{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"lambda-cloud","slug":"lambda-cloud","name":"Lambda Cloud","type":"platform","url":"https://lambdalabs.com/service/gpu-cloud","page_url":"https://unfragile.ai/lambda-cloud","categories":["deployment-infra"],"tags":[],"pricing":{"model":"usage-based","free":false,"starting_price":"$1.10/hr"},"status":"active","verified":false},"capabilities":[{"id":"lambda-cloud__cap_0","uri":"capability://automation.workflow.on.demand.nvidia.h100.a100.gpu.cluster.provisioning","name":"on-demand nvidia h100/a100 gpu cluster provisioning","description":"Provisions bare-metal or containerized NVIDIA H100 and A100 GPU clusters on-demand with sub-minute spin-up times through a cloud orchestration layer that manages hardware allocation, network configuration, and resource scheduling. Uses a capacity-pooling model where GPUs are pre-allocated across regional data centers and assigned to users via API or web dashboard, eliminating the multi-day wait times typical of reserved capacity models.","intents":["I need to train a large language model on 8-16 H100s without committing to a 1-year contract","I want to scale from 2 GPUs to 32 GPUs for a distributed training job without infrastructure setup","I need to run multiple concurrent training experiments on different GPU types to benchmark performance"],"best_for":["ML researchers and engineers running large-scale model training","startups and enterprises prototyping foundation models without capital expenditure","teams needing burst capacity for time-sensitive training runs"],"limitations":["Availability of H100s is constrained by global supply; peak demand may result in queuing","Per-minute billing means idle time is expensive; no automatic cost optimization for underutilized clusters","Regional availability varies; some regions may only offer A100s, not H100s","No built-in multi-region failover; cluster failure requires manual re-provisioning"],"requires":["AWS, GCP, or Azure account for billing integration (or direct credit card)","SSH key pair for cluster access","Sufficient account credit or payment method on file","Network connectivity to assigned cluster (public IP or VPN)"],"input_types":["cluster configuration (GPU count, GPU type, vCPU, RAM, region)","SSH public key","container image URI or base OS selection"],"output_types":["cluster endpoint (IP address, SSH connection string)","cluster status (running, provisioning, terminated)","resource metrics (GPU utilization, temperature, memory usage)"],"categories":["automation-workflow","infrastructure-as-code"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"lambda-cloud__cap_1","uri":"capability://automation.workflow.pre.configured.deep.learning.environment.templates","name":"pre-configured deep learning environment templates","description":"Provides pre-built container images and OS snapshots with PyTorch, TensorFlow, CUDA, cuDNN, and common training libraries (DeepSpeed, Hugging Face Transformers, vLLM) pre-installed and optimized for the target GPU. Users select a template at cluster creation time; the orchestration layer pulls the image and boots the cluster with all dependencies ready, eliminating 30-60 minutes of manual environment setup.","intents":["I want to start training immediately without installing CUDA, PyTorch, and dependencies","I need a reproducible training environment across multiple cluster runs","I want to use the latest optimized versions of DeepSpeed and Transformers without manual compilation"],"best_for":["ML engineers who want to minimize time-to-first-training-step","teams running standardized training pipelines across multiple experiments","researchers who need consistent environments for reproducibility"],"limitations":["Templates are curated by Lambda; custom library versions require manual installation post-boot","Template updates are infrequent; users may need to manually patch security vulnerabilities","No template versioning; rolling updates may break existing scripts expecting older library versions","Limited to Lambda's supported frameworks (PyTorch, TensorFlow); specialized frameworks require custom setup"],"requires":["Selection of template at cluster creation (PyTorch, TensorFlow, or custom)","Familiarity with the pre-installed library versions","SSH access to cluster for any post-boot customization"],"input_types":["template selection (enum: pytorch-latest, tensorflow-latest, custom-image-uri)","optional custom container image URI"],"output_types":["running cluster with pre-installed environment","environment manifest (installed library versions, CUDA version, cuDNN version)"],"categories":["automation-workflow","deployment-infra"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"lambda-cloud__cap_2","uri":"capability://automation.workflow.persistent.distributed.storage.with.cluster.attachment","name":"persistent distributed storage with cluster attachment","description":"Provides NFS-mounted or block-storage volumes that persist across cluster termination and can be shared across multiple concurrent clusters. Storage is provisioned in the same