{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"valohai","slug":"valohai","name":"Valohai","type":"platform","url":"https://valohai.com","page_url":"https://unfragile.ai/valohai","categories":["deployment-infra"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"valohai__cap_0","uri":"capability://automation.workflow.git.integrated.pipeline.definition.and.version.control","name":"git-integrated pipeline definition and version control","description":"Valohai stores pipeline definitions (YAML/configuration format) alongside application code in Git repositories, enabling version-controlled ML workflows where pipeline structure, parameters, and code evolve together. The platform syncs with Git to track pipeline changes, trigger runs on commits, and maintain complete lineage between code versions and experiment runs. This approach eliminates separate pipeline storage systems and leverages existing Git workflows for reproducibility.","intents":["I want my ML pipeline definitions to live in the same Git repo as my training code so changes are tracked together","I need to trigger pipeline runs automatically when I push code changes to specific branches","I want to compare experiment results across different Git commits to understand how code changes affected model performance"],"best_for":["teams already using Git for code management","organizations wanting to treat ML pipelines as code with full version history","developers building CI/CD workflows for ML"],"limitations":["Pipeline definition format not publicly documented — requires learning Valohai-specific YAML schema","Tight coupling to Git means pipeline changes require Git commits; no UI-only pipeline editing for ad-hoc experiments","Git integration limited to code/config; experiment metadata and model artifacts stored in Valohai, creating partial portability"],"requires":["Git repository (GitHub, GitLab, Bitbucket, or self-hosted)","Valohai project linked to Git repository","Pipeline definition file in repository root or specified directory"],"input_types":["YAML pipeline configuration","Python/code files","Git commit metadata"],"output_types":["versioned pipeline runs","experiment metadata linked to Git commits","lineage tracking across code versions"],"categories":["automation-workflow","version-control"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"valohai__cap_1","uri":"capability://data.processing.analysis.automatic.experiment.tracking.with.metric.comparison.and.lineage","name":"automatic experiment tracking with metric comparison and lineage","description":"Valohai automatically captures experiment metadata (hyperparameters, metrics, artifacts, environment) during pipeline runs without explicit logging code, then provides dashboards for comparing metrics across runs and tracing complete lineage (code version → data version → model output). The platform uses a metadata collection layer that intercepts training outputs and correlates them with Git commits, dataset versions, and infrastructure configuration.","intents":["I want to automatically log all my training metrics and hyperparameters without writing custom logging code","I need to compare model performance across 50+ experiments to find the best hyperparameter configuration","I want to understand which data version, code commit, and hardware configuration produced a specific model"],"best_for":["teams running many experiments and needing systematic comparison","researchers tracking complex lineage across data, code, and model versions","organizations requiring audit trails for model governance and compliance"],"limitations":["Automatic tracking requires Valohai SDK integration (valohai.inputs(), valohai.outputs()); custom metrics need explicit logging","Comparison UI limited to metrics stored in Valohai — external logging systems require manual integration","Lineage tracking depends on Git integration; experiments without Git commits lose code version context","No built-in statistical significance testing or automated hyperparameter optimization — comparison is manual"],"requires":["Python SDK (valohai library)","Pipeline runs executed through Valohai (not local execution)","Git integration for code version tracking","Metrics output in standard formats (JSON, CSV, or via SDK)"],"input_types":["training metrics (loss, accuracy, custom metrics)","hyperparameters","model artifacts","environment metadata"],"output_types":["experiment comparison dashboards","lineage graphs (code → data → model)","audit logs with timestamps and user attribution","structured experiment metadata (JSON)"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"valohai__cap_10","uri":"capability://data.processing.analysis.real.time.cost.tracking.and.underutilization.alerts","name":"real-time cost tracking and underutilization alerts","description":"Valohai provides real-time visibility into compute costs across multi-cloud infrastructure, tracking spending per job, pipeline, and project. The platform generates alerts when infrastructure is underutilized (e.g., GPUs idle, compute allocated but unused), enabling teams to optimize resource allocation and reduce costs. Cost tracking integrates with the per-user licensing model, separating infrastructure costs from platform licensing.","intents":["I want to understand how much each experiment costs and identify expensive hyperparameter searches","I need alerts