{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"vast-ai","slug":"vast-ai","name":"Vast.ai","type":"platform","url":"https://vast.ai","page_url":"https://unfragile.ai/vast-ai","categories":["deployment-infra"],"tags":[],"pricing":{"model":"usage-based","free":false,"starting_price":"$0.10/hr"},"status":"active","verified":false},"capabilities":[{"id":"vast-ai__cap_0","uri":"capability://search.retrieval.real.time.gpu.marketplace.discovery.with.supply.demand.pricing","name":"real-time gpu marketplace discovery with supply-demand pricing","description":"Vast.ai operates a live GPU marketplace where 20,000+ distributed providers list hardware with real-time pricing that fluctuates based on supply and demand dynamics. Developers query available GPUs across 68+ model types (RTX 3060, B200, etc.) with filterable attributes (VRAM, CPU specs, bandwidth, region), and prices are transparently set by provider competition rather than fixed by Vast. The marketplace aggregates listings across 40+ global data centers and updates pricing continuously, enabling cost-optimized instance selection without long-term contracts or vendor lock-in.","intents":["Find the cheapest GPU for my specific workload right now","Compare GPU availability and pricing across regions and hardware types","Identify which GPU offers best value for my model's memory and compute requirements","Discover new GPU types entering the market with competitive pricing"],"best_for":["Cost-conscious ML teams running batch jobs or non-critical inference","Researchers prototyping on diverse hardware without budget constraints","Startups optimizing GPU spend across variable workloads"],"limitations":["Pricing volatility — spot instances can become unavailable or expensive during demand spikes, requiring fallback strategies","No SLA or uptime guarantees on spot instances — workloads must be fault-tolerant or use on-demand tier","Limited visibility into provider reliability or historical pricing trends — no price history or provider reputation scoring documented","Geographic distribution across community providers may introduce latency variability not quantified in docs"],"requires":["Vast.ai account with API key (VAST_API_KEY environment variable)","Python 3.6+ for SDK usage or curl/HTTP client for REST API","Understanding of GPU specs (VRAM, compute capability) for effective filtering"],"input_types":["filter parameters (GPU model, VRAM, CPU, bandwidth, region)","workload requirements (memory, compute, latency tolerance)"],"output_types":["structured GPU listing with real-time pricing","availability status per provider","estimated monthly cost projections"],"categories":["search-retrieval","marketplace-discovery"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vast-ai__cap_1","uri":"capability://automation.workflow.per.second.gpu.instance.provisioning.with.programmatic.scaling","name":"per-second gpu instance provisioning with programmatic scaling","description":"Vast.ai provisions GPU compute instances with per-second billing granularity (no rounding, no minimum hours), allowing developers to spin up, scale, and terminate instances on-demand via Python SDK, REST API, or CLI. The provisioning model supports three tiers: on-demand (guaranteed uptime, per-second billing), interruptible/spot (50%+ cheaper, preemptible), and reserved (1/3/6-month terms with up to 50% discount). Instances are Docker-based, deployable in seconds, and can be scaled programmatically via API calls without manual intervention or long-term contracts.","intents":["Spin up a GPU instance for a one-off inference job and pay only for compute time used","Automatically scale GPU capacity up or down based on workload demand via API","Switch between GPU types mid-experiment without termination penalties","Run cost-optimized batch jobs using spot instances with automatic fallback to on-demand"],"best_for":["Solo developers and small teams running episodic ML workloads (training, inference, data processing)","Batch processing pipelines with flexible timing and fault tolerance","Cost-sensitive startups optimizing cloud spend with dynamic scaling"],"limitations":["Spot instances are preemptible — no interruption guarantees or SLA, requiring application-level fault tolerance and checkpoint/resume logic","Cold start latency not specified — 'deploy in seconds' claim is vague; actual time from API call to GPU-ready instance unknown","No persistent storage documented — unclear if instances have block storage, how data persists across terminations, or egress costs","Scaling speed and warm-up time not documented — 'scale programmatically' lacks detail on latency or concurrency limits","No built-in load balancing or multi-instance orchestration — developers must manage instance coordination manually"],"requires":["Python 3.6+ with vastai SDK (pip install vastai) or curl/HTTP client for REST API","VAST_API_KEY environment variable for authentication","Docker image or template for workload deployment","Minimum $5 credit to start (usage-based billing thereafter)"],"input_types":["GPU instance configuration (GPU type, VRAM, CPU, region)","Docker image URI or template ID","scaling parameters (instance count, GPU type, duration)"],"output_types":["instance ID and SSH connection details","real-time pricing and cost