimize wasted capacity","organizations running mixed workloads (training, inference, batch processing) requiring efficient scheduling","enterprises with SLA requirements for resource utilization efficiency"],"limitations":["Definition of 'goodput' not specified; unclear if this includes I/O wait time, synchronization overhead, or only pure compute time","No baseline comparison provided; unclear if 96% is better than AWS/GCP or industry standard","No published methodology for measuring goodput or transparency into how this metric is calculated","Goodput may vary significantly by workload type (training vs. inference) but no per-workload metrics provided","No guidance on how to achieve 96% goodput with customer workloads; unclear if this is a platform capability or requires workload optimization"],"requires":["workloads optimized for GPU utilization (minimal I/O, efficient data loading)","monitoring and observability to measure cluster goodput","understanding of GPU utilization metrics and optimization techniques"],"input_types":["cluster configuration and workload specifications","monitoring data (GPU utilization, idle time, synchronization overhead)"],"output_types":["cluster goodput metrics (% GPU utilization)","optimization recommendations to improve goodput"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"coreweave__cap_12","uri":"capability://automation.workflow.10x.faster.inference.spin.up.time.vs.baseline","name":"10x faster inference spin-up time vs. baseline","description":"Achieves 10x faster inference instance startup time compared to an unspecified baseline, enabling rapid deployment of inference workloads and reduced cold-start latency. Likely achieved through optimized container image caching, pre-warmed GPU memory, and streamlined provisioning workflows. Baseline and absolute startup time not documented.","intents":["I want to deploy inference workloads with minimal cold-start latency for time-sensitive applications","I need to understand inference startup time to plan auto-scaling policies and SLA compliance","I want to benchmark CoreWeave inference startup vs. competitors to validate cost-effectiveness"],"best_for":["teams deploying inference workloads with strict latency SLAs","applications requiring rapid scaling in response to traffic spikes","organizations optimizing inference cost-per-request by minimizing startup overhead"],"limitations":["Baseline comparison not specified; unclear if 10x is vs. AWS, GCP, or generic Kubernetes","Absolute startup time not published; unclear if 10x faster means 1 second vs. 10 seconds or 100ms vs. 1 second","Startup time may vary significantly by GPU architecture, container size, and model size; no per-workload breakdown provided","No guidance on how to achieve 10x faster startup with customer workloads; unclear if this is automatic or requires optimization","No published methodology for measuring startup time or transparency into optimization techniques"],"requires":["inference workload with containerized model","understanding of cold-start latency and its impact on SLAs","monitoring to measure actual startup time in production"],"input_types":["container image with inference model","inference configuration (batch size, sequence length)"],"output_types":["inference instance startup time metrics","inference results after startup"],"categories":["automation-workflow","infrastructure-provisioning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"coreweave__cap_13","uri":"capability://automation.workflow.50.fewer.interruptions.per.day.vs.baseline","name":"50% fewer interruptions per day vs. baseline","description":"Reduces infrastructure interruptions (node failures, network issues, GPU errors) by 50% compared to an unspecified baseline, improving workload reliability and reducing manual intervention. Achieved through health monitoring, automated recovery, and infrastructure redundancy (specific mechanisms not documented). Baseline and absolute interruption rate not specified.","intents":["I want to run long-duration training jobs without frequent interruptions or manual restarts","I need to understand infrastructure reliability to plan SLA commitments for production workloads","I want to benchmark CoreWeave reliability vs. competitors to validate production readiness"],"best_for":["teams running long-duration training jobs (> 24 hours) requiring high reliability","enterprises with SLA requirements for infrastructure uptime","organizations seeking to minimize manual intervention and operational overhead"],"limitations":["Baseline comparison not specified; unclear if 50% reduction is vs. AWS, GCP, or generic Kubernetes","Absolute interruption rate not published; unclear if 50% fewer means 1 interruption/day vs. 2, or 0.1 vs. 0.2","Interruption types not defined; unclear if this includes planned maintenance, hardware failures, or network issues","No published SLA uptime percentage or MTTR (mean time to recovery)","No guidance on how to achieve 50% fewer interruptions with customer workloads; unclear if this is automatic or requires optimization"],"requires":["workloads with checkpoint/restart capability to survive interruptions","monitoring to measure actual interruption