### Environment Variables Configuration Example

Source: https://github.com/devinschumacher/cloud-gpu-servers-services-providers/blob/main/_autodocs/api-reference/deployment-platforms.md

Common environment variables used across various deployment platforms for sensitive data and configuration.

```text
DATABASE_URL: connection string
API_KEY: service credentials
ENVIRONMENT: production/staging
DEBUG: true/false
```

--------------------------------

### ProviderComparison Interface

Source: https://github.com/devinschumacher/cloud-gpu-servers-services-providers/blob/main/_autodocs/types.md

Represents comparison data between multiple providers based on specific criteria. This interface is useful for creating matrices and selection guides for cloud services or infrastructure.

```typescript
interface ProviderComparison {
  // Comparison Criteria
  criteria: string                // e.g., "Total GPU VRAM"
  unit: string                    // e.g., "GB", "USD/hour"
  
  // Provider Values
  values: {
    providerName: string
    value: string | number
    ranking?: number              // 1st, 2nd, 3rd place
    notes?: string
  }[]
  
  // Context
  useCase?: string
  workloadType?: string
}
```

--------------------------------

### Production Startup Deployment (< $100/month)

Source: https://github.com/devinschumacher/cloud-gpu-servers-services-providers/blob/main/_autodocs/configuration.md

For production startups aiming for under $100/month, platforms like Render, Digital Ocean, or PythonAnywhere Hacker are suitable. These services range from $12 to $50 per month and include always-on capabilities, databases, and monitoring, making them perfect for MVPs and small teams.

```text
Platforms: Render, Digital Ocean, PythonAnywhere Hacker
Cost: $12 - $50/month
Includes: Always-on, database, monitoring
Perfect for: MVP, small teams
```

--------------------------------

### Minimal Cost Deployment for Learning

Source: https://github.com/devinschumacher/cloud-gpu-servers-services-providers/blob/main/_autodocs/configuration.md

For learning and minimal cost, platforms like Glitch, Heroku free, or PythonAnywhere free are ideal. These platforms offer $0 cost but come with tradeoffs such as app sleeping and limited resources, making them perfect for student projects and prototyping.

```text
Platforms: Glitch, Heroku free, PythonAnywhere free
Cost: $0
Tradeoff: App sleeps, limited resources
Perfect for: Student projects, prototyping
```

--------------------------------

### Production Scale Deployment (> $500/month)

Source: https://github.com/devinschumacher/cloud-gpu-servers-services-providers/blob/main/_autodocs/configuration.md

For production scale needs exceeding $500 per month, consider platforms like AWS, GCP, Azure, or Heroku Professional. These services offer enterprise features, scaling capabilities, and SLAs, making them ideal for business-critical applications.

```text
Platforms: AWS, GCP, Azure, Heroku Professional
Cost: Varies, typically $500+/month
Includes: Enterprise features, scaling, SLA
Perfect for: Business-critical applications
```

--------------------------------

### GPU Server Operating System Selection

Source: https://github.com/devinschumacher/cloud-gpu-servers-services-providers/blob/main/_autodocs/configuration.md

Lists supported Linux and Windows operating systems for GPU servers, noting that OS selection does not impact cost.

```yaml
Linux:
  - CentOS/RHEL
  - Ubuntu
  - Custom distributions
  
Windows:
  - Windows Server 2019
  - Windows Server 2022
  
Cost: Same across all OS options
```

--------------------------------

### Pricing Model Interface

Source: https://github.com/devinschumacher/cloud-gpu-servers-services-providers/blob/main/_autodocs/types.md

Defines the pricing structure for cloud services, including base rates, commitment options, and billing models. Essential for all GPU provider pricing documentation.

```typescript
interface PricingModel {
  // Base Pricing
  baseRate: {
    amount: number                // e.g., 1.57
    currency: string              // e.g., "USD"
    unit: string                  // e.g., "per hour"
  }
  
  // Commitment Options
  commitments?: {
    months: number                // e.g., 12, 36
    discountPercentage: number    // e.g., 20, 35
    estimatedSavings?: string     // e.g., "~20% savings"
  }[]
  
  // Billing
  billing: {
    model: "pay-as-you-go" | "subscription" | "reserved"
    minimumCommitment?: string
    monthlyCap?: boolean
    transparentPricing: boolean
  }
  
  // Special Terms
  specialPrograms?: {
    name: string                  // e.g., "Free Trial"
    details: string
    creditAmount?: string
  }[]
}
```