estimates","instance status (running, terminated, failed)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vast-ai__cap_10","uri":"capability://automation.workflow.provider.earnings.program.for.gpu.host.monetization","name":"provider earnings program for gpu host monetization","description":"Vast.ai operates a 'Host GPUs and earn' program enabling individuals and organizations to monetize idle GPU hardware by listing it on the marketplace. Providers set their own prices and contract terms, competing in the marketplace to attract customers. The program aggregates 20,000+ GPUs from distributed providers worldwide, creating the supply side of the marketplace. However, revenue share model, provider requirements, onboarding process, and payout terms are not documented.","intents":["Monetize idle GPU hardware by listing it on Vast.ai marketplace","Set competitive pricing for GPU capacity based on market demand","Earn passive income from underutilized GPU infrastructure","Expand GPU supply globally by recruiting providers"],"best_for":["GPU owners with idle capacity seeking to monetize hardware","Data center operators looking to fill unused capacity","Individuals with high-end GPUs wanting passive income"],"limitations":["Revenue share model not documented — unclear what percentage of customer payments providers receive","Provider requirements not specified — unclear what hardware specs, uptime SLA, or security requirements are needed","Onboarding process not detailed — unclear how long it takes to list GPUs or what verification is required","Payout terms not documented — unclear how often providers are paid or what minimum payout threshold exists","Provider reliability scoring not public — no way for providers to build reputation or for customers to identify reliable providers","No provider support or SLA guarantees documented"],"requires":["GPU hardware (specs unknown)","Internet connection and network bandwidth","Vast.ai provider account (onboarding process unknown)","Compliance with Vast.ai terms of service"],"input_types":["GPU hardware specifications","pricing and contract terms","geographic location and data center info"],"output_types":["provider account and listing","earnings and payout information","customer bookings and utilization metrics"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vast-ai__cap_11","uri":"capability://tool.use.integration.framework.and.tool.integration.with.pytorch.vllm.and.comfyui","name":"framework and tool integration with pytorch, vllm, and comfyui","description":"Vast.ai instances support popular ML frameworks and tools including PyTorch, vLLM (for optimized LLM inference), and ComfyUI (for generative AI workflows). Integration is achieved through Docker-based deployments where frameworks are installed as dependencies in container images. Pre-configured templates may include optimized versions of these frameworks, though specific integration depth, performance optimizations, and compatibility details are not documented. Developers can use standard framework APIs without Vast-specific modifications.","intents":["Run PyTorch training scripts on GPU without code modifications","Deploy vLLM-based inference endpoints for optimized LLM serving","Execute ComfyUI workflows for generative AI tasks on GPU","Use standard framework APIs without learning Vast-specific abstractions"],"best_for":["ML engineers with existing PyTorch or vLLM codebases","Generative AI developers using ComfyUI for image/video generation","Teams migrating from on-premise GPU clusters to cloud"],"limitations":["Integration depth not documented — unclear if Vast provides optimized versions or just standard framework support","Framework versions not specified — unclear which PyTorch, vLLM, or ComfyUI versions are supported","Performance optimizations not detailed — no benchmarks showing speedup from Vast-specific tuning","Compatibility with custom framework extensions unknown — unclear if custom CUDA kernels or plugins work","No framework-specific documentation or examples provided"],"requires":["Docker image with framework installed","GPU with sufficient VRAM for framework and model","Familiarity with framework APIs (PyTorch, vLLM, ComfyUI)"],"input_types":["framework code (training script, inference pipeline, workflow)","model weights and data","framework configuration"],"output_types":["training logs and checkpoints","inference results and metrics","generated images/videos (ComfyUI)"],"categories":["tool-use-integration","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vast-ai__cap_12","uri":"capability://search.retrieval.global.gpu.availability.across.40.datacenters","name":"global gpu availability across 40+ datacenters","description":"Aggregates GPU inventory from 20,000+ instances across 40+ distributed datacenters worldwide, enabling developers to provision compute in geographically diverse locations. Availability is queryable by region and filtered by instance count (High: 120+, Medium: 40-119, Low: <40), allowing developers to find capacity in preferred regions or fallback to alternative locations. No specific region names or latency guarantees are documented.","intents":["Provision GPU instances in a specific geographic region for data residency or latency requirements","Find available GPU capacity globally