{"id":"lambda-labs-api","name":"Lambda Labs Cloud API","homepage":"https://docs.lambdalabs.com/cloud/","repo_url":null,"category":"ai-ml","subcategories":["gpu-cloud","ml-infrastructure","training","cloud-compute"],"tags":["lambda-labs","gpu-cloud","a100","h100","ml-training","inference","rest-api","cloud-compute","cuda"],"what_it_does":"GPU cloud computing API providing on-demand and reserved access to NVIDIA H100, A100, and V100 GPU clusters for ML training, fine-tuning, and inference workloads.","use_cases":["Launching GPU instances for ML model training via REST API from agent pipelines","Programmatically managing GPU cluster lifecycle (start, stop, terminate)","Automated provisioning of GPU clusters for batch fine-tuning jobs","Cost-optimized GPU compute as an alternative to AWS/Azure for ML workloads","Persistent GPU instance management for long-running inference servers"],"not_for":["Serverless or auto-scaling compute (Lambda Labs is always-on instance management)","Non-GPU CPU workloads (overpriced for CPU-only work)","Teams requiring enterprise SLA beyond what Lambda Labs provides"],"best_when":"You need dedicated GPU instances at competitive prices for ML training or long-running inference, with simple REST API management.","avoid_when":"You need serverless GPU compute (use Modal), multi-cloud availability, or enterprise support.","alternatives":["modal-api","replicate-api","huggingface-api"],"af_score":80.5,"security_score":null,"reliability_score":null,"package_type":"mcp_server","discovery_source":["github"],"priority":"low","status":"evaluated","version_evaluated":"current","last_evaluated":"2026-03-01T09:50:05.766710+00:00","performance":{"latency_p50_ms":200,"latency_p99_ms":500,"uptime_sla_percent":99.5,"rate_limits":"Not publicly documented; typical cloud API limits apply","data_source":"llm_estimated","measured_on":null}}