{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"railway","slug":"railway","name":"Railway","type":"platform","url":"https://railway.app","page_url":"https://unfragile.ai/railway","categories":["deployment-infra"],"tags":[],"pricing":{"model":"usage","free":false,"starting_price":"$5/mo"},"status":"active","verified":false},"capabilities":[{"id":"railway__cap_0","uri":"capability://automation.workflow.github.triggered.containerized.application.deployment","name":"github-triggered containerized application deployment","description":"Automatically deploys Docker containers from GitHub repositories on push or pull request events, with branch-based routing and automatic preview environment creation. Railway monitors GitHub webhooks, builds container images using Railpack (automatic configuration) or custom Dockerfiles, and routes traffic based on branch names. Preview environments are automatically torn down on merge, enabling zero-configuration staging workflows without manual environment management.","intents":["Deploy my FastAPI/Flask backend automatically whenever I push to main branch","Create temporary staging environments for every pull request to test changes before merging","Avoid manual Docker image building and registry management for my AI service","Set up CI/CD without writing YAML configuration files"],"best_for":["Solo developers building AI backends who want zero-config deployment","Teams migrating from manual Docker deployments to automated CI/CD","Startups prototyping AI services without DevOps infrastructure"],"limitations":["Build timeouts range from 10 minutes (Free tier post-trial) to 90+ minutes (Pro/Enterprise), limiting complex multi-stage builds","Build image size capped at 4 GB (Free), 100 GB (Hobby), unlimited (Pro/Enterprise) — large ML models may require Pro tier","Concurrent builds limited to 3 (Hobby) or 10 (Pro), creating bottlenecks in high-frequency deployment scenarios","No native support for monorepo deployments with path-based triggers — entire repo triggers builds"],"requires":["GitHub repository with Dockerfile or Railpack-compatible project structure","Railway account with connected GitHub OAuth","Public or private GitHub repository access"],"input_types":["GitHub repository URL","Dockerfile or auto-detected project type","Environment variables as Railway config"],"output_types":["Running containerized service with public URL","Deployment logs and build artifacts","Preview environment URLs per pull request"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"railway__cap_1","uri":"capability://automation.workflow.consumption.based.per.second.compute.billing.with.auto.scaling","name":"consumption-based per-second compute billing with auto-scaling","description":"Charges for CPU and memory consumption at granular per-second intervals ($0.00000772 per vCPU/second, $0.00000386 per GB/second) rather than fixed instance sizes, with automatic vertical scaling on Pro/Enterprise tiers that adjusts CPU/RAM allocation based on real-time workload demand. Horizontal scaling supports up to 50 replicas with automatic load balancing, enabling cost-efficient burst handling for variable-load AI services without pre-provisioning peak capacity.","intents":["Pay only for actual compute used by my inference API, not reserved capacity","Automatically scale up during traffic spikes and scale down during idle periods to minimize costs","Handle unpredictable ML workload patterns (batch inference, periodic retraining) without over-provisioning","Compare cost-per-request across different service configurations"],"best_for":["Startups running variable-load AI services with unpredictable traffic patterns","Teams deploying multiple small services that don't justify dedicated infrastructure","Researchers prototyping inference APIs with cost-conscious budgets"],"limitations":["Vertical auto-scaling available only on Pro/Enterprise tiers; Free/Hobby tiers have fixed resource allocations (0.5-1 vCPU, 0.5-1 GB RAM post-trial)","Horizontal scaling up to 50 replicas requires Enterprise tier; Hobby tier limited to 6 replicas, creating ceiling for high-concurrency workloads","No spot/preemptible instance option for cost-optimized batch processing — all compute billed at standard rates","Egress costs ($0.05/GB for public traffic) can exceed compute costs for data-heavy services; internal networking free but limited to 10 Gbps public bandwidth","Minimum 1 replica always running (no true serverless/cold-start option), incurring baseline costs even during idle periods"],"requires":["Railway account with payment method for post-trial usage","$5 monthly credits (Free tier) or $20+ (Hobby tier minimum)","Service configured with resource requests/limits"],"input_types":["Service CPU/memory allocation settings","Replica count configuration","Workload metrics (CPU%, memory%)"],"output_types":["Per-second billing granularity in usage dashboard","Auto-scaled replica count and resource allocation","Cost projections based on current consumption"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"railway__cap_10","uri":"capability://tool.use.integration.graphql.api.for.programmatic.infrastructure.management","name":"graphql api for programmatic infrastructure management","description":"Exposes a GraphQL API with 100+ methods enabling programmatic deployment, configuration, and monitoring of Railway services. The API is the same interface powering the Railway console, enabling infrastructure-as-code workflows and custom