How Serverless Architecture Works (Benefits & Use Cases)
David Abramson··23 min read
⚡Key Takeaways
- Serverless architecture connects requests and events to cloud-managed functions, containers, and services. The provider manages underlying infrastructure and scales compute, while persistent application data lives in databases or other external storage.
- Serverless can reduce infrastructure maintenance, accelerate development, and lower spending on idle compute. Usage-based pricing particularly suits intermittent workloads, but sustained high utilization may be more economical on provisioned infrastructure.
- Variable-traffic APIs, webhooks, scheduled jobs, and background processing are strong serverless candidates. Workloads with demanding latency, state, or compute requirements need closer evaluation, making a hybrid architecture a practical choice for many applications.
- Qrvey delivers an embedded analytics layer within serverless SaaS applications. Built on a cloud-native Kubernetes architecture and deployed in your cloud, it’s equipped with embedded dashboards, self-service, governed AI, and workflow automation, while reducing the engineering work required to build analytics in-house.
Traditional applications often begin with a question like, “How many servers do we need?” Serverless architecture starts somewhere else: what event happened, and what work needs to run because of it?
HTTP requests, file uploads, database changes, messages, and scheduled events can trigger small pieces of compute that call managed services, pass work through queues, and scale independently as demand changes.
This guide explains the serverless request lifecycle, the technologies that make it work, its main benefits and use cases, and where the model can become more complex.
How Serverless Architecture Works
Serverless architecture works by connecting incoming requests and events to cloud-managed compute and services. Developers define the application logic and configure its triggers; the provider manages the underlying servers. Applications commonly use loosely coupled services that communicate through events, with each component handling a specific responsibility.
1. A Request or Event Triggers the Work
The process begins when something happens: an HTTP request arrives, a file uploads, a queue receives a message, a database record changes, or a timer fires.
An API gateway can route incoming HTTP requests, while event routers and service integrations connect other triggers to the appropriate compute. For example, a customer submitting a registration form can trigger account creation.
2. The Cloud Platform Starts the Required Compute
The platform directs the work to an available execution environment or starts another when needed. It does not necessarily create a fresh instance for every request.
That compute might be a function or a managed serverless container. In a function-based workflow, each function typically performs a bounded task, such as validating registration details and creating an account record.
3. The Function Calls Managed Services or Other Components
The function reads or writes data, accesses object storage, or calls another service. For registration, it might save the customer’s profile in a managed database.
Functions are generally designed to be stateless: information needed across executions belongs in persistent services. A customer’s account must remain available even after the compute instance that created it shuts down.
4. Events Can Trigger Additional Asynchronous Work
Account creation can publish an event or place a message in a queue for follow-up work. Separate workers can then send welcome emails or configure the customer’s workspace.
The initial request can receive confirmation without waiting for every background task to finish. Queues buffer pending work, while event buses route events to interested services. This lets components process work independently and at different speeds.
5. Resources Scale With Demand
As requests or queued events increase, the platform can add compute capacity within configured limits. When activity falls, it can reduce capacity.
The details depend on the service and hosting plan. Scale-to-zero behavior, concurrent requests per instance, execution limits, and billing models vary, so teams must check how their chosen platform handles the workload.
Key Components of a Serverless Architecture
A serverless application typically combines five core components. Each handles a different responsibility, from executing code to storing data or coordinating background work. Your architecture determines which services you need and how they connect.
- Serverless compute: Functions or managed serverless containers execute application logic without your team provisioning servers. AWS Lambda and Azure Functions support function-based execution, while Google Cloud Run supports managed container workloads. You supply the code or container image and configure its execution requirements; the provider manages the underlying compute infrastructure.
- Triggers and event routing: HTTP requests, queue messages, database changes, storage events, and timers initiate work. Event-routing services such as Amazon EventBridge and Azure Event Grid direct events to interested components. Routing rules determine which service should respond, so a file upload and a payment notification can start different workflows.
- API and request management: API gateways expose application capabilities through controlled endpoints. They route requests, support authentication and authorization, and apply throttling policies. For example, Amazon API Gateway can connect an application’s HTTP endpoint to a Lambda function while providing centralized access controls and request monitoring.
- Managed data and storage services: Databases and object storage retain data outside short-lived compute, while managed caches accelerate repeated reads. Services such as Amazon DynamoDB and Amazon S3 hold application records and files independently of function execution. These stores can also supply datasets to a cloud-based analytics layer.
- Messaging and workflow orchestration: Queues and messaging services, including Amazon SQS, Azure Service Bus, and Google Cloud Pub/Sub, let components exchange work asynchronously. Orchestration services coordinate processes with dependencies, branching, retries, and multiple steps. The distinction matters: delivering a message and tracking an entire workflow are different responsibilities.
Benefits of Serverless Architecture
Serverless can give your team more time for application development, help services absorb changing demand, and reduce spending on idle compute. The benefits are strongest when the workload suits the platform’s execution and pricing model.