rates in production","understanding of reliability requirements and SLA targets"],"input_types":["workload specification with reliability requirements","checkpoint/restart configuration"],"output_types":["interruption event logs and metrics","reliability reports and SLA compliance status"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"coreweave__cap_2","uri":"capability://automation.workflow.infiniband.accelerated.multi.node.gpu.cluster.networking","name":"infiniband-accelerated multi-node gpu cluster networking","description":"Interconnects multiple GPU nodes using InfiniBand networking (specific bandwidth/topology not documented) to enable low-latency, high-throughput communication for distributed training and inference. Reduces inter-GPU communication bottlenecks compared to Ethernet-based clusters, critical for large-scale model training where collective communication (all-reduce, all-gather) dominates compute time.","intents":["I need to train a 70B parameter model across 8 H100 GPUs with minimal communication overhead","I want to run distributed inference with model parallelism across multiple GPU nodes without network latency becoming the bottleneck","I need to benchmark training throughput on a cluster with optimized networking vs. standard Ethernet"],"best_for":["teams training models > 30B parameters requiring distributed training","researchers optimizing collective communication patterns (NCCL, Gloo)","enterprises running large-scale inference with model/tensor parallelism"],"limitations":["InfiniBand topology and bandwidth specifications not published; unclear if full-mesh, fat-tree, or other topology is used","InfiniBand support may require custom NCCL/Gloo configurations; standard PyTorch distributed training may not automatically optimize for InfiniBand","No documented support for InfiniBand across multiple regions or availability zones","Switching costs if migrating to Ethernet-based clusters (NCCL tuning parameters may differ)","No published benchmarks comparing InfiniBand vs. Ethernet throughput on CoreWeave"],"requires":["distributed training framework with NCCL/Gloo support (PyTorch, TensorFlow, JAX)","multi-node cluster (minimum 2 GPU nodes)","workload that benefits from low-latency communication (training, not inference-only)"],"input_types":["distributed training script (PyTorch DistributedDataParallel, TensorFlow MultiWorkerMirroredStrategy)","NCCL environment variables or Gloo configuration"],"output_types":["reduced training time due to optimized collective communication","cluster network utilization metrics"],"categories":["automation-workflow","infrastructure-provisioning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"coreweave__cap_3","uri":"capability://automation.workflow.cluster.health.monitoring.and.automated.resilience.management","name":"cluster health monitoring and automated resilience management","description":"Provides integrated health monitoring and automated recovery for GPU clusters, including node health checks, GPU memory error detection, thermal monitoring, and automated node replacement or workload migration on failure. Implements 'deep observability' across cluster infrastructure to detect and mitigate failures before they impact running workloads, reducing manual intervention and cluster downtime.","intents":["I want to run a 72-hour training job without manual intervention if a GPU node fails","I need visibility into GPU memory errors, thermal throttling, and other hardware issues that could degrade training performance","I want automatic workload migration if a node becomes unhealthy, rather than manual job restart"],"best_for":["teams running long-duration training jobs (> 24 hours) that cannot tolerate interruption","enterprises requiring high availability and automated failover","ML engineers who want to focus on model development rather than infrastructure troubleshooting"],"limitations":["Specific health metrics, detection thresholds, and recovery actions not documented","No published MTTR (mean time to recovery) or SLA uptime percentages","Automated recovery mechanisms may cause workload interruption or data loss if not properly integrated with checkpoint/restart logic","No documented support for stateful workloads or persistent storage failover","Monitoring data retention period and historical analysis capabilities unknown"],"requires":["workloads with checkpoint/restart capability to survive node failures","Kubernetes cluster with health monitoring agents deployed","persistent storage (external to GPU nodes) for checkpoints if automatic recovery is required"],"input_types":["cluster configuration (health check intervals, thresholds)","workload metadata (restart policies, checkpoint locations)"],"output_types":["health status dashboard/metrics","automated node replacement or workload migration events","alerts/notifications on cluster health degradation"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"coreweave__cap_4","uri":"capability://automation.workflow.inference.optimized.gpu.instance.pricing.with.dedicated.inference.tier","name":"inference-optimized gpu instance pricing with dedicated inference tier","description":"Offers separate, lower-cost