--------------------------------

### GPU Server System Memory Configuration

Source: https://github.com/devinschumacher/cloud-gpu-servers-services-providers/blob/main/_autodocs/configuration.md

Details minimum, maximum, and increment for system memory on L40S and H100 NVL servers, noting cost impact.

```yaml
L40S Server:
  Minimum: 32GB DDR5
  Maximum: 768GB DDR5
  Increment: Custom negotiation
  Cost Impact: Linear scaling
```

```yaml
H100 NVL Server:
  Minimum: System dependent
  Maximum: 1.5TB DDR5
  Increment: Custom negotiation
  Cost Impact: Linear scaling
```

--------------------------------

### Host System Configuration Interface

Source: https://github.com/devinschumacher/cloud-gpu-servers-services-providers/blob/main/_autodocs/types.md

Represents customizable host system options, including CPU, memory, storage, and operating systems. Used for detailing provider configurations and host system options.

```typescript
interface HostSystemConfiguration {
  cpu: {
    architecture: string          // e.g., "x86-64"
    vendors: string[]             // e.g., ["Intel", "AMD"]
    generation?: string           // e.g., "latest-generation"
  }
  
  memory: {
    minimum: string               // e.g., "32GB"
    maximum: string               // e.g., "768GB"
    type: string                  // e.g., "DDR5"
  }
  
  storage: {
    primary: string               // e.g., "NVMe"
    options: string[]             // e.g., ["Enterprise SSD"]
    maxCapacity?: string          // e.g., "7.68TB"
  }
  
  operatingSystems: string[]      // e.g., ["Linux", "Windows Server"]
  
  customization: {
    vCpuAdjustable: boolean
    ramAdjustable: boolean
    storageAdjustable: boolean
  }
}
```

--------------------------------

### FreeCredits TypeScript Interface

Source: https://github.com/devinschumacher/cloud-gpu-servers-services-providers/blob/main/_autodocs/types.md

Models free trial or credit offerings from providers, including program details, credit amounts, expiration, terms, and access methods. Useful for cost optimization and free tier documentation.

```typescript
interface FreeCredits {
  provider: string
  program: string                 // e.g., "Free Trial", "Student Pack"
  creditAmount?: string           // e.g., "$300", "166 GPU hours"
  expirationDays?: number
  terms: string[]
  howToAccess: string             // e.g., "New account", "GitHub Student Pack"
  special?: string                // e.g., "Fast.ai Special Discount"
}
```

--------------------------------

### Deployment Model Interface

Source: https://github.com/devinschumacher/cloud-gpu-servers-services-providers/blob/main/_autodocs/types.md

Represents different ways to deploy resources on a platform, detailing configuration, resource allocation, and performance characteristics. Used for deployment configuration and workload matching.

```typescript
interface DeploymentModel {
  // Configuration
  name: string                    // e.g., "Partial GPU", "Dedicated GPU"
  type: "fractional" | "dedicated" | "shared"
  description: string
  
  // Resource Allocation
  resourceAllocation: {
    gpuAccess: string             // e.g., "Exclusive", "Shared"
    gpuCount?: number
    vCpuRange?: string            // e.g., "1-64"
    memoryRange?: string          // e.g., "4GB-512GB"
  }
  
  // Performance
  performanceCharacteristics: {
    hypervisorOverhead: boolean
    nativeAccess: boolean
    scaling: "linear" | "sublinear" | "variable"
  }
  
  // Use Cases
  useCase: string[]               // e.g., ["Development", "Production"]
  costProfile: "budget" | "balanced" | "premium"
}
```

--------------------------------

### MLOps Environment Common Configuration Parameters

Source: https://github.com/devinschumacher/cloud-gpu-servers-services-providers/blob/main/_autodocs/configuration.md