automation. API authentication uses Railway tokens, and responses include deployment status, service metrics, and configuration details.","intents":["Programmatically deploy a new service from a GitHub repository without using the Railway console","Query service metrics (CPU, memory, network) via API for custom monitoring dashboards","Automate service scaling (replica count, resource allocation) based on external signals (e.g., queue depth)","Integrate Railway deployments into custom CI/CD pipelines or infrastructure automation tools"],"best_for":["Teams building custom deployment automation or infrastructure-as-code tools","Developers integrating Railway into existing CI/CD pipelines (GitHub Actions, GitLab CI)","Advanced users requiring programmatic control beyond Railway console UI"],"limitations":["API documentation quality unknown — no API schema or endpoint reference provided in architectural analysis","GraphQL schema not documented — unclear which mutations/queries are available for common operations (deploy, scale, delete)","Rate limiting not documented — unclear if API has usage quotas or throttling","Error handling and retry logic not documented — unclear if API supports idempotent operations","No native SDKs documented — API access requires manual GraphQL query construction or third-party libraries","Authentication token management not documented — unclear if tokens can be scoped to specific projects or services"],"requires":["Railway API token (generated in Railway console)","GraphQL client library (e.g., Apollo Client, graphql-request)","Understanding of GraphQL query syntax"],"input_types":["GraphQL queries and mutations","Service configuration parameters","Authentication token"],"output_types":["Service deployment status","Infrastructure metrics and logs","Configuration details and environment variables"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"railway__cap_11","uri":"capability://tool.use.integration.cli.based.local.repository.deployment.and.management","name":"cli-based local repository deployment and management","description":"Railway CLI (25+ commands) enables deployment of local repositories without GitHub integration, supporting manual pushes and local testing workflows. CLI commands include service creation, configuration management, log streaming, and deployment status checks. Local deployments are useful for testing before pushing to GitHub or for CI/CD systems that don't integrate with GitHub.","intents":["Deploy my AI service from a local Git repository without pushing to GitHub first","Stream logs from a running service directly to my terminal for debugging","Manage service configuration (environment variables, replicas) from the command line","Integrate Railway deployments into custom CI/CD systems (Jenkins, GitLab CI, Buildkite)"],"best_for":["Developers preferring CLI workflows over web console","Teams using non-GitHub version control systems (GitLab, Gitea, local Git)","Custom CI/CD systems that don't integrate with GitHub (Jenkins, Buildkite)"],"limitations":["CLI command reference not documented — unclear which of 25+ commands are available and their syntax","Local deployment workflow not documented — unclear if CLI pushes code to Railway or requires Docker image","Authentication and token management not documented — unclear how CLI authenticates with Railway","No native support for monorepo deployments — unclear if CLI can deploy specific subdirectories","CLI version management not documented — unclear if CLI auto-updates or requires manual updates","Cross-platform support not documented — unclear if CLI works on Windows, macOS, and Linux"],"requires":["Railway CLI installed (version not specified)","Railway API token for authentication","Local Git repository with Dockerfile or Railpack-compatible project"],"input_types":["CLI commands (deploy, logs, config, etc.)","Service name and configuration","Local repository path"],"output_types":["Deployment status and logs","Service configuration details","Real-time log streaming"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"railway__cap_12","uri":"capability://data.processing.analysis.structured.json.logging.with.7.90.day.retention.and.log.forwarding","name":"structured json logging with 7-90 day retention and log forwarding","description":"Collects structured JSON logs from all services with configurable retention (7 days Hobby, 30 days Pro, 90 days Enterprise) and supports log forwarding to external systems. Logs are queryable and filterable by service, timestamp, and log level, enabling debugging and audit trails. Log forwarding enables integration with external log aggregation platforms (e.g., Datadog, Splunk) for long-term retention.","intents":["Debug my inference API by querying logs for specific error messages or request IDs","Forward all service logs to Datadog for long-term retention and analysis beyond 90 days","Filter logs by service and time range to investigate incidents","Export logs for compliance audits or post-incident analysis"],"best_for":["Teams debugging production issues and needing quick log access","Services requiring audit trails for compliance (HIPAA, SOC 2)","Teams using external log aggregation platforms (Datadog, Splunk, ELK)"],"limitations":["Log retention limited to 90 days (Enterprise) — long-term retention requires external log forwarding","Log forwarding configuration not documented — unclear which external systems are supported or how to configure","Log query capabilities not documented — unclear