1. Less Infrastructure to Provision and Maintain
The cloud provider handles the underlying servers, including hardware maintenance and host operating-system management. Your team spends less time preparing machines, patching hosts, and maintaining spare capacity for application workloads. That can free engineering time for product features and reliability improvements closer to the customer experience.
2. Automatic Scaling for Variable Demand
Depending on the service, growing event volume or request concurrency can trigger additional compute instances without developers manually provisioning capacity. A SaaS application receiving a surge of customer imports, for example, can increase processing capacity as demand rises and reduce it afterward.
This reduces the need to reserve peak capacity throughout quieter periods. Configure scaling and concurrency limits around what downstream databases and external APIs can handle, since those dependencies may reach their limits before the compute layer does.
3. Usage-Based Cost Efficiency
Pay-per-use pricing can work well for intermittent or bursty workloads. A function that processes occasional uploads or runs a scheduled task may avoid much of the compute expense associated with keeping a dedicated server available all day. Actual charges depend on the service, execution time, and resources consumed.
However, continuously busy workloads aren’t automatically cheaper on serverless infrastructure. When evaluating serverless platform costs, compare the full workload cost with provisioned compute, including storage, requests, data transfer, and supporting services.
4. Faster Development and Smaller Deployable Services
Functions and managed services let developers implement a specific capability without building every supporting system themselves. A team adding document validation can use existing storage, messaging, and compute services, then focus its code on the validation rules.
When components have clear boundaries, developers can update and deploy them independently. Changing that validation logic doesn’t have to require redeploying unrelated account or billing services. Smaller changes can shorten release cycles and make testing more focused, provided teams also test the integrations between services.
5. Easier Event-Driven and Asynchronous Workflows
Managed queues, event buses, and orchestration services simplify workflows that involve several independent tasks. An uploaded document could trigger validation, text extraction, and a completion notification without keeping the original HTTP request open throughout processing.
This separation lets each component run at an appropriate pace. A slow notification service doesn’t need to delay document acceptance, and queued work can wait for available processing capacity. Your team still defines retry behavior and failure handling, but managed services provide much of the coordination infrastructure.
Common Serverless Architecture Patterns
Serverless applications often combine several patterns. An API may accept a request, a queue may hold the resulting work, and an event may trigger follow-up services. Each pattern addresses a different part of that process.
1. API-Driven Applications
An API-driven application exposes functionality through HTTP endpoints backed by serverless compute. For example, a customer submits a shipping-quote request through an application.
Amazon API Gateway routes it to a function, which validates the details, retrieves shipping rates, and returns a response. Different endpoints can connect to separate functions, allowing quote calculation and shipment tracking to evolve independently.
2. Event-Driven Processing
This pattern reacts to a change in application state, with interested services responding to a published event. For example, a successful subscription payment could trigger:
- Account updates: Activate the customer’s purchased plan.
- Customer communication: Send a payment receipt.
- Financial records: Update the accounting system.
These consumers can process the event independently, allowing another service to subscribe later without redesigning the payment component.
3. Scheduled Functions
Scheduled functions run at specified times or intervals. A SaaS company could use Amazon EventBridge Scheduler to invoke a nightly billing-reconciliation function. The function compares payment records with invoices and flags discrepancies for review.
This pattern suits work tied to a business schedule, such as daily synchronization or monthly reporting, with execution limits guiding how much work each invocation handles.
4. Serverless Data Processing
Serverless compute can prepare incoming data for downstream systems. When a customer uploads a CSV file to object storage, a storage event can initiate processing:
- Validate records: Check required fields and reject malformed entries.
- Transform values: Standardize dates, currencies, or identifiers.
- Store results: Write cleaned records to the destination database.
This can support automated data analytics by preparing source data before analysis begins.
5. Microservices
Microservices organize an application around independently deployable business capabilities. A commerce platform might run pricing, inventory, and customer accounts as separate services, each with its own release cycle and scaling requirements.
Serverless functions or managed serverless containers can host those services.
- Microservices and serverless aren’t synonyms: microservices describe how the application is organized; serverless is one approach to running its components.
6. Asynchronous Workflows
Asynchronous workflows separate accepting work from completing it. An order-processing flow could include:
- Order received: Accept the request and place work in a queue.
- Inventory update: A worker processes the queued order and reserves stock.
- Notification: A subsequent event triggers confirmation to the customer.
The customer doesn’t need to keep the original request open while processing finishes. Queues buffer work, while orchestration can manage dependencies and retries.
The example below combines API-driven, queued, and event-driven processing:
| flowchart TD A[“Customer Request”] –> B[“API Gateway”] B –> C[“Order Function”] C –> D[“Request Accepted Response”] C –> E[“Order Queue”] E –> F[“Inventory Worker”] F –>|”Stock reserved”| G[“Event Bus”] G –> H[“Customer Notification”] G –> I[“Fulfillment Service”] |
Where Serverless Architecture Fits in SaaS Applications
In