when preferred region is fully booked","Distribute inference workloads across multiple regions for redundancy and lower latency","Comply with data sovereignty requirements by selecting specific datacenters"],"best_for":["Teams with geographic constraints (data residency, latency, compliance)","Global applications requiring distributed inference serving","Organizations seeking redundancy across multiple regions"],"limitations":["Specific datacenter names and locations not documented; unclear which regions are available","No latency guarantees or SLA for inter-region communication; unclear if suitable for low-latency applications","Availability filtering uses broad buckets (High/Medium/Low) rather than exact instance counts","No documented pricing differences across regions; unclear if some regions are more expensive","No automatic failover or multi-region orchestration; developers must implement region selection logic","Network connectivity between regions not documented; unclear if direct interconnects or internet-only"],"requires":["Vast.ai account with global access","Region selection via API or web portal (specific region names unknown)"],"input_types":["region filter (format and available regions unknown)","availability tier (High/Medium/Low)"],"output_types":["GPU instances in selected region with pricing and specs"],"categories":["search-retrieval","deployment-infra"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vast-ai__cap_13","uri":"capability://data.processing.analysis.api.driven.cost.optimization.and.pricing.transparency","name":"api-driven cost optimization and pricing transparency","description":"Exposes real-time pricing data via REST API (/api/v1/bundles/) enabling developers to query current GPU prices, compare costs across instance types and regions, and make cost-optimized provisioning decisions programmatically. Pricing is transparent and set by individual providers based on supply-demand, allowing developers to see exact prices before committing. Per-second billing granularity enables cost-aware workload scheduling and dynamic instance selection based on price thresholds.","intents":["Query current GPU prices via API to find the cheapest instance for a given workload","Implement cost-aware workload scheduling that selects GPU types based on price-to-performance ratio","Build dashboards showing GPU pricing trends and cost optimization opportunities","Automatically select spot vs on-demand vs reserved instances based on cost thresholds"],"best_for":["Cost-conscious ML teams optimizing GPU spending","Developers building cost-aware workload schedulers and orchestrators","Organizations with variable compute needs seeking dynamic cost optimization"],"limitations":["Pricing is dynamic and provider-set; no historical pricing data or trend analysis exposed","No price forecasting or predictive analytics; developers must implement their own prediction logic","No documented cost tracking or budget alerts; developers must build custom monitoring","Egress and bandwidth costs not documented; total cost of ownership unclear","No integration with cost management tools (CloudHealth, Kubecost); requires custom integration","Per-second billing granularity enables cost optimization but requires careful workload design to avoid idle charges"],"requires":["API key for accessing pricing endpoint","Bearer token authentication","Network access to https://cloud.vast.ai/api/v1/"],"input_types":["filter parameters (GPU type, VRAM, region, availability tier)"],"output_types":["JSON array of GPU instances with current pricing, specs, and provider details"],"categories":["data-processing-analysis","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vast-ai__cap_2","uri":"capability://automation.workflow.serverless.gpu.inference.with.openai.api.compatibility","name":"serverless gpu inference with openai api compatibility","description":"Vast.ai's serverless product auto-scales GPU inference endpoints with a PyWorker execution model, automatically benchmarking and optimizing workloads across GPU types. Endpoints expose an OpenAI API-compatible interface, allowing developers to swap Vast.ai serverless for OpenAI's API with minimal code changes. Instances scale to zero (pay only for compute time), with automatic load balancing and optimization across available GPU types. The serverless model abstracts GPU selection and scaling, targeting developers who want inference without infrastructure management.","intents":["Deploy a model endpoint that scales automatically from zero to handle variable traffic","Use Vast.ai serverless as a drop-in replacement for OpenAI API with lower costs","Run inference without managing GPU instances, scaling, or load balancing manually","Optimize inference costs by letting Vast auto-select best GPU for each request"],"best_for":["Teams migrating from OpenAI API seeking cost reduction with API compatibility","Startups deploying inference endpoints without DevOps infrastructure","Developers wanting serverless simplicity without Lambda/Functions constraints"],"limitations":["PyWorker execution model is proprietary to Vast.ai — not standard Lambda/Functions, limiting portability and community tooling","Cold start latency not specified — 'autoscale to zero' claim lacks detail on warm-up time or latency