if full-text search, regex, or structured queries are supported","Log volume limits not documented — unclear if high-volume services (e.g., high-traffic APIs) have rate limits","No native log sampling or filtering at source — all logs collected and counted against retention quota","Structured logging format not documented — unclear if custom JSON fields are preserved or flattened"],"requires":["Railway services generating logs (stdout/stderr)","External log forwarding system (optional, for retention beyond 90 days)","Log query access via Railway console or API"],"input_types":["Service logs (stdout/stderr from containers)","Log forwarding configuration (if using external system)"],"output_types":["Queryable and filterable logs in Railway console","Structured JSON log format","Forwarded logs in external system"],"categories":["data-processing-analysis","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"railway__cap_13","uri":"capability://automation.workflow.template.based.service.deployment.with.2.000.pre.built.configurations","name":"template-based service deployment with 2,000+ pre-built configurations","description":"Provides 2,000+ pre-built deployment templates for common services (databases, frameworks, tools) that can be customized and deployed with one click. Templates are shareable and customizable, enabling teams to standardize service configurations and reduce deployment time. Templates include pre-configured environment variables, resource allocations, and health checks.","intents":["Deploy a PostgreSQL database with pre-configured backups and monitoring in one click","Use a FastAPI template to quickly scaffold a new AI service with best practices","Share a custom service template with my team to ensure consistent configurations","Reduce deployment time by starting from a template instead of configuring from scratch"],"best_for":["Teams standardizing service configurations across projects","Developers new to Railway who want quick-start templates","Organizations creating internal service templates for compliance and best practices"],"limitations":["Template customization capabilities not documented — unclear if templates can be modified before deployment or only after","Template sharing mechanism not documented — unclear if templates are public, private, or organization-scoped","Template versioning not documented — unclear if templates are updated or if old versions remain available","No template marketplace or discovery mechanism documented — unclear how to find templates beyond Railway's built-in 2,000+","Template maintenance not documented — unclear if community-contributed templates are supported or only official Railway templates","No template validation or testing — unclear if templates are tested before deployment"],"requires":["Railway account with project","Template selection from Railway's template library","Optional customization before deployment"],"input_types":["Template selection (from 2,000+ available)","Customization parameters (optional)","Service name and configuration"],"output_types":["Deployed service with template configuration","Pre-configured environment variables and resource allocations","Health checks and monitoring enabled"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"railway__cap_14","uri":"capability://automation.workflow.real.time.project.canvas.and.team.collaboration.pro.enterprise","name":"real-time project canvas and team collaboration (pro/enterprise)","description":"Provides a real-time visual project canvas showing all services, databases, and connections with drag-and-drop interface for managing infrastructure. Enables team collaboration with shared project access and real-time updates. Available only on Pro/Enterprise tiers. No explicit documentation on concurrent editor limits, conflict resolution, or audit trails.","intents":["Visualize all services and databases in a project at a glance","Drag and drop to create connections between services","Collaborate with team members on infrastructure changes in real-time","Understand service dependencies and data flow visually"],"best_for":["Teams managing complex multi-service architectures","Visual learners preferring graphical infrastructure management","Teams requiring real-time collaboration on infrastructure"],"limitations":["Real-time project canvas only on Pro/Enterprise tiers — Free/Hobby excluded","Concurrent editor limits not documented — unclear if multiple users can edit simultaneously","Conflict resolution not documented — unclear behavior if two users modify same service","Audit trail not documented — unclear if changes are tracked per user","Export/import of canvas not mentioned — unclear if visual layout is portable"],"requires":["Pro/Enterprise tier","Team members with project access","Modern web browser with WebSocket support"],"input_types":["Service/database selection","Drag-and-drop actions","Configuration changes"],"output_types":["Visual project diagram","Service connection graph","Real-time collaboration updates"],"categories":["automation-workflow","collaboration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"railway__cap_2","uri":"capability://data.processing.analysis.managed.postgresql.mysql.mongodb.and.redis.database.deployment","name":"managed postgresql, mysql, mongodb, and redis database deployment","description":"Provisions managed database instances (PostgreSQL, MySQL, MongoDB, Redis) as Railway services with automatic backups, point-in-time recovery, and connection