Details common configuration parameters for MLOps environments, including compute specifications, environment variables, and scaling settings.

```yaml
Compute Specification:
  - CPU cores: 1-128
  - Memory: 512MB - 1TB+
  - GPU: 0-8 GPUs
  - Storage: Configurable

Environment Variables:
  - API keys and secrets
  - Database connections
  - Service endpoints
  - Debug flags

Scaling Configuration:
  - Min/max replicas
  - Auto-scaling metrics
  - Health check settings
  - Timeout policies
```

--------------------------------

### DeploymentPlatform Data Model

Source: https://github.com/devinschumacher/cloud-gpu-servers-services-providers/blob/main/_autodocs/types.md

Represents a web or API deployment service for ML models. Details include identity, supported runtimes and frameworks, deployment models, compute units, included services, pricing, and constraints.

```typescript
interface DeploymentPlatform {
  // Identity
  name: string                    // e.g., "Heroku", "Render"
  website: string
  
  // Runtimes & Frameworks
  supportedRuntimes: string[]     // e.g., ["Python", "Node.js"]
  frameworkSupport: string[]      // e.g., ["Django", "Flask"]
  containerSupport: boolean       // Docker support
  
  // Deployment Model
  deploymentModel: {
    type: "serverless" | "platform" | "infrastructure"
    autoDeployment: boolean
    gitIntegration: boolean
    cicdCapability: boolean
  }
  
  // Computing Units
  computeUnits?: {
    name: string                  // e.g., "Dyno", "Instance"
    tiers: {
      name: string
      memoryGB: number
      cpu: string
      monthlyPrice?: number
    }[]
  }
  
  // Services
  includedServices: {
    database: boolean
    cdn: boolean
    ssl: boolean
    monitoring: boolean
  }
  
  // Pricing
  pricing: PricingModel
  
  // Constraints
  constraints: {
    maxModelSize?: string
    sleepOnInactivity?: boolean
    sessionTimeout?: string
    concurrencyLimits?: string
  }
}
```

--------------------------------

### Cost-Conscious GPU Development Recommendation

Source: https://github.com/devinschumacher/cloud-gpu-servers-services-providers/blob/main/_autodocs/configuration.md

For cost-conscious development, consider Vast AI or free tiers for experimentation and learning. This strategy leverages fractional GPU access and spot pricing for expected costs between $0.30 and $0.50 per hour.

```text
Recommendation: Vast AI or free tiers
Strategy: Fractional GPU access, spot pricing
Expected Cost: $0.30 - $0.50/hour
Use Case: Experimentation, learning, prototyping
```

--------------------------------

### GPU Server Storage Configuration

Source: https://github.com/devinschumacher/cloud-gpu-servers-services-providers/blob/main/_autodocs/configuration.md

Specifies primary storage type, available options, maximum capacity, and pricing model for GPU servers.

```yaml
Primary: NVMe (configurable capacity)
Options:
  - Enterprise SSD
  - RAID protection available
Maximum: 7.68TB capacity
Cost: Additional per-GB pricing
```

--------------------------------

### GPUProvider Data Model

Source: https://github.com/devinschumacher/cloud-gpu-servers-services-providers/blob/main/_autodocs/types.md

Defines a comprehensive data model for a cloud GPU provider. It includes identity, core services, infrastructure details, availability, support, compliance, pricing, framework support, and workload affinities.

```typescript
interface GPUProvider {
  // Identity
  name: string                    // e.g., "Atlantic.net"
  website: string
  ranking?: number                // e.g., 1, 2, 3
  
  // Core Services
  gpuAccelerators: GPUSpecification[]
  hostConfigurations: HostSystemConfiguration[]
  deploymentModels: DeploymentModel[]
  
  // Infrastructure
  infrastructure: {
    datacenters: {
      region: string              // e.g., "North America"
      locations: string[]         // e.g., ["New York", "Dallas"]
    }[]
    globalFootprint: string
    networkCapability: string     // e.g., "100 Gbps"
  }
  
  // Availability
  availability: {
    uptime: string                // e.g., "100%"
    slaPeriod: string
    guaranteedSLA: string
  }
  
  // Support
  support: {
    hours: string                 // e.g., "24x7x365"
    geographyBased: string        // e.g., "US-based"
    specialization: string        // e.g., "GPU-focused"
    dedicatedAccounts: boolean
  }
  
  // Compliance
  certifications: ComplianceCertification[]
  
  // Pricing
  pricing: PricingModel
  
  // Framework Support
  frameworks: string[]            // e.g., ["CUDA", "cuDNN", "Docker"]
  
  // Workload Affinities
  workloads: string[]             // e.g., ["LLM", "Computer Vision"]
}
```