region/availability zone as the cluster to minimize latency; the orchestration layer automatically mounts volumes at cluster boot via fstab or cloud-init, exposing them as standard Linux mount points accessible to training jobs.","intents":["I need to store training datasets (100GB-10TB) that persist across multiple training runs","I want to share model checkpoints between a training cluster and an inference cluster","I need to accumulate training logs and metrics across multiple experiments without re-downloading data"],"best_for":["teams running iterative training experiments with large, reusable datasets","organizations with multi-stage ML pipelines (training → evaluation → inference)","researchers managing long-running projects with persistent state"],"limitations":["NFS throughput is limited to ~1-2 GB/s; not suitable for high-frequency I/O patterns (e.g., reading millions of small files per second)","Cross-region storage access incurs significant latency and egress charges","No built-in replication or backup; data loss risk if storage volume fails","Storage costs accumulate monthly; large datasets (>10TB) become expensive over time","No versioning or snapshot management; overwrites are permanent"],"requires":["Storage volume provisioned in the same region as the cluster","Sufficient storage quota on account","Mount point path specified at cluster creation or via cloud-init script"],"input_types":["storage size (GB or TB)","storage type (NFS or block)","mount point path (e.g., /data, /mnt/training-data)","optional initial data source (S3 URI, HTTP URL)"],"output_types":["mounted filesystem accessible at specified path","storage usage metrics (GB used, GB available)","I/O performance metrics (throughput, latency, IOPS)"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"lambda-cloud__cap_3","uri":"capability://automation.workflow.private.networking.and.vpc.isolation","name":"private networking and vpc isolation","description":"Allocates clusters within isolated virtual private clouds (VPCs) with configurable security groups, allowing users to restrict inbound/outbound traffic and establish private connectivity between clusters. Clusters receive private IP addresses by default; public IPs are optional and can be disabled for security-sensitive workloads. VPC peering or VPN tunnels can be configured to connect Lambda clusters to on-premises infrastructure or other cloud providers.","intents":["I need to ensure my training data and models never traverse the public internet","I want to restrict cluster access to specific IP ranges or team members only","I need to connect my Lambda cluster to an on-premises data center for data ingestion"],"best_for":["enterprises with strict data residency or compliance requirements (HIPAA, SOC 2, FedRAMP)","teams handling proprietary models or sensitive datasets","organizations integrating Lambda clusters into hybrid cloud architectures"],"limitations":["VPC peering setup requires manual configuration; no one-click integration with AWS/GCP VPCs","Private-only clusters cannot access public package repositories (PyPI, Docker Hub) without explicit proxy configuration","VPN tunnel setup adds 50-100ms latency for on-premises data transfers","No built-in DDoS protection or WAF; security relies on security group rules","Cross-region VPC peering is not supported; clusters must be in the same region to communicate privately"],"requires":["VPC configuration (CIDR block, subnet specification)","Security group rules (inbound/outbound port and protocol specifications)","Optional VPN credentials or VPC peering configuration","Network connectivity from client machine to VPC (direct or via VPN)"],"input_types":["VPC CIDR block (e.g., 10.0.0.0/16)","security group rules (protocol, port, source/destination CIDR)","optional VPN configuration (pre-shared key, endpoint IP)","optional VPC peering request (peer VPC ID, peer account ID)"],"output_types":["cluster private IP address","cluster public IP address (if enabled)","VPC status and security group configuration","VPN tunnel status (connected/disconnected)"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"lambda-cloud__cap_4","uri":"capability://automation.workflow.distributed.training.orchestration.and.multi.node.coordination","name":"distributed training orchestration and multi-node coordination","description":"Provides built-in support for distributed training across multiple GPUs and nodes via pre-configured NCCL (NVIDIA Collective Communications Library) settings, automatic rank assignment, and environment variable injection (MASTER_ADDR, MASTER_PORT, RANK, WORLD_SIZE). Users launch training scripts with a single command; the orchestration layer handles inter-node communication setup, GPU affinity, and collective operation optimization for the specific GPU topology.","intents":["I want to train a model on 16 H100s across 2 nodes without manually configuring NCCL and rank assignment","I need to run distributed training with automatic gradient synchronization and loss scaling","I want to benchmark training throughput across different numbers of GPUs without