December 18, 2022 5 min read

The Serverless Benefits Realized in 2022

Serverless Azure Functions Cloud Native Architecture Cost

Serverless computing has matured significantly. Let’s examine the real benefits organizations have achieved with serverless architectures in 2022.

The Serverless Value Proposition

The promise of serverless has largely been fulfilled:

Pay only for what you use
No server management
Automatic scaling
Faster time to market

Real Cost Savings

# Cost comparison: Traditional vs Serverless
cost_comparison = {
    "traditional_always_on": {
        "monthly_vm_cost": 500,  # D4s_v3 24/7
        "monthly_requests": 1_000_000,
        "cost_per_request": 500 / 1_000_000,  # $0.0005
        "utilization": "20%"  # Typical underutilization
    },
    "serverless": {
        "monthly_executions": 1_000_000,
        "execution_time_ms": 100,
        "memory_mb": 256,
        "cost_per_million": 0.20,  # Execution cost
        "cost_per_gb_second": 0.000016,
        "monthly_cost": calculate_serverless_cost(1_000_000, 100, 256),
        "utilization": "100%"
    }
}

def calculate_serverless_cost(executions: int, duration_ms: int, memory_mb: int) -> float:
    """Calculate Azure Functions consumption plan cost."""
    execution_cost = (executions / 1_000_000) * 0.20

    gb_seconds = (executions * duration_ms / 1000) * (memory_mb / 1024)
    memory_cost = gb_seconds * 0.000016

    # First 400,000 GB-seconds and 1M executions free
    return max(0, execution_cost + memory_cost - free_tier_value)

Operational Benefits

Reduced Management Overhead

# What you don't manage with serverless
eliminated_tasks:
  infrastructure:
    - Server provisioning
    - OS patching
    - Security updates
    - Capacity planning
    - Load balancer configuration

  operations:
    - Server monitoring
    - Disk space management
    - Memory optimization
    - Process management
    - Restart handling

  scaling:
    - Manual scaling decisions
    - Auto-scaling configuration
    - Peak capacity planning
    - Load testing for capacity

# What you still manage
remaining_tasks:
  - Application code
  - Business logic
  - Integration testing
  - Monitoring and alerting
  - Cost optimization

Automatic Scaling

// Azure Functions scales automatically based on demand
// This code handles 10 requests or 10,000 requests without changes

[Function("ProcessOrder")]
public async Task<IActionResult> ProcessOrder(
    [HttpTrigger(AuthorizationLevel.Function, "post")] HttpRequest req)
{
    var order = await req.ReadFromJsonAsync<Order>();

    // Each request gets its own isolated execution
    await _orderService.ProcessAsync(order);

    return new OkResult();
}

// Event-driven scaling with queue triggers
[Function("ProcessQueueMessage")]
public async Task ProcessQueueMessage(
    [QueueTrigger("orders")] string message)
{
    // Automatically scales based on queue depth
    // More messages = more parallel executions
    await ProcessMessageAsync(message);
}

Development Velocity

Faster Time to Production

# Traditional deployment pipeline: Days to weeks
traditional_steps = [
    "Request VM provisioning",      # 1-3 days
    "Wait for security approval",   # 2-5 days
    "Configure networking",         # 1-2 days
    "Set up load balancer",         # 1 day
    "Install runtime/dependencies", # 1 day
    "Deploy application",           # 1 day
    "Configure monitoring",         # 1 day
    "Load testing",                 # 2-3 days
]
# Total: 10-17 days

# Serverless deployment pipeline: Hours
serverless_steps = [
    "Write function code",          # Hours
    "Push to repo",                 # Minutes
    "CI/CD deploys automatically",  # Minutes
    "Test in production-like env",  # Hours
    "Promote to production",        # Minutes
]
# Total: Hours to 1 day

Focus on Business Logic

// Serverless lets you focus on what matters
// No boilerplate for server setup, HTTP handling, scaling

// This is all you need for a working API endpoint:
[Function("GetCustomer")]
public async Task<IActionResult> GetCustomer(
    [HttpTrigger(AuthorizationLevel.Function, "get", Route = "customers/{id}")]
    HttpRequest req,
    string id)
{
    var customer = await _customerService.GetByIdAsync(id);

    return customer is not null
        ? new OkObjectResult(customer)
        : new NotFoundResult();
}

Event-Driven Architecture Enablement

// Serverless excels at event-driven patterns
public class EventDrivenFunctions
{
    // React to blob uploads
    [Function("ProcessUpload")]
    public async Task ProcessUpload(
        [BlobTrigger("uploads/{name}")] Stream blob,
        string name)
    {
        await ProcessFileAsync(blob, name);
    }

    // React to database changes
    [Function("OnCustomerChange")]
    public async Task OnCustomerChange(
        [CosmosDBTrigger("database", "customers",
            Connection = "CosmosConnection",
            LeaseContainerName = "leases")]
        IReadOnlyList<Customer> changes)
    {
        foreach (var customer in changes)
        {
            await SyncToSearchIndexAsync(customer);
        }
    }

    // React to messages
    [Function("ProcessEvent")]
    public async Task ProcessEvent(
        [ServiceBusTrigger("events")] EventMessage message)
    {
        await HandleEventAsync(message);
    }

    // Scheduled tasks
    [Function("DailyReport")]
    public async Task DailyReport(
        [TimerTrigger("0 0 8 * * *")] TimerInfo timer)
    {
        await GenerateAndSendReportAsync();
    }
}

When Serverless Fits

serverless_fit_matrix = {
    "excellent_fit": [
        "Event-driven processing",
        "APIs with variable traffic",
        "Scheduled tasks / cron jobs",
        "Webhooks and integrations",
        "File processing",
        "IoT data ingestion",
        "Chatbots and notifications"
    ],
    "good_fit": [
        "Microservices",
        "Background jobs",
        "Data transformation",
        "Authentication services"
    ],
    "consider_alternatives": [
        "Long-running processes (>15 min)",
        "Stateful applications",
        "High-frequency trading",
        "Constant high-load applications",
        "Applications requiring GPUs"
    ]
}

Lessons Learned in 2022

serverless_lessons = {
    "cold_starts_manageable": """
        Cold starts improved significantly in 2022.
        Premium plans, provisioned concurrency, and
        better runtime optimizations make cold starts
        acceptable for most use cases.
    """,

    "observability_critical": """
        Distributed tracing and proper logging are
        essential. Without them, debugging serverless
        applications is very difficult.
    """,

    "cost_can_surprise": """
        At very high scale, serverless can become
        expensive. Model costs carefully and consider
        premium plans or containers for predictable
        high-volume workloads.
    """,

    "testing_requires_investment": """
        Local development and testing need proper
        tooling. Azure Functions Core Tools and
        Azurite make this manageable.
    """,

    "vendor_lock_in_real": """
        Serverless ties you to a platform. This is
        often acceptable but should be a conscious
        decision.
    """
}

The Hybrid Approach

Most organizations use serverless alongside other patterns:

hybrid_architecture:
  serverless:
    - API endpoints (Azure Functions)
    - Event processing
    - Scheduled jobs
    - Integration webhooks

  containers:
    - Long-running services (Container Apps)
    - Complex dependencies
    - Stateful workloads

  managed_services:
    - Databases (Cosmos DB, SQL)
    - Messaging (Service Bus)
    - Storage (Blob, Queue)

  virtual_machines:
    - Legacy applications
    - Specialized workloads
    - Compliance requirements

Conclusion

Serverless delivered on its promises in 2022. For the right workloads, it provides significant cost savings, operational simplicity, and development velocity. The key is understanding where it fits and where alternatives are better.