--------------------------------

### Flexible GPU Operations Pricing

Source: https://github.com/devinschumacher/cloud-gpu-servers-services-providers/blob/main/_autodocs/configuration.md

For flexible operations, Atlantic.net with a 12-month term provides a medium commitment with a 20% discount. This option balances cost savings with flexibility for GPU resource needs.

```text
Recommendation: Atlantic.net with 12-month term
Strategy: Medium commitment (20% discount)
Expected Cost: L40S ~$1.26/hour, H100 ~$3.15/hour
Savings: 20% vs on-demand
Payback: 8.33 months
```

--------------------------------

### MLOps Platform Tiers

Source: https://github.com/devinschumacher/cloud-gpu-servers-services-providers/blob/main/_autodocs/configuration.md

Overview of MLOps platforms categorized by cost and features, suitable for different user needs from research to enterprise.

```text
Platforms: MLflow (self-hosted), Flyte, KubeFlow
Cost: Infrastructure only
Requirements: Kubernetes or compute resources
Perfect for: Academic research, open source
```

```text
Platforms: ClearML free, Neptune.ai free tier, Gradient free
Cost: $0 - $10/month
Includes: Experiment tracking, basic serving
Perfect for: Small teams, learning
```

```text
Platforms: DataRobot, SageMaker, Vertex AI
Cost: $1000+ /month
Includes: Full lifecycle, support, compliance
Perfect for: Large organizations, regulated industries
```

--------------------------------

### ExperimentMetrics Interface

Source: https://github.com/devinschumacher/cloud-gpu-servers-services-providers/blob/main/_autodocs/types.md

Represents metrics and artifacts collected during machine learning experiments. This interface includes hyperparameters, mutable metrics, and artifact details, useful for tracking experiment progress and results.

```typescript
interface ExperimentMetrics {
  // Identifiers
  experimentId: string
  runId: string
  timestamp: string               // ISO 8601 format
  
  // Parameters (immutable)
  hyperparameters: {
    [key: string]: string | number | boolean
  }
  
  // Metrics (mutable, collected over time)
  metrics: {
    [metricName: string]: {
      value: number
      step?: number
      timestamp?: string
    }[]
  }
  
  // Artifacts (outputs)
  artifacts: {
    type: string                  // e.g., "model", "plot", "log"
    path: string
    metadata?: Record<string, unknown>
  }[]
  
  // Context
  tags: string[]
  notes?: string
}
```

--------------------------------

### ModelRegistry Interface

Source: https://github.com/devinschumacher/cloud-gpu-servers-services-providers/blob/main/_autodocs/types.md

Defines the structure for a model registry, including model identification, versioning details, artifacts, metadata, and serving configurations. Use this to manage the lifecycle of machine learning models.

```typescript
interface ModelRegistry {
  // Identity
  modelId: string
  modelName: string
  description: string
  
  // Versioning
  versions: {
    version: string               // e.g., "1.0.0", "2.1.3"
    createdAt: string
    artifacts: {
      modelFile: string           // Path/URL to model
      dependencies: string[]      // Python packages, etc.
      framework: string           // e.g., "pytorch", "tensorflow"
      formatType: string          // e.g., "ONNX", "SavedModel"
    }
    metadata: {
      author: string
      trainingDataset?: string
      metrics?: Record<string, number>
    }
  }[]
  
  // Staging
  stages: ("dev" | "staging" | "production" | "archived")[]
  currentStage: "dev" | "staging" | "production" | "archived"
  
  // Serving Configuration
  servingConfig: {
    framework: string
    containerImage?: string
    resources: {
      memory: string
      cpu: string
      gpu?: number
    }
  }
}
```