pooling. Databases are deployed as containers within the same Railway project, enabling zero-configuration networking between services via internal DNS (service-to-service communication over private 100 Gbps network). Persistent volumes up to 5 TB store database files with automatic IOPS provisioning (3,000 read/write operations per second standard).","intents":["Deploy a PostgreSQL database for my AI application without managing AWS RDS separately","Connect my FastAPI service to a Redis cache for embedding storage without external configuration","Automatically back up my ML training data and model metadata without writing backup scripts","Scale database storage from 10 GB to 5 TB as my dataset grows without migration"],"best_for":["Solo developers building AI backends who want single-platform infrastructure","Teams avoiding multi-vendor complexity (Railway for compute + RDS for databases)","Startups with moderate data volumes (< 5 TB) that don't require multi-region replication"],"limitations":["No managed read replicas or multi-region replication — single-region deployments only (Enterprise can deploy across regions but as separate instances)","IOPS fixed at 3,000 read/write operations per second (standard tier); high-throughput ML pipelines may require custom Enterprise configuration","Backup retention and point-in-time recovery windows not documented — unclear if suitable for compliance-heavy workloads","No native connection pooling configuration exposed; connection limits depend on database tier and Railway's internal pooling","MongoDB support exists but less documented than PostgreSQL/MySQL; unclear if suitable for production ML feature stores","Persistent volume performance (100 Gbps private network) not independently benchmarked against dedicated database services"],"requires":["Railway project with available storage quota","Service-to-service networking enabled (automatic within Railway)","Database credentials stored as Railway environment variables"],"input_types":["Database type selection (PostgreSQL, MySQL, MongoDB, Redis)","Initial storage size (GB)","Database name and credentials"],"output_types":["Connection string (DATABASE_URL environment variable)","Managed database instance with automatic backups","Persistent volume with automatic IOPS provisioning"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"railway__cap_3","uri":"capability://automation.workflow.scheduled.cron.job.execution.with.5.minute.minimum.intervals","name":"scheduled cron job execution with 5-minute minimum intervals","description":"Executes containerized cron jobs on a schedule with 5-minute minimum interval granularity, enabling periodic ML tasks like model retraining, batch inference, and data pipeline runs. Jobs are deployed as Railway services with the same container infrastructure as web services, sharing access to databases and environment variables. Execution logs are retained for 7 days (Hobby), 30 days (Pro), or 90 days (Enterprise), enabling debugging of failed training runs.","intents":["Retrain my ML model every 6 hours without managing a separate scheduler like Celery","Run batch inference on new data every night and store results in PostgreSQL","Sync embeddings from external APIs to my vector database on a schedule","Clean up old model artifacts and logs automatically to manage storage costs"],"best_for":["Teams running periodic ML jobs (retraining, batch inference) without Kubernetes","Startups avoiding Airflow/Prefect complexity for simple scheduled tasks","Solo developers needing reliable cron execution without managing cron daemon"],"limitations":["5-minute minimum interval granularity — unsuitable for sub-minute polling or high-frequency tasks (e.g., real-time feature engineering)","No built-in retry logic or failure notifications documented — failed jobs may silently fail without alerting","No job queuing or concurrency control — overlapping job runs could cause resource contention if previous run exceeds 5 minutes","Log retention limited to 90 days (Enterprise) — long-term audit trails require external log forwarding","No native support for distributed job execution or task dependencies — complex ML pipelines require external orchestration","Cron syntax limited to standard Unix cron format; no human-readable scheduling UI"],"requires":["Containerized job image (Docker) with entrypoint script","Railway service configured with cron schedule","Environment variables for database credentials and API keys"],"input_types":["Cron schedule expression (e.g., '0 */6 * * *' for every 6 hours)","Docker image or GitHub repository with job code","Environment variables for job configuration"],"output_types":["Job execution logs (stdout/stderr)","Exit code (0 for success, non-zero for failure)","Execution timestamp and duration"],"categories":["automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"railway__cap_4","uri":"capability://automation.workflow.multi.region.deployment.with.automatic.load.balancing","name":"multi-region deployment with automatic load balancing","description":"Deploys services across 4 geographic regions (US East, US West, Europe West, Southeast Asia) with automatic load balancing and failover. Enterprise tier supports concurrent multi-region deployments with a single configuration, routing traffic based on geographic proximity or custom rules. Internal networking (100 Gbps private) enables low-latency service-to-service communication within regions, while public egress (10 Gbps) is shared across