when my GPU cluster is underutilized so I can adjust resource allocation","I want to track total ML infrastructure spending across AWS, GCP, and on-premises"],"best_for":["teams with large compute budgets wanting cost visibility","organizations optimizing infrastructure spending across multiple clouds","teams with variable workloads needing to identify idle resources"],"limitations":["Cost optimization recommendations not mentioned — tracking is visibility-only, not prescriptive","No built-in cost control mechanisms (e.g., budget caps, auto-shutdown) mentioned","Underutilization alert thresholds and configuration not documented","Cost tracking accuracy depends on cloud provider billing APIs — latency and granularity unknown","No integration with cloud provider cost management tools (AWS Cost Explorer, GCP Cost Management) mentioned","Cost attribution to specific users or teams not documented"],"requires":["Cloud provider accounts with billing APIs enabled","Valohai agent with access to infrastructure cost data","Email or notification system for alerts"],"input_types":["infrastructure usage metrics (CPU, GPU, memory, duration)","cloud provider billing data"],"output_types":["cost dashboards per job/pipeline/project","underutilization alerts","cost trends and forecasts"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"valohai__cap_11","uri":"capability://tool.use.integration.pre.built.integrations.with.data.sources.and.ml.frameworks","name":"pre-built integrations with data sources and ml frameworks","description":"Valohai provides native integrations with popular data sources (Snowflake, BigQuery, Redshift), labeling platforms (Labelbox, V7 Labs), and ML frameworks (Hugging Face, Super Gradients) to simplify data loading and model integration. These integrations abstract authentication, data transfer, and API interactions, reducing boilerplate code. However, Valohai's architecture supports running arbitrary code, so teams are not limited to pre-built integrations.","intents":["I want to load training data from Snowflake without writing custom SQL and authentication code","I need to integrate Hugging Face models into my Valohai pipeline without manual API calls","I want to pull labeled data from Labelbox and automatically version it with my experiments"],"best_for":["teams using popular data sources and frameworks that have pre-built integrations","organizations wanting to reduce boilerplate for common integrations","teams with limited DevOps resources for custom integrations"],"limitations":["Limited to pre-built integrations — custom data sources require manual API integration","Integration documentation not provided — unclear how to use each integration","No mention of integration versioning or updates — unclear how breaking changes are handled","Authentication mechanism for integrations not documented","No integration with other popular tools (DVC, Weights & Biases, Neptune) mentioned","Integrations may lag behind upstream API changes"],"requires":["Credentials for integrated data source or framework","Network connectivity to external services","Valohai pipeline configuration referencing integration"],"input_types":["data source credentials","query parameters (SQL, API filters)","framework-specific configuration"],"output_types":["loaded data in pipeline","model artifacts from frameworks","integration metadata and logs"],"categories":["tool-use-integration","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"valohai__cap_12","uri":"capability://safety.moderation.audit.logging.and.governance.for.compliance","name":"audit logging and governance for compliance","description":"Valohai maintains comprehensive audit logs tracking all platform actions (experiment runs, model deployments, data access, user actions) with timestamps and user attribution. These logs enable compliance with regulatory requirements (HIPAA, SOC2, GDPR) and provide accountability for ML model decisions. Audit logs are stored in Valohai and can be exported for compliance audits. Specific log retention policies and encryption are not documented.","intents":["I need to prove which user trained a specific model and when for compliance audits","I want to track all access to sensitive training data for HIPAA compliance","I need to demonstrate reproducibility and traceability of model decisions for regulatory approval"],"best_for":["regulated industries (healthcare, finance, government) with compliance requirements","organizations with strict governance and accountability requirements","teams needing to demonstrate model reproducibility for audits"],"limitations":["Audit log retention policies not documented — unclear how long logs are retained","Log encryption and security not specified","Compliance certifications (SOC2, HIPAA, GDPR) not mentioned","Log export format and API not documented","No mention of log immutability or tamper-proofing","Audit log querying and filtering capabilities not detailed"],"requires":["Valohai project with audit logging enabled","User authentication and role-based access control configured","Compliance requirements documented"],"input_types":["user actions (experiment runs, deployments, data access)","system events (infrastructure changes, errors)"],"output_types":["audit logs with timestamps and user attribution","compliance reports","exported audit trails for external