SLA","Timeout, memory, and concurrency limits not documented — unclear if there are constraints on request duration or payload size","OpenAI API compatibility scope unclear — unknown which endpoints, parameters, and models are supported or how differences are handled","No multi-region failover or disaster recovery documented","Supported Python versions and dependencies not specified"],"requires":["Python 3.6+ with Vast.ai SDK","VAST_API_KEY for authentication","Model compatible with PyWorker execution environment (details unknown)","Understanding of OpenAI API format for request/response mapping"],"input_types":["inference request in OpenAI API format (prompt, model, parameters)","PyWorker function definition (Python code)"],"output_types":["inference response in OpenAI API format (completion, tokens, metadata)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vast-ai__cap_3","uri":"capability://automation.workflow.docker.based.custom.workload.deployment.with.ssh.jupyter.access","name":"docker-based custom workload deployment with ssh/jupyter access","description":"Vast.ai instances accept Docker images for custom workload deployment, enabling developers to run arbitrary containerized applications (training, inference, data processing) on rented GPUs. Instances provide multiple connection methods: SSH for command-line access, Jupyter notebooks for interactive development, and web portal for management. Docker-based deployments are portable across providers and cloud platforms, reducing vendor lock-in. Instances are provisioned in seconds with full root access and support for custom dependencies, libraries, and frameworks (PyTorch, vLLM, ComfyUI, etc.).","intents":["Deploy a custom training script or inference pipeline on GPU without modifying code","Run interactive Jupyter notebooks on remote GPU for exploratory ML work","Execute arbitrary containerized workloads (data processing, batch jobs) on GPU","Migrate existing Docker-based ML pipelines to Vast.ai without refactoring"],"best_for":["ML engineers with existing Docker workflows seeking GPU acceleration","Researchers running custom training scripts or experimental code","Teams migrating from on-premise GPU clusters to cloud with minimal code changes"],"limitations":["No persistent storage documented — unclear if instances have block storage, how data persists across terminations, or egress costs for data transfer","SSH/Jupyter access requires manual connection setup — no built-in CI/CD integration or automated deployment pipelines documented","Docker image size and build time not constrained in docs — potential for slow deployments if images are large","No multi-instance orchestration or load balancing — developers must manage distributed workloads manually","Instance lifecycle management (cleanup, termination) not detailed — unclear how long instances persist or if automatic cleanup is available"],"requires":["Docker image with workload code and dependencies","SSH client or Jupyter client for connection","VAST_API_KEY for instance provisioning","Understanding of Docker and container networking"],"input_types":["Docker image URI or Dockerfile","instance configuration (GPU type, VRAM, region)","startup command or entrypoint"],"output_types":["SSH connection string and credentials","Jupyter notebook URL","instance logs and status"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vast-ai__cap_4","uri":"capability://automation.workflow.pre.configured.model.deployment.templates.with.one.click.launch","name":"pre-configured model deployment templates with one-click launch","description":"Vast.ai provides curated deployment templates for popular open-source models (Kimi K2.6, Gemma 4 26B/31B, Qwen3.5 27B, etc.) with pre-optimized configurations, dependencies, and startup scripts. Templates abstract away infrastructure setup, allowing developers to deploy models with a single click or API call without writing Docker files or managing dependencies. Templates include vision-language models with 256K context windows and multi-billion parameter MoE architectures, targeting developers who want fast model deployment without infrastructure expertise.","intents":["Deploy a popular open-source model to GPU in under five minutes without writing infrastructure code","Launch a vision-language model endpoint for inference without manual dependency management","Experiment with different model architectures (MoE, vision-language) without infrastructure overhead","Quickly prototype inference pipelines using pre-configured model templates"],"best_for":["Non-technical founders and product managers prototyping AI features","ML engineers wanting fast iteration without infrastructure setup","Teams evaluating multiple models quickly without deployment overhead"],"limitations":["Limited template library — only 4 models documented (Kimi K2.6, Gemma 4 26B/31B, Qwen3.5 27B); unclear if community can contribute or if library grows regularly","No customization details documented — unclear if templates support parameter tuning, quantization, or model fine-tuning","Template versioning and updates not specified — no documentation on how model versions are managed or updated","No performance benchmarks provided — unclear what throughput, latency, or cost to expect for each template","Dependency lock-in — templates may pin specific library versions that become outdated or incompatible"],"requires":["Vast.ai account with API key","Selection of compatible GPU type (VRAM requirements per model)","No coding required for basic deployment, but customization may require Docker knowledge"],"input_types":["template ID or model name","GPU type and region selection","optional configuration parameters (batch size, quantization, etc.)"],"output_types":["deployed model endpoint URL","inference API documentation","connection details (SSH, Jupyter, API key)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vast-ai__cap_5","uri":"capability://tool.use.integration.python.sdk.and.rest.api.for.programmatic.instance.management","name":"python sdk and rest api for programmatic instance management","description":"Vast.ai exposes a Python SDK (vastai package) and REST API for programmatic GPU instance management, enabling developers to search, filter, provision, scale, and terminate instances via code. The SDK provides both CLI and programmatic interfaces from a single package, supporting instance lifecycle operations (create, list, connect, terminate) and cost estimation. REST API uses Bearer token authentication (VAST_API_KEY) and exposes endpoints like /api/v1/bundles/ for instance queries. The API enables integration with CI/CD pipelines, orchestration frameworks, and custom automation scripts.","intents":["Programmatically search and filter GPUs by specs, price, and region in Python","Automate GPU instance provisioning and teardown in CI/CD pipelines","Build custom orchestration logic that scales GPU instances based on workload metrics","Integrate Vast.ai GPU provisioning into existing ML workflow automation"],"best_for":["ML engineers building custom orchestration and automation scripts","DevOps teams integrating GPU provisioning into CI/CD pipelines","Researchers automating large-scale hyperparameter sweeps across GPU types"],"limitations":["API rate limits and quotas not documented — unclear if there are throttling constraints or burst limits","Error handling and error codes not specified — no documentation on how to handle failures, retries, or timeouts","SDK version and changelog not provided — unclear what versions are available or how frequently updates occur","CLI command reference incomplete — full list of commands and options not documented in source material","No built-in retry logic or circuit breaker patterns documented — developers must implement their own fault tolerance","REST API response formats and schema not detailed — unclear what fields are returned or how to parse responses"],"requires":["Python 3.6+ with vastai SDK (pip install vastai)","VAST_API_KEY environment variable for authentication","HTTP client (curl, requests, etc.) for REST API usage","Understanding of GPU specs and filtering parameters"],"input_types":["filter parameters (GPU model, VRAM, CPU, bandwidth, region, price range)","instance configuration (GPU type, Docker image, region)","scaling parameters (instance count, duration)"],"output_types":["structured GPU listings with pricing","instance IDs and connection details","cost estimates and billing information","instance status and logs"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vast-ai__cap_6","uri":"capability://search.retrieval.multi.tier.pricing.with.on.demand.spot.and.reserved.instances","name":"multi-tier pricing with on-demand, spot, and reserved instances","description":"Vast.ai offers three pricing tiers optimized for different workload patterns: on-demand (guaranteed uptime, per-second billing, no minimums), interruptible/spot (50%+ cheaper, preemptible, fault-tolerant workloads), and reserved (1/3/6-month terms with up to 50% discount and volume discounts). All tiers use per-second billing granularity with no rounding, enabling precise cost control. Prices are set by supply-demand dynamics across 20,000+ distributed providers rather than fixed by Vast, allowing developers to shop for best value. No long-term contracts or exit penalties apply, enabling instant termination and GPU type switching.","intents":["Run cost-optimized batch jobs using spot instances with automatic fallback to on-demand","Reserve GPU capacity for predictable workloads with 1/3/6-month discounts","Minimize inference costs by using on-demand instances with per-second billing","Compare pricing across GPU types and regions to find best value for workload"],"best_for":["Cost-conscious teams running batch jobs, training, or non-critical inference","Startups optimizing cloud spend with variable workloads","Researchers with flexible timelines who can tolerate spot interruptions"],"limitations":["Spot instances are preemptible — no interruption guarantees or SLA, requiring application-level fault tolerance and checkpoint/resume logic","Pricing volatility — spot prices can spike during demand peaks, potentially making cost estimates unreliable","No price history or trend analysis documented — developers cannot predict future pricing or plan budgets based on historical data","Provider reliability not scored or documented — no way to identify stable providers vs. flaky ones","Egress and bandwidth costs not detailed — unclear if data transfer out of instances incurs additional charges","Reserved instance commitment is