replicas.","intents":["Deploy my inference API to multiple regions to reduce latency for global users","Automatically fail over to a different region if one region experiences outage","Run the same service configuration across US and Europe without duplicating deployment code","Minimize egress costs by keeping data transfers internal within regions"],"best_for":["Teams serving global users with latency-sensitive AI services (inference APIs, real-time recommendations)","Enterprise customers requiring geographic redundancy and compliance with data residency rules","Services with uneven geographic traffic distribution (e.g., US-heavy but growing EU presence)"],"limitations":["Multi-region deployment limited to Enterprise tier; Hobby/Pro tiers deploy to single region only","No documented cross-region database replication — databases remain single-region, requiring application-level data sync","Public egress bandwidth shared across all replicas (10 Gbps total), creating potential bottleneck for high-throughput services","Geographic routing rules not documented — unclear if custom routing policies (e.g., weighted round-robin) are supported","No documented SLA for failover latency or recovery time objectives (RTO)","Internal networking (100 Gbps) limited to within-region communication; cross-region traffic uses public internet"],"requires":["Enterprise tier subscription","Service configuration compatible with multi-region deployment","Stateless service design (or external state store for session persistence)"],"input_types":["Region selection (US East, US West, Europe West, Southeast Asia)","Replica count per region","Traffic routing policy (if custom)"],"output_types":["Service endpoints in each region","Automatic load balancing across regions","Regional metrics and logs"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"railway__cap_5","uri":"capability://safety.moderation.environment.variable.and.secrets.management.with.reference.support","name":"environment variable and secrets management with reference support","description":"Manages service configuration through encrypted environment variables with support for variable references (e.g., $DATABASE_URL), shared variables across services, and automatic secret encryption at rest. Variables are scoped to individual services or shared project-wide, enabling centralized configuration management without duplicating credentials across multiple services. Secrets are encrypted using Railway's key management system and never exposed in logs or deployment artifacts.","intents":["Store API keys, database credentials, and model paths as encrypted secrets without hardcoding in source code","Reference the same PostgreSQL connection string across multiple services (API, worker, scheduler) without duplication","Rotate secrets (e.g., API keys) without redeploying services","Manage environment-specific configuration (dev/staging/prod) without separate deployment files"],"best_for":["Teams managing multiple services with shared credentials (databases, external APIs)","Developers avoiding secrets in Git repositories and environment files","Startups with simple configuration needs (no complex secret rotation workflows)"],"limitations":["No documented secret rotation API or automated rotation policies — manual updates required for key rotation","Variable references limited to Railway-managed variables; no support for external secret stores (AWS Secrets Manager, HashiCorp Vault)","No audit logging for secret access — unclear if secret reads are logged for compliance purposes","Encryption key management not documented — unclear if customer-managed keys (CMK) are supported","No native support for secret versioning or rollback — previous secret values not retained","Variable size limits not documented — large configuration files may require external config management"],"requires":["Railway service with environment variable configuration UI or CLI","Plaintext secret values to be entered into Railway console or CLI","Service code that reads environment variables (standard practice)"],"input_types":["Variable name (e.g., DATABASE_URL, OPENAI_API_KEY)","Variable value (plaintext, encrypted at rest)","Variable scope (service-specific or project-wide)"],"output_types":["Encrypted environment variables injected into service containers","Variable references resolved at deployment time","Secrets never exposed in logs or build artifacts"],"categories":["safety-moderation","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"railway__cap_6","uri":"capability://data.processing.analysis.customizable.monitoring.dashboards.with.metric.visualization","name":"customizable monitoring dashboards with metric visualization","description":"Provides drag-and-drop customizable dashboards displaying real-time metrics (CPU, RAM, disk, network usage) for services and databases. Metrics are collected automatically from all Railway services without instrumentation, enabling quick visibility into infrastructure health. Dashboards support custom time ranges, metric aggregation, and export capabilities for post-incident analysis.","intents":["Monitor CPU and memory usage of my inference API to identify performance bottlenecks","Track disk usage of my PostgreSQL database to predict when storage scaling is needed","Compare network egress costs across services to optimize expensive data transfers","Create a dashboard for my team showing health of all AI services in one view"],"best_for":["Teams managing multiple services who need