audits"],"categories":["safety-moderation","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"valohai__cap_2","uri":"capability://automation.workflow.multi.cloud.and.hybrid.infrastructure.orchestration.with.dynamic.resource.allocation","name":"multi-cloud and hybrid infrastructure orchestration with dynamic resource allocation","description":"Valohai abstracts compute infrastructure across AWS, GCP, Azure, on-premises, and private cloud environments through a unified job submission interface. Users define resource requirements (CPU, GPU, memory) in pipeline configurations, and Valohai's scheduler routes jobs to available infrastructure, auto-scaling compute up/down based on queue depth and workload. The platform supports Kubernetes, Slurm, and Docker-based execution, enabling teams to run the same pipeline across heterogeneous infrastructure without code changes.","intents":["I want to run the same training pipeline on AWS for development and on-premises for production without rewriting code","I need to automatically scale GPU resources up when many experiments queue and down when idle to control costs","I want to distribute training across multiple cloud providers to avoid vendor lock-in and optimize costs"],"best_for":["enterprises with multi-cloud strategies or hybrid cloud/on-premises setups","teams with variable compute needs (burst training, continuous inference)","organizations needing cost optimization across infrastructure providers"],"limitations":["Auto-scaling mechanism not documented — likely depends on cloud provider APIs or Kubernetes; scaling latency unknown","GPU availability depends on user's infrastructure; Valohai does not provide managed GPU pools","No built-in cost optimization (e.g., spot instances, reserved capacity) — cost tracking is visibility-only","Hybrid orchestration adds operational complexity; requires managing credentials and infrastructure access across providers","Cold start latency for job scheduling not specified; could be significant for interactive workloads"],"requires":["AWS, GCP, Azure, or on-premises infrastructure with Kubernetes/Slurm/Docker","Valohai agent deployed in each infrastructure environment","Network connectivity between Valohai control plane and compute environments","Cloud provider credentials or on-premises access configured"],"input_types":["pipeline definitions with resource requirements (CPU, GPU, memory)","job submission requests","infrastructure configuration (cloud provider, region, instance type)"],"output_types":["job execution across selected infrastructure","resource utilization metrics","cost tracking per job/pipeline","auto-scaling decisions and logs"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"valohai__cap_3","uri":"capability://data.processing.analysis.data.versioning.and.lineage.tracking.without.duplication","name":"data versioning and lineage tracking without duplication","description":"Valohai tracks dataset versions and their relationships to experiments through a versioning system that claims to avoid data duplication (mechanism unspecified). The platform maintains lineage between datasets, pipeline runs, and models, enabling users to understand which data version produced which model and to reproduce experiments with exact dataset snapshots. Integration with data sources (Snowflake, BigQuery, Redshift) and labeling platforms (Labelbox, V7 Labs) enables tracking of unstructured data lineage.","intents":["I want to know exactly which version of my training dataset was used to train a specific model","I need to reproduce an experiment from 3 months ago with the exact same data, even if the source dataset has changed","I want to track how data quality issues (e.g., label corrections) affected model performance across experiments"],"best_for":["teams with large datasets where versioning and reproducibility are critical","organizations with complex data pipelines involving multiple sources","regulated industries (healthcare, finance) requiring audit trails for data provenance"],"limitations":["Data deduplication mechanism not documented — unclear how Valohai avoids storing duplicate data or whether it's storage-efficient","Lineage tracking limited to data sources with pre-built integrations (Snowflake, BigQuery, Redshift, Labelbox, V7 Labs); custom data sources require manual tracking","No built-in data validation or quality checks — lineage is passive tracking, not active data governance","Data versioning tied to Valohai platform; exporting versioned datasets for use outside Valohai is not documented","Storage quotas and retention policies not specified"],"requires":["Data source integration (Snowflake, BigQuery, Redshift, or S3/cloud storage)","Valohai SDK or API to register dataset versions","Network connectivity to data sources"],"input_types":["dataset snapshots (CSV, Parquet, database queries)","data source metadata (table names, schemas)","labeling platform exports (Labelbox, V7 Labs)"],"output_types":["versioned dataset references","lineage graphs linking data → experiments → models","dataset comparison (schema changes, row counts)","audit logs for data access"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"valohai__cap_4","uri":"capability://automation.workflow.batch.and.real.time.model.inference.deployment","name":"batch and real-time model inference deployment","description":"Valohai supports deploying trained models for both batch inference (processing large datasets asynchronously) and real-time inference (serving predictions on-demand). The platform abstracts deployment infrastructure, allowing models to be deployed to the same multi-cloud environments used for training. Deployment configuration is defined in pipeline YAML, enabling version-controlled model serving. Real-time inference mechanism (API endpoints, containerization, scaling) is not detailed in documentation.","intents":["I want to deploy a trained model to production for batch scoring of new data without manual infrastructure setup","I need to serve real-time predictions from a model with automatic scaling based on request volume","I want to version and track which model version is deployed in production and roll back if needed"],"best_for":["teams deploying models trained in Valohai to production","organizations needing batch inference without managing Kubernetes or serverless infrastructure","teams wanting version-controlled model deployments tied to Git commits"],"limitations":["Real-time inference details not documented — unclear if endpoints are REST APIs, gRPC, or other protocols","No mention of inference optimization (quantization, distillation, batching) — likely requires manual model optimization","Deployment scaling mechanism not specified; auto-scaling behavior and latency SLAs unknown","No built-in A/B testing or canary deployment strategies mentioned","Model serving is secondary feature; primary offering is orchestration, not inference hosting","No public model serving endpoints or inference API marketplace"],"requires":["Trained model artifact stored in Valohai Model Hub or S3","Deployment configuration in pipeline YAML","Target infrastructure (AWS, GCP, Azure, or on-premises) with Valohai agent","Model serving runtime (Docker container or framework-specific)"],"input_types":["trained model artifacts (HDF5, SavedModel, ONNX, etc.)","deployment configuration (resource requirements, scaling policies)","inference request data (batch or real-time)"],"output_types":["deployed model endpoints","inference predictions","deployment logs and metrics","version tracking for deployed models"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"valohai__cap_5","uri":"capability://automation.workflow.distributed.training.orchestration.across.multiple.nodes","name":"distributed training orchestration across multiple nodes","description":"Valohai supports distributed training by orchestrating multi-node jobs across its infrastructure abstraction layer, enabling teams to scale training across multiple GPUs or CPUs without manual distributed training setup. The platform handles job coordination, resource allocation, and communication between nodes. Specific distributed training frameworks supported (Horovod, PyTorch DDP, TensorFlow distributed) are not documented.","intents":["I want to train a large model across 8 GPUs without manually configuring distributed training code","I need to scale training from single-GPU experiments to multi-node production training without rewriting code","I want to use distributed training on-premises and in the cloud with the same pipeline definition"],"best_for":["teams training large models that exceed single-GPU memory","organizations with heterogeneous infrastructure (on-premises + cloud) needing consistent distributed training","researchers experimenting with different distributed training strategies"],"limitations":["Supported distributed training frameworks not documented — unclear which frameworks (Horovod, PyTorch DDP, TensorFlow) are natively supported","Communication overhead and synchronization latency not specified","No built-in distributed training debugging or profiling tools mentioned","Distributed training configuration in pipeline YAML not documented — learning curve for setup","Network bandwidth requirements and inter-node communication patterns not specified"],"requires":["Multi-node infrastructure (Kubernetes cluster, Slurm HPC, or cloud VMs)","Distributed training framework (PyTorch, TensorFlow, etc.)","Training code compatible with distributed training (or Valohai abstraction layer)","Network connectivity between nodes with low latency"],"input_types":["training code with distributed training support","distributed training configuration (number of nodes, GPUs per node)","training data (must be accessible from all nodes)"],"output_types":["trained model from distributed training","training logs from all nodes","distributed training metrics (throughput, communication overhead)"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"valohai__cap_6","uri":"capability://planning.reasoning.hyperparameter.optimization.and.tuning","name":"hyperparameter optimization and tuning","description":"Valohai supports hyperparameter optimization by enabling teams to define parameter search spaces in pipeline configurations and automatically running multiple experiments with different hyperparameter combinations. The platform orchestrates parallel hyperparameter tuning jobs across available infrastructure and tracks results for comparison. Specific optimization algorithms (grid search, random search, Bayesian optimization) are not documented.","intents":["I want to automatically run 100 experiments with different hyperparameters and find the best configuration","I need to parallelize hyperparameter tuning across my GPU cluster to reduce search