non-refundable — no flexibility if workload requirements change mid-term"],"requires":["Vast.ai account with minimum $5 credit to start","Understanding of workload fault tolerance (for spot instances)","Ability to estimate GPU requirements and duration"],"input_types":["instance configuration (GPU type, VRAM, CPU, region)","pricing tier selection (on-demand, spot, reserved)","duration (for reserved instances) or estimated runtime"],"output_types":["hourly/monthly cost estimates","total cost for instance lifetime","pricing comparison across GPU types and tiers"],"categories":["search-retrieval","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vast-ai__cap_7","uri":"capability://search.retrieval.global.gpu.availability.across.40.data.centers.with.region.filtering","name":"global gpu availability across 40+ data centers with region filtering","description":"Vast.ai aggregates GPU availability across 40+ global data centers, combining secure Vast-operated datacenters with community provider infrastructure. Developers can filter instances by region, enabling latency-optimized and data-residency-compliant deployments. The distributed model enables geographic redundancy and local compute for latency-sensitive workloads. However, specific regions, latency guarantees, and data residency options are not documented, and provider reliability across regions is not scored or tracked.","intents":["Deploy inference endpoints in specific regions to minimize latency for end users","Ensure data residency compliance by selecting GPUs in specific geographic regions","Distribute workloads across multiple regions for redundancy and fault tolerance","Find GPUs in regions with lowest cost or best availability"],"best_for":["Teams with latency-sensitive inference workloads requiring regional deployment","Companies with data residency requirements (GDPR, HIPAA, etc.)","Global applications needing geographic redundancy"],"limitations":["Specific regions and data center locations not documented — unclear which geographic areas are covered or how to select specific regions","Latency guarantees not provided — no SLA or latency targets documented for inter-region communication","Data residency compliance not detailed — unclear if Vast can guarantee data stays in specific regions or meets regulatory requirements","Provider reliability not scored by region — no way to identify stable providers in specific regions","Cross-region data transfer costs not specified — unclear if moving data between regions incurs egress charges","No multi-region failover or disaster recovery documented"],"requires":["Vast.ai account with API key","Understanding of latency requirements and data residency constraints","Knowledge of which regions are available (not documented)"],"input_types":["region or geographic area preference","latency tolerance","data residency requirements"],"output_types":["available GPUs in selected region","pricing and availability by region","estimated latency (if documented)"],"categories":["search-retrieval","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vast-ai__cap_8","uri":"capability://data.processing.analysis.cost.estimation.and.pricing.calculator.for.budget.planning","name":"cost estimation and pricing calculator for budget planning","description":"Vast.ai provides a pricing calculator enabling developers to estimate costs for GPU instances based on configuration (GPU type, VRAM, CPU, region), pricing tier (on-demand, spot, reserved), and duration. The calculator displays hourly, daily, and monthly cost projections, enabling budget planning and cost comparison across GPU types and regions. Real-time pricing data from the marketplace is used to generate estimates, accounting for supply-demand fluctuations. However, the calculator does not account for egress costs, data transfer, or other ancillary charges.","intents":["Estimate total cost for a training job before provisioning instances","Compare costs across GPU types and regions to find best value","Plan monthly budget for recurring inference workloads","Understand cost impact of switching between on-demand, spot, and reserved tiers"],"best_for":["Teams budgeting GPU spend before provisioning","Startups optimizing cloud spend and forecasting costs","Researchers comparing costs across different hardware configurations"],"limitations":["Does not account for egress costs or data transfer charges — total cost estimates may be incomplete","Spot pricing volatility not reflected — estimates assume current prices, which may change significantly","No historical pricing data or trend analysis — developers cannot forecast future costs based on past patterns","Ancillary costs (storage, networking, support) not included in estimates","No cost alerts or notifications if prices spike — developers must manually monitor pricing"],"requires":["Vast.ai account (no API key required for calculator)","Knowledge of GPU specs and workload requirements","Understanding of pricing tiers and duration"],"input_types":["GPU type and VRAM","CPU and bandwidth requirements","region preference","pricing tier (on-demand, spot, reserved)","duration (hours, days, months)"],"output_types":["hourly cost estimate","daily cost estimate","monthly cost estimate","total cost for specified duration","cost comparison across GPU