modifying my training script"],"best_for":["ML engineers training large models (>10B parameters) that require multi-GPU distribution","teams using PyTorch DDP or TensorFlow distributed strategies","researchers benchmarking scaling efficiency across different cluster sizes"],"limitations":["NCCL optimization is GPU-topology-specific; heterogeneous clusters (mixed H100/A100) may have suboptimal communication patterns","No automatic load balancing; uneven batch distribution across nodes can cause stragglers","Requires training script to use standard distributed training APIs (torch.nn.parallel.DistributedDataParallel); custom communication patterns need manual NCCL tuning","Inter-node bandwidth is limited by cluster network (typically 100 Gbps); all-reduce operations on large models can become bottlenecks","No built-in fault tolerance; node failure requires manual restart and checkpoint recovery"],"requires":["Training script using PyTorch DDP, TensorFlow distributed.Strategy, or equivalent","Multi-node cluster provisioned with matching GPU types","NCCL environment variables set (typically handled automatically by Lambda)","Shared filesystem or object storage for checkpoint synchronization"],"input_types":["cluster configuration (number of nodes, GPUs per node)","training script (Python file or container entrypoint)","distributed training framework (PyTorch DDP, TensorFlow, DeepSpeed)","optional NCCL tuning parameters (NCCL_DEBUG, NCCL_ALGO)"],"output_types":["training logs with per-node GPU utilization and throughput","distributed training metrics (samples/sec, gradient synchronization time)","checkpoint files saved to persistent storage"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"lambda-cloud__cap_5","uri":"capability://automation.workflow.usage.based.billing.with.per.minute.gpu.charging","name":"usage-based billing with per-minute gpu charging","description":"Charges users per minute of GPU usage (not per hour or per node), with pricing differentiated by GPU type (H100 vs A100) and region. Billing starts when the cluster is in 'running' state and stops immediately upon termination; no minimum commitment or reservation fees. Costs are aggregated hourly and billed to the user's account; detailed usage reports are available via dashboard or API.","intents":["I want to pay only for the GPU time I actually use, without long-term commitments","I need to understand the cost of each training experiment and optimize for cost efficiency","I want to run short experiments (5-10 minutes) without paying for a full hour"],"best_for":["startups and researchers with limited budgets who want to avoid capital expenditure","teams running many short experiments and needing granular cost tracking","organizations with variable workloads that don't justify reserved capacity"],"limitations":["Per-minute billing means idle clusters are expensive; users must actively manage cluster lifecycle to avoid cost overruns","No automatic cost optimization; users must manually terminate underutilized clusters","Pricing is higher per-GPU-hour than reserved capacity (AWS Savings Plans, GCP Commitments); long-running workloads are more expensive","No volume discounts or multi-year pricing; cost per GPU remains constant regardless of usage volume","Billing is not integrated with enterprise cost allocation tools (e.g., AWS Cost Explorer); manual tracking required"],"requires":["Valid payment method on file (credit card or prepaid account credit)","Sufficient account balance or credit limit","Awareness of current pricing (varies by region and GPU type)"],"input_types":["cluster configuration (GPU type, count, region)","cluster runtime duration (minutes)"],"output_types":["usage report (GPU-minutes consumed, cost per GPU type)","billing invoice (total cost, itemized by cluster)","cost forecast (estimated cost for planned usage)"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"lambda-cloud__cap_6","uri":"capability://automation.workflow.cluster.lifecycle.management.via.api.and.web.dashboard","name":"cluster lifecycle management via api and web dashboard","description":"Provides REST API and web UI for creating, monitoring, and terminating clusters with full state tracking (provisioning, running, stopping, terminated). API supports programmatic cluster creation with configuration parameters (GPU type, count, region, image); dashboard provides real-time monitoring of GPU utilization, temperature, memory usage, and network I/O. Cluster state transitions are logged and queryable for auditing and automation.","intents":["I want to automate cluster provisioning as part of my training pipeline (e.g., create cluster, run training, terminate)","I need to monitor GPU utilization and temperature in real-time to detect performance issues","I want to query historical cluster usage for cost analysis and capacity planning"],"best_for":["ML engineers building automated training pipelines with cluster orchestration","DevOps teams integrating Lambda into CI/CD workflows","organizations needing audit trails and usage reporting for compliance"],"limitations":["API rate limits may