time","I want to track which hyperparameters had the biggest impact on model performance"],"best_for":["teams with compute resources for parallel hyperparameter tuning","researchers exploring large hyperparameter spaces","organizations wanting systematic hyperparameter optimization without custom code"],"limitations":["Optimization algorithms not documented — unclear if platform supports grid search, random search, Bayesian optimization, or other strategies","No built-in early stopping or adaptive sampling — all experiments may run to completion regardless of performance","Hyperparameter search space definition format not documented","No integration with AutoML frameworks (Optuna, Ray Tune, Hyperopt) mentioned","Results analysis is manual comparison — no automated recommendations for best hyperparameters"],"requires":["Pipeline definition with hyperparameter search space","Training code that accepts hyperparameters as arguments","Compute resources for parallel job execution","Metrics output for comparison"],"input_types":["hyperparameter search space definition","training code","training data"],"output_types":["multiple experiment runs with different hyperparameters","comparison of results across hyperparameter combinations","best hyperparameter configuration"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"valohai__cap_7","uri":"capability://planning.reasoning.human.in.the.loop.workflow.integration","name":"human-in-the-loop workflow integration","description":"Valohai supports human-in-the-loop workflows by enabling pipelines to pause for human review or decision-making before proceeding to next steps. This allows teams to implement approval gates (e.g., model validation before deployment), manual data labeling, or human feedback loops within automated pipelines. Specific implementation (UI for approvals, API for feedback) is not detailed.","intents":["I want to require manual approval before deploying a model to production","I need to collect human feedback on model predictions and retrain with corrected labels","I want to pause training pipelines for human review of intermediate results before continuing"],"best_for":["teams with regulatory requirements for human approval in ML workflows","organizations using active learning or human-in-the-loop model improvement","teams wanting to combine automated pipelines with manual quality gates"],"limitations":["Human-in-the-loop implementation details not documented — unclear how approvals are triggered, who can approve, or timeout behavior","No mention of UI for human review or feedback collection — may require custom integration","Feedback integration mechanism not specified — unclear how human decisions are fed back into pipelines","No built-in role-based access control for approvals mentioned","Scalability for high-volume feedback loops not addressed"],"requires":["Pipeline definition with pause/approval points","User roles and permissions configured in Valohai","Notification system for approval requests (email, Slack, etc.)"],"input_types":["pipeline execution state","data for human review (model predictions, metrics, etc.)","human decisions (approve/reject/feedback)"],"output_types":["pipeline continuation or termination based on approval","feedback data for model retraining","audit logs of human decisions"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"valohai__cap_8","uri":"capability://tool.use.integration.api.and.webhook.based.pipeline.triggering.and.integration","name":"api and webhook-based pipeline triggering and integration","description":"Valohai exposes REST APIs and webhooks enabling external systems (CI/CD, data platforms, monitoring tools) to trigger pipeline runs, query experiment results, and integrate with existing workflows. Pipelines can be triggered via API calls, scheduled on intervals, or triggered by Git events. Webhooks enable Valohai to notify external systems of pipeline completion or status changes. Specific API endpoints, authentication mechanisms, and webhook payload formats are not documented.","intents":["I want to trigger model retraining from my CI/CD pipeline when new training data arrives","I need to integrate Valohai experiments into my existing monitoring and alerting system","I want to query experiment results programmatically to build custom dashboards"],"best_for":["teams integrating Valohai into existing CI/CD and data pipelines","organizations building custom dashboards or monitoring systems","teams automating MLOps workflows across multiple tools"],"limitations":["API documentation not provided — no OpenAPI/Swagger specification available","Authentication mechanism not specified — unclear if API keys, OAuth, or other auth is used","Webhook payload format and retry behavior not documented","Rate limits and API quotas not specified","No SDKs mentioned for languages other than Python","API versioning and backward compatibility not addressed"],"requires":["Valohai API key or authentication credentials","Network connectivity to Valohai API endpoints","External system capable of making HTTP requests (CI/CD tool, data platform, etc.)"],"input_types":["API requests (pipeline trigger, experiment query)","webhook events from Valohai","Git events (push, pull request)"],"output_types":["pipeline run execution","experiment metadata and results","webhook notifications to external