types and regions"],"categories":["data-processing-analysis","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vast-ai__cap_9","uri":"capability://tool.use.integration.community.support.and.24.7.chat.assistance","name":"community support and 24/7 chat assistance","description":"Vast.ai provides community support through Discord (for peer discussions and help), 24/7 in-console chat support for account and technical issues, and email support (contact@vast.ai). Support channels enable developers to troubleshoot deployment issues, ask questions about GPU selection, and get help with API usage. However, support SLA, response times, and escalation procedures are not documented, and no community contribution or knowledge base features are mentioned.","intents":["Get help troubleshooting GPU instance deployment or connection issues","Ask questions about GPU selection and pricing in Discord community","Contact support for account, billing, or technical issues via chat or email","Learn from other developers' experiences and best practices in community"],"best_for":["Developers new to Vast.ai seeking guidance on GPU selection and deployment","Teams troubleshooting technical issues with instances or API","Community members sharing knowledge and best practices"],"limitations":["Support SLA and response times not documented — unclear how quickly issues are resolved","No knowledge base or FAQ beyond basic documentation — developers must ask support for common questions","Community features limited to Discord — no in-platform community forum, discussions, or knowledge sharing","No escalation procedures documented — unclear how critical issues are prioritized","No community contribution program — users cannot contribute templates, guides, or improvements"],"requires":["Vast.ai account for chat support","Discord account for community discussions (optional)","Email for contacting support"],"input_types":["support question or issue description","account/billing information (for support requests)"],"output_types":["support response and troubleshooting guidance","community discussion and peer advice","resolution or escalation"],"categories":["tool-use-integration","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vast-ai__headline","uri":"capability://deployment.infra.affordable.gpu.marketplace.for.ai.developers","name":"affordable gpu marketplace for ai developers","description":"Vast.ai is a GPU marketplace that connects AI developers with affordable, on-demand GPU compute resources from a variety of global providers, enabling flexible deployments for AI workloads.","intents":["best GPU marketplace","GPU compute for AI development","affordable GPU instances for machine learning","on-demand GPU resources for AI projects","spot GPU pricing for AI workloads"],"best_for":["AI developers seeking cost-effective GPU resources"],"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":["Vast.ai account with API key (VAST_API_KEY environment variable)","Python 3.6+ for SDK usage or curl/HTTP client for REST API","Understanding of GPU specs (VRAM, compute capability) for effective filtering","Python 3.6+ with vastai SDK (pip install vastai) or curl/HTTP client for REST API","VAST_API_KEY environment variable for authentication","Docker image or template for workload deployment","Minimum $5 credit to start (usage-based billing thereafter)","GPU hardware (specs unknown)","Internet connection and network bandwidth","Vast.ai provider account (onboarding process unknown)"],"failure_modes":["Pricing volatility — spot instances can become unavailable or expensive during demand spikes, requiring fallback strategies","No SLA or uptime guarantees on spot instances — workloads must be fault-tolerant or use on-demand tier","Limited visibility into provider reliability or historical pricing trends — no price history or provider reputation scoring documented","Geographic distribution across community providers may introduce latency variability not quantified in docs","Spot instances are preemptible — no interruption guarantees or SLA, requiring application-level fault tolerance and checkpoint/resume logic","Cold start latency not specified — 'deploy in seconds' claim is vague; actual time from API call to GPU-ready instance unknown","No persistent storage documented — unclear if instances have block storage, how data persists across terminations, or egress costs","Scaling speed and warm-up time not documented — 'scale programmatically' lacks detail on latency or concurrency limits","No built-in load balancing or multi-instance orchestration — developers must manage instance coordination manually","Revenue share model not documented — unclear what percentage of customer payments providers receive","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":"2026-05-24T12:16:34.118Z","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=vast-ai","compare_url":"https://unfragile.ai/compare?artifact=vast-ai"}},"signature":"yRIHqULZsV3PK3HyFJVwgvGoF+kmP/ZK5NqQOQlOVtL98oLS61E/uy75QKnJ7tuuGav1gl2vwBUlk57jJwNeCQ==","signedAt":"2026-06-20T16:44:03.039Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/vast-ai","artifact":"https://unfragile.ai/vast-ai","verify":"https://unfragile.ai/api/v1/verify?slug=vast-ai","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"}}