pricing for inference workloads compared to training, with per-hour rates optimized for inference throughput rather than peak training performance. Enables cost-effective serving of large language models and vision models by matching GPU allocation to inference utilization patterns (lower memory bandwidth requirements, higher batch sizes).","intents":["I want to serve a 70B parameter model at lower cost than training-tier GPU pricing","I need to understand the cost difference between training and inference workloads to optimize my deployment strategy","I want to run inference on B200 GPUs at a lower hourly rate than training workloads"],"best_for":["teams deploying large language models for production inference","startups optimizing inference costs to improve unit economics","enterprises running inference-heavy workloads (chatbots, content generation)"],"limitations":["Inference pricing only published for single-GPU instances; multi-GPU inference pricing not shown","No guidance on when to use inference vs. training pricing (e.g., batch size thresholds, throughput targets)","Inference tier may have different hardware configurations or performance characteristics not documented","No published benchmarks comparing inference throughput/latency on inference vs. training tier GPUs","Unclear if inference pricing applies to all GPU models or only specific architectures"],"requires":["inference workload (model serving, batch processing)","inference framework (vLLM, TensorRT, TorchServe, etc.)","understanding of inference throughput requirements to validate cost-effectiveness"],"input_types":["model weights (GGUF, SafeTensors, PyTorch format)","inference configuration (batch size, sequence length, quantization)"],"output_types":["inference results (text, embeddings, etc.)","billing record at inference-tier hourly rate"],"categories":["automation-workflow","infrastructure-provisioning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"coreweave__cap_5","uri":"capability://automation.workflow.spot.gpu.instance.provisioning.with.limited.availability","name":"spot gpu instance provisioning with limited availability","description":"Offers discounted spot pricing (54% discount for RTX PRO 6000) for interruptible GPU instances, allowing cost-sensitive workloads to access GPUs at lower rates in exchange for potential interruption. Currently limited to RTX PRO 6000 architecture; premium GPUs (B200, B300, H100) do not offer spot pricing, restricting this capability to lower-tier inference and development workloads.","intents":["I want to run a fault-tolerant batch inference job on RTX PRO 6000 GPUs at 54% discount","I need to optimize costs for development/testing workloads that can tolerate occasional interruptions","I want to understand spot pricing availability across different GPU architectures to plan cost-effective deployments"],"best_for":["teams running fault-tolerant batch workloads (data processing, non-critical inference)","developers testing models during development phase","cost-sensitive startups willing to trade availability for lower costs"],"limitations":["Spot pricing only available for RTX PRO 6000; premium GPUs (H100, H200, B200, B300) show 'N/A' for spot pricing","No published interruption rate, average instance lifetime, or SLA for spot instances","No documented mechanism for graceful shutdown or checkpoint saving before interruption","Spot availability may be unpredictable, making it unsuitable for time-sensitive workloads","No spot pricing for inference-tier instances; unclear if spot is available for training-tier only"],"requires":["workload with fault tolerance (checkpoint/restart capability)","RTX PRO 6000 GPU (only architecture with spot pricing)","acceptance of potential interruption and job restart"],"input_types":["container image with checkpoint/restart logic","workload specification requesting spot instance"],"output_types":["allocated spot GPU instance","billing record at 54% discounted rate","interruption notification (if applicable)"],"categories":["automation-workflow","infrastructure-provisioning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"coreweave__cap_6","uri":"capability://automation.workflow.cross.cloud.ai.workload.portability.with.multi.cloud.orchestration","name":"cross-cloud ai workload portability with multi-cloud orchestration","description":"Enables deployment of AI workloads across CoreWeave and other cloud providers (AWS, GCP, Azure) using unified orchestration, reducing vendor lock-in and allowing customers to optimize workload placement based on cost, availability, and performance. Leverages Kubernetes-standard APIs to abstract cloud-specific infrastructure details, enabling workloads to migrate between clouds with minimal code changes.","intents":["I want to run the same Kubernetes workload on CoreWeave for training and AWS for inference to optimize costs","I need to avoid vendor lock-in by ensuring my training pipeline can run on multiple cloud providers","I want to burst training workloads to CoreWeave when AWS capacity is unavailable"],"best_for":["enterprises with multi-cloud strategies seeking to avoid lock-in","teams optimizing costs by running workloads on the cheapest