throttle rapid cluster creation/termination (typical: 10 requests/minute); batch operations require backoff logic","Dashboard metrics are updated every 30-60 seconds; real-time monitoring requires polling the API","No webhooks or event streaming; users must poll for cluster state changes","API authentication uses API keys (no OAuth); key rotation requires manual updates","No built-in cost alerts; users must implement custom monitoring to detect runaway costs"],"requires":["API key for authentication (generated in account settings)","Familiarity with REST API conventions (JSON payloads, HTTP status codes)","Optional: SDK for Python or other languages (if available)"],"input_types":["cluster configuration (GPU type, count, region, image, storage)","cluster ID or name (for querying/terminating)","optional filters (region, status, creation date)"],"output_types":["cluster details (ID, status, IP address, creation time, cost)","resource metrics (GPU utilization %, temperature, memory usage, network throughput)","usage history (clusters created, terminated, total GPU-minutes)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"lambda-cloud__cap_7","uri":"capability://automation.workflow.enterprise.grade.cluster.support.and.sla.guarantees","name":"enterprise-grade cluster support and sla guarantees","description":"Offers dedicated support for large-scale training runs (typically 16+ GPUs) with guaranteed uptime SLAs (e.g., 99.9%), priority access to GPU capacity during peak demand, and direct communication with Lambda engineers for troubleshooting. Support includes pre-flight cluster validation, performance tuning recommendations, and post-incident analysis for failed training runs.","intents":["I'm running a critical training job and need guaranteed GPU availability and fast support response","I want Lambda engineers to review my cluster configuration and recommend optimizations before training starts","I need a post-mortem analysis if a training run fails due to infrastructure issues"],"best_for":["enterprises running production training pipelines with SLA requirements","organizations training models worth millions of dollars in compute cost","teams needing expert guidance on distributed training optimization"],"limitations":["Enterprise support requires a minimum commitment (typically $10k-$50k/month); not available for pay-as-you-go accounts","SLA guarantees apply only to cluster uptime, not training job success (e.g., out-of-memory errors are not covered)","Support response times vary by severity (critical: 1 hour, high: 4 hours); non-critical issues may have longer resolution times","No guaranteed GPU capacity reservation; 'priority access' means faster provisioning, not guaranteed availability","Support is available during business hours (typically 9am-5pm PT); off-hours support may require additional fees"],"requires":["Enterprise support contract signed with Lambda","Minimum monthly commitment (varies by contract terms)","Designated point of contact for support escalation","Cluster configuration details provided to support team for pre-flight review"],"input_types":["cluster configuration (GPU type, count, region, expected duration)","training job details (model size, batch size, expected throughput)","optional: performance targets or optimization goals"],"output_types":["pre-flight validation report (configuration issues, optimization recommendations)","SLA agreement (uptime guarantee, response time SLA)","post-incident analysis (root cause, remediation steps, prevention measures)"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"lambda-cloud__cap_8","uri":"capability://automation.workflow.multi.region.cluster.deployment.with.regional.failover","name":"multi-region cluster deployment with regional failover","description":"Allows users to specify preferred regions and fallback regions at cluster creation time; the orchestration layer attempts to provision in the primary region and automatically falls back to secondary regions if capacity is unavailable. Users can query regional availability and pricing before cluster creation to make informed decisions about region selection.","intents":["I want to provision a cluster in my preferred region, but fall back to another region if capacity is full","I need to understand GPU availability and pricing across regions before deciding where to train","I want to distribute training across multiple regions for redundancy or data locality"],"best_for":["teams with geographic data locality requirements (e.g., training on data in a specific region)","organizations needing redundancy across regions for critical training jobs","researchers comparing training performance across different data center locations"],"limitations":["Fallback provisioning adds 2-5 minutes to cluster creation time (due to retry logic)","Cross-region data transfer incurs egress charges; training data must be replicated to each region","Regional pricing varies significantly; fallback regions may be substantially more expensive","No automatic multi-region load balancing; 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