--------------------------------

### Committed Production GPU Pricing

Source: https://github.com/devinschumacher/cloud-gpu-servers-services-providers/blob/main/_autodocs/configuration.md

For committed production workloads, Atlantic.net with a 36-month term offers significant savings through a 35% long-term commitment discount. This strategy is suitable for production environments requiring stable, long-term GPU access.

```text
Recommendation: Atlantic.net with 36-month term
Strategy: Long-term commitment discount (35%)
Expected Cost: L40S ~$1.02/hour, H100 ~$2.56/hour
Savings: 35% vs on-demand
Payback: 21.6 months minimum commitment
```

--------------------------------

### MLOpsPlatform Interface

Source: https://github.com/devinschumacher/cloud-gpu-servers-services-providers/blob/main/_autodocs/types.md

Defines the structure for an MLOps platform, including its identity, architecture, core capabilities, framework support, integration points, pricing, and deployment modes. Use this to represent and compare different MLOps solutions.

```typescript
interface MLOpsPlatform {
  // Identity
  name: string                    // e.g., "MLflow", "ClearML"
  website: string
  category: "open-source" | "commercial" | "freemium"
  
  // Architecture
  architecture: {
    distributed: boolean
    selfHosted: boolean
    cloudManaged: boolean
    kubernetesNative: boolean
  }
  
  // Core Capabilities
  capabilities: {
    experimentTracking: boolean
    modelRegistry: boolean
    hyperparameterTuning: boolean
    workflowOrchestration: boolean
    modelServing: boolean
    featureStore: boolean
    datasetManagement: boolean
    monitoring: boolean
  }
  
  // Framework Support
  frameworks: {
    tensorflow: boolean
    pytorch: boolean
    scikitlearn: boolean
    xgboost: boolean
    onnx: boolean
  }
  
  // Integration Points
  integrations: {
    gitPlatforms: string[]        // e.g., ["GitHub", "GitLab"]
    cloudProviders: string[]      // e.g., ["AWS", "GCP", "Azure"]
    dataStores: string[]          // e.g., ["S3", "GCS", "ADLS"]
    monitoring: string[]          // e.g., ["Prometheus", "Datadog"]
  }
  
  // Pricing
  pricing: PricingModel
  
  // Deployment
  deployment: {
    modes: string[]               // e.g., ["local", "cloud", "enterprise"]
    containerization: boolean
    scalability: "limited" | "moderate" | "unlimited"
  }
}
```

--------------------------------

### GPU Specification Interface

Source: https://github.com/devinschumacher/cloud-gpu-servers-services-providers/blob/main/_autodocs/types.md

Defines the technical specifications of a GPU accelerator, including core compute, memory, interconnect, and power details. Used for GPU provider specifications and comparison matrices.

```typescript
interface GPUSpecification {
  // Identification
  name: string                    // e.g., "NVIDIA L40S", "NVIDIA H100 NVL"
  architecture: string            // e.g., "Ada Lovelace", "Hopper"
  
  // Core Compute
  cudaCores: number               // Total CUDA cores
  tensorCores: number             // Specialized tensor cores
  rtCores?: number                // Ray-tracing cores (optional)
  
  // Memory Subsystem
  memory: {
    capacity: string              // e.g., "48GB", "94GB"
    type: string                  // e.g., "GDDR6", "HBM3"
    bandwidth: string             // e.g., "864 GB/s", "3.9 TB/s"
    ecc?: boolean                 // Error correction support
  }
  
  // Interconnect
  systemInterface: {
    type: string                  // e.g., "PCIe 4.0 x16", "PCIe 5.0"
    bandwidth: string             // e.g., "64 GB/s", "128 GB/s"
    biDirectional: boolean
  }
  
  // Optional Features
  nvLinkBandwidth?: string        // e.g., "900 GB/s" (for multi-GPU)
  transformerEngine?: boolean     // LLM-specific optimization
  precision: string[]             // e.g., ["FP8", "FP16", "FP32", "FP64"]
  
  // Power & Thermal
  thermalOptimization: boolean
  powerEnvelope?: string
}
```