quick infrastructure visibility","Developers debugging performance issues without external monitoring tools","Startups avoiding Datadog/New Relic costs for basic infrastructure monitoring"],"limitations":["Metrics limited to infrastructure-level (CPU, RAM, disk, network) — no application-level metrics (request latency, error rates, custom business metrics)","No native integration with ML-specific observability (model inference latency, prediction accuracy drift, feature store health)","Dashboard customization limited to drag-and-drop UI — no programmatic dashboard-as-code or API for automation","Metric retention not documented — unclear if historical metrics available for long-term trend analysis","No native alerting on metric thresholds from dashboards — alerts require separate configuration","Export capabilities not documented — unclear if metrics can be exported to external systems for analysis"],"requires":["Railway services deployed and running","Access to Railway console or API","No additional instrumentation or agent installation required"],"input_types":["Service selection (which services to monitor)","Metric type (CPU%, memory%, disk usage, network)","Time range for visualization"],"output_types":["Real-time metric graphs and time-series data","Custom dashboards with multiple metric panels","Exported metric data (format not documented)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"railway__cap_7","uri":"capability://safety.moderation.alert.notifications.via.email.slack.and.discord.webhooks","name":"alert notifications via email, slack, and discord webhooks","description":"Sends configurable alerts to email, Slack, or Discord when service metrics exceed thresholds or usage limits are approached. Alerts support low/high urgency levels, enabling different notification channels for critical vs. informational events. Usage alerts can be configured with soft caps (warning) and hard caps (service termination), preventing unexpected billing surprises.","intents":["Get notified in Slack when my inference API CPU usage exceeds 80% so I can scale up","Receive email warning when my monthly Railway bill approaches my budget limit","Alert my team on Discord when a service crashes or becomes unhealthy","Prevent runaway costs by setting a hard cap that stops services when usage exceeds threshold"],"best_for":["Teams managing production AI services who need real-time incident notifications","Developers avoiding surprise bills by setting usage alerts","On-call engineers using Slack/Discord as primary communication channel"],"limitations":["Alert thresholds limited to metric-based conditions (CPU%, memory%, usage) — no support for custom application-level alerts (error rates, inference latency)","No alert routing or escalation policies — all alerts go to same channel regardless of severity","No alert deduplication or grouping — high-frequency alerts (e.g., CPU spikes) may spam notification channels","Webhook payload format not documented — unclear if alerts can be parsed by custom automation","No native integration with PagerDuty, Opsgenie, or other incident management platforms","Hard cap behavior (service termination) not documented — unclear if services restart automatically or require manual intervention"],"requires":["Railway service with metrics being collected","Slack workspace or Discord server for webhook integration","Email address for email alerts"],"input_types":["Alert type (metric threshold, usage limit)","Threshold value (e.g., 80% CPU, $100 monthly spend)","Urgency level (low/high)","Notification channel (email, Slack, Discord)"],"output_types":["Alert notification in configured channel","Alert metadata (service name, metric value, timestamp)","Soft cap warning or hard cap service termination"],"categories":["safety-moderation","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"railway__cap_8","uri":"capability://automation.workflow.custom.health.check.configuration.with.automatic.service.restart","name":"custom health check configuration with automatic service restart","description":"Configures custom HTTP health check endpoints with adjustable timeout and interval settings, automatically restarting services that fail health checks. Health checks are performed at configurable intervals (default not documented) and services are marked unhealthy after consecutive failures, triggering automatic restart. Failed health checks are logged and visible in Railway's monitoring dashboard.","intents":["Automatically restart my inference API if it becomes unresponsive (e.g., deadlock in model loading)","Configure a custom /health endpoint that checks database connectivity before reporting service as healthy","Detect and recover from partial failures (e.g., GPU memory leak) without manual intervention","Ensure my FastAPI service is always available by auto-restarting on health check failures"],"best_for":["Teams running production AI services that require high availability","Developers avoiding manual service restarts due to transient failures","Services with known failure modes (e.g., GPU memory leaks) that benefit from periodic restarts"],"limitations":["Health check configuration limited to HTTP endpoints — no support for TCP, gRPC, or custom protocol checks","Timeout and interval settings not documented — unclear if configurable or using Railway defaults","Restart behavior not documented — unclear if services restart immediately or after delay, or if restart count is limited","No health check history or metrics — failed checks not visible in monitoring dashboard (only in logs)","No support for graceful shutdown during restart — services may be forcefully killed mid-request","Health check endpoint must be accessible from Railway infrastructure — no support for private health checks"],"requires":["Service with HTTP health check endpoint (e.g., GET /health returning 200 OK)","Railway service configuration with health check URL","Service code that responds to health check requests"],"input_types":["Health check endpoint URL (e.g., /health)","HTTP method (GET, POST, etc.)","Timeout duration (seconds)","Check interval (seconds)"],"output_types":["Health check status (healthy/unhealthy)","Automatic service restart on failure","Health check logs and failure history"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"railway__cap_9","uri":"capability://data.processing.analysis.persistent.volume.storage.with.automatic.iops.provisioning","name":"persistent volume storage with automatic iops provisioning","description":"Provides persistent storage volumes up to 5 TB per service with automatic IOPS provisioning (3,000 read/write operations per second standard tier). Volumes are mounted to service containers at specified paths, enabling stateful services like databases and file caches. Storage is billed separately at $0.00000006 per GB/second, and volumes persist across service restarts and deployments.","intents":["Store model weights and embeddings on persistent disk for my inference API without re-downloading on restart","Persist training checkpoints and logs to disk so my ML training job can resume from last checkpoint","Cache feature vectors in a local vector database (e.g., Milvus) without external storage service","Store application state (e.g., conversation history) on disk for stateful services"],"best_for":["Services requiring persistent state (databases, file caches, model weights)","Teams avoiding external storage services (S3, GCS) for cost or latency reasons","Stateful ML services (training jobs, feature stores) that benefit from local disk caching"],"limitations":["IOPS fixed at 3,000 read/write operations per second — high-throughput workloads (e.g., real-time feature engineering) may experience I/O bottlenecks","No documented IOPS burst capability — sustained workloads exceeding 3,000 IOPS will be throttled","Volume performance not independently benchmarked — unclear if suitable for latency-sensitive workloads (e.g., real-time inference with local cache)","No native volume snapshots or backup mechanism — data loss risk if volume becomes corrupted","Volume size limits (5 TB) may be insufficient for large model repositories or training datasets","No documented volume encryption — unclear if data encrypted at rest or in transit"],"requires":["Railway service with persistent volume mount configured","Mount path specified in service configuration","Storage quota available in Railway project"],"input_types":["Volume size (GB, up to 5 TB)","Mount path in container (e.g., /data, /models)","Service to attach volume to"],"output_types":["Persistent storage accessible at mount path","Automatic IOPS provisioning (3,000 ops/sec standard)","Storage billing at $0.00000006 per GB/second"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"railway__headline","uri":"capability://deployment.infra.simplified.deployment.platform.for.applications.and.services","name":"simplified deployment platform for applications and services","description":"Railway is an infrastructure platform that simplifies the deployment of applications and services with one-click deploys from GitHub, automatic scaling, and support for various databases, making it a user-friendly alternative to AWS.","intents":["best deployment platform","deployment platform for AI backends","easy application deployment service","one-click deploy platform","best alternative to AWS for deployment"],"best_for":["developers looking for easy deployment solutions","teams using GitHub for version control"],"limitations":["no GPU support","lack of marketplace features"],"requires":["GitHub account for deployment"],"input_types":["GitHub repositories","container images"],"output_types":["deployed applications","APIs"],"categories":["deployment-infra"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":56,"verified":false,"data_access_risk":"high","permissions":["GitHub repository with Dockerfile or Railpack-compatible project structure","Railway account with connected GitHub OAuth","Public or private GitHub repository access","Railway account with payment method for post-trial usage","$5 monthly credits (Free tier) or $20+ (Hobby tier minimum)","Service configured with resource requests/limits","Railway API token (generated in Railway console)","GraphQL client library (e.g., Apollo Client, graphql-request)","Understanding of GraphQL query syntax","Railway CLI installed (version not specified)"],"failure_modes":["Build timeouts range from 10 minutes (Free tier post-trial) to 90+ minutes (Pro/Enterprise), limiting complex multi-stage builds","Build image size capped at 4 GB (Free), 100 GB (Hobby), unlimited (Pro/Enterprise) — large ML models may require Pro tier","Concurrent builds limited to 3 (Hobby) or 10 (Pro), creating bottlenecks in high-frequency deployment scenarios","No native support for monorepo deployments with path-based triggers — entire repo triggers builds","Vertical auto-scaling available only on Pro/Enterprise tiers; 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