systems"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"valohai__cap_9","uri":"capability://memory.knowledge.model.hub.versioning.and.artifact.management","name":"model hub versioning and artifact management","description":"Valohai's Model Hub provides centralized storage and versioning for trained model artifacts, enabling teams to track model versions, metadata, and relationships to training runs. Models can be tagged, compared across versions, and deployed directly from the Hub. The Hub integrates with experiment tracking to link models to specific training runs and hyperparameters. Specific artifact formats supported (SavedModel, ONNX, HDF5, etc.) and storage backend are not detailed.","intents":["I want to store and version all my trained models in a central location with metadata about their performance","I need to compare two model versions to understand what changed and which performs better","I want to deploy a specific model version to production and track which training run produced it"],"best_for":["teams managing multiple model versions and needing to track lineage","organizations with governance requirements for model versioning and audit trails","teams deploying models from a central registry"],"limitations":["Artifact format support not documented — unclear which model formats (SavedModel, ONNX, HDF5, PyTorch) are supported","Storage backend not specified — unclear if models are stored in cloud object storage or Valohai-managed storage","Model comparison features not detailed — unclear if comparison is limited to metadata or includes model structure/weights","No mention of model signing, encryption, or security controls","Storage quotas and retention policies not specified","Model Hub is not a public registry — no community sharing or model discovery features"],"requires":["Trained model artifact in supported format","Valohai project with Model Hub enabled","Metadata (tags, description, performance metrics)"],"input_types":["model artifacts (SavedModel, ONNX, HDF5, etc.)","model metadata (tags, description, performance metrics)","training run information (hyperparameters, data version)"],"output_types":["versioned model artifacts","model metadata and lineage","model comparison results","deployment-ready model references"],"categories":["memory-knowledge","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"valohai__headline","uri":"capability://deployment.infra.mlops.platform.for.automated.machine.learning.workflows","name":"mlops platform for automated machine learning workflows","description":"Valohai is an MLOps platform that automates machine learning infrastructure, offering version-controlled pipelines, automatic experiment tracking, and multi-cloud orchestration for teams scaling ML in production.","intents":["best MLOps platform","MLOps for automated machine learning","top tools for machine learning deployment","MLOps solutions for scaling ML","MLOps platforms with experiment tracking"],"best_for":["teams scaling ML","automating ML workflows"],"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":["Git repository (GitHub, GitLab, Bitbucket, or self-hosted)","Valohai project linked to Git repository","Pipeline definition file in repository root or specified directory","Python SDK (valohai library)","Pipeline runs executed through Valohai (not local execution)","Git integration for code version tracking","Metrics output in standard formats (JSON, CSV, or via SDK)","Cloud provider accounts with billing APIs enabled","Valohai agent with access to infrastructure cost data","Email or notification system for alerts"],"failure_modes":["Pipeline definition format not publicly documented — requires learning Valohai-specific YAML schema","Tight coupling to Git means pipeline changes require Git commits; no UI-only pipeline editing for ad-hoc experiments","Git integration limited to code/config; experiment metadata and model artifacts stored in Valohai, creating partial portability","Automatic tracking requires Valohai SDK integration (valohai.inputs(), valohai.outputs()); custom metrics need explicit logging","Comparison UI limited to metrics stored in Valohai — external logging systems require manual integration","Lineage tracking depends on Git integration; experiments without Git commits lose code version context","No built-in statistical significance testing or automated hyperparameter optimization — comparison is manual","Cost optimization recommendations not mentioned — tracking is visibility-only, not prescriptive","No built-in cost control mechanisms (e.g., budget caps, auto-shutdown) mentioned","Underutilization alert thresholds and configuration not documented","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.7,"quality":0.9,"ecosystem":0.15000000000000002,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.3,"quality":0.25,"ecosystem":0.15,"match_graph":0.25,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:34.118Z","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=valohai","compare_url":"https://unfragile.ai/compare?artifact=valohai"}},"signature":"fT4ADgTIiK4mSbwHeixVg899HiqFibcfnW9KyJUMt/Z49IR/wPO1bAactjxI4op4+3VPMpypUm28JHbWCgKwDQ==","signedAt":"2026-06-22T02:49:08.993Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/valohai","artifact":"https://unfragile.ai/valohai","verify":"https://unfragile.ai/api/v1/verify?slug=valohai","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"}}