available cloud","organizations with existing AWS/GCP infrastructure seeking to add CoreWeave capacity"],"limitations":["Cross-cloud orchestration mechanism not detailed; unclear if this uses Kubernetes federation, custom controllers, or third-party tools","No published guidance on data transfer costs, latency, or bandwidth between clouds","Cloud-specific features (e.g., InfiniBand on CoreWeave vs. Ethernet on AWS) may not be portable without workload changes","No documented support for stateful workloads or persistent storage across clouds","Multi-cloud networking and security policies not specified"],"requires":["Kubernetes clusters on CoreWeave and at least one other cloud provider","workloads using standard Kubernetes APIs (no cloud-specific extensions)","network connectivity between clouds (VPN, direct connect, or public internet)"],"input_types":["Kubernetes manifests compatible with multiple clouds","workload placement policies (cost, latency, availability constraints)"],"output_types":["workload deployed across multiple clouds","cost and performance metrics per cloud"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"coreweave__cap_7","uri":"capability://automation.workflow.enterprise.support.with.24.7.dedicated.engineering.teams","name":"enterprise support with 24/7 dedicated engineering teams","description":"Provides enterprise-grade support with 24/7 availability and dedicated engineering teams for mission-critical AI deployments. Offers technical assistance for infrastructure troubleshooting, performance optimization, and workload deployment, with SLA commitments for response time and issue resolution (specific SLA terms not documented).","intents":["I need 24/7 support for a production inference cluster serving millions of requests daily","I want dedicated engineering support to optimize training performance on CoreWeave infrastructure","I need guaranteed response times and issue resolution SLAs for mission-critical workloads"],"best_for":["enterprises running production AI workloads with high availability requirements","teams lacking in-house Kubernetes/GPU infrastructure expertise","organizations with SLA requirements for customer-facing AI services"],"limitations":["Specific SLA terms (response time, resolution time, uptime %) not published","Support scope not defined; unclear if support covers customer application code or only CoreWeave infrastructure","No published support tiers or pricing; unclear if 24/7 support is included in standard pricing or requires premium tier","No documented escalation procedures or support channel options (email, phone, Slack, etc.)","No information on support team expertise or certifications"],"requires":["enterprise account with CoreWeave","support contract or premium tier subscription (terms unknown)","contact information and communication preferences"],"input_types":["support tickets/requests describing infrastructure issues","cluster logs and performance metrics for troubleshooting"],"output_types":["technical guidance and issue resolution","performance optimization recommendations","SLA compliance reports"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"coreweave__cap_8","uri":"capability://automation.workflow.managed.software.services.for.ai.frameworks.and.tools","name":"managed software services for ai frameworks and tools","description":"Provides pre-configured, managed software services for popular AI frameworks and tools (specific frameworks not documented), reducing setup complexity and enabling faster time-to-training. Abstracts away framework installation, dependency management, and configuration tuning, allowing teams to focus on model development rather than infrastructure setup.","intents":["I want to start training a model immediately without spending hours configuring PyTorch, CUDA, and NCCL","I need pre-optimized framework configurations for distributed training on CoreWeave GPUs","I want to use managed services for common ML tools (Jupyter, TensorBoard, etc.) without self-hosting"],"best_for":["teams new to GPU infrastructure seeking to minimize setup overhead","researchers wanting to focus on model development rather than infrastructure","organizations seeking pre-optimized framework configurations for CoreWeave hardware"],"limitations":["Specific managed services and supported frameworks not documented; unclear which frameworks are available (PyTorch, TensorFlow, JAX, etc.)","No published list of pre-installed tools, versions, or optimization configurations","Managed services may lag behind latest framework releases, creating compatibility issues","No documented support for custom framework versions or dependencies","Unclear if managed services are included in standard pricing or require additional cost"],"requires":["CoreWeave account with managed services enabled","familiarity with supported frameworks and tools","acceptance of pre-configured environments (limited customization)"],"input_types":["framework selection (PyTorch, TensorFlow, etc.)","training script or notebook"],"output_types":["pre-configured training environment with framework installed","training results and logs"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"coreweave__cap_9","uri":"capability://automation.workflow.gpu.hardware.diversity.across.training.and.inference.architectures","name":"gpu hardware diversity across training and inference architectures","description":"Offers a wide range of NVIDIA GPU architectures spanning multiple generations (H100, H200, B200, B300, L40, RTX PRO 6000, GH200) with varying VRAM, compute performance, and cost profiles. Enables customers to select optimal hardware for specific workloads (e.g., H100 for training, L40 for inference) and benchmark performance across architectures without vendor lock-in to a single GPU generation.","intents":["I want to benchmark training performance on H100 vs. B200 to understand the cost-benefit tradeoff","I need to select the right GPU architecture for my inference workload based on model size and latency requirements","I want to run different workloads on different GPU architectures (training on H100, inference on L40) to optimize costs"],"best_for":["teams evaluating GPU architectures for new workloads","researchers benchmarking performance across GPU generations","enterprises optimizing costs by matching GPU architecture to workload requirements"],"limitations":["No published performance benchmarks (training throughput, inference latency) across GPU architectures","No guidance on when to use each architecture (e.g., H100 vs. B200 for 70B model training)","Availability of specific GPU architectures not guaranteed; inventory may vary by region and time","No published roadmap for future GPU architectures or deprecation timeline for older GPUs","Pricing differences between architectures may not reflect performance differences, making cost-optimization unclear"],"requires":["understanding of workload requirements (model size, batch size, latency targets)","ability to run benchmarks across multiple GPU architectures","familiarity with GPU specifications (VRAM, memory bandwidth, compute performance)"],"input_types":["workload specification (model, batch size, sequence length)","GPU architecture selection"],"output_types":["training throughput or inference latency metrics","cost per unit of performance ($/TFLOPS, $/token/sec)"],"categories":["automation-workflow","infrastructure-provisioning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"coreweave__headline","uri":"capability://deployment.infra.high.performance.gpu.cloud.platform.for.ai.workloads","name":"high-performance gpu cloud platform for ai workloads","description":"CoreWeave is a specialized GPU cloud provider that delivers high-performance NVIDIA GPU infrastructure optimized for AI training and inference workloads, designed for mission-critical AI deployment at scale.","intents":["best GPU cloud provider for AI","GPU infrastructure for AI training","high-performance cloud for AI inference","NVIDIA GPU cloud solutions","Kubernetes-native AI deployment","enterprise GPU cloud services"],"best_for":["AI training and inference","enterprise-level deployments"],"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":["CoreWeave account with payment method","Kubernetes cluster or container orchestration capability to manage workloads","Network connectivity to North America region (latency/bandwidth requirements unknown)","Kubernetes 1.20+ (assumed; exact version not specified)","kubectl CLI installed locally","Docker/OCI container images for workloads","Familiarity with Kubernetes YAML manifests and resource definitions","CoreWeave account with North America region access","network connectivity to North America (latency/bandwidth requirements unknown)","workloads optimized for GPU utilization (minimal I/O, efficient data loading)"],"failure_modes":["Hourly billing granularity means short jobs (< 1 hour) incur full hour charges; no per-minute or per-second billing","No automatic scaling or reservation system mentioned — capacity may be unavailable during peak demand","Spot pricing only available for RTX PRO 6000 (54% discount); premium GPUs (B200, B300) have no spot option","Minimum allocation unit is typically a full 8-GPU node; cannot rent individual GPUs from multi-GPU systems","No published SLA uptime guarantees or instance availability percentages","Kubernetes API compatibility does not guarantee full feature parity with managed Kubernetes services (EKS/GKE); specific API versions and CRDs not documented","Auto-scaling policies and scaling speed not specified — unclear if HPA (Horizontal Pod Autoscaler) or custom scaling mechanisms are used","No multi-region failover or cross-region Kubernetes federation mentioned","Proprietary 'Mission Control' platform may require non-standard extensions to Kubernetes API","Documentation does not specify Kubernetes version support or upgrade cadence","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=coreweave","compare_url":"https://unfragile.ai/compare?artifact=coreweave"}},"signature":"Yx3ZuXCLm1LHhKdMd5kZGtGJ6nOapTrEfJ2JMfRPQ/g/V+I/+GRu0Uwn8RbTI5YzWFQDzEs++JS7hFSoHf0bDg==","signedAt":"2026-06-21T23:44:06.767Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/coreweave","artifact":"https://unfragile.ai/coreweave","verify":"https://unfragile.ai/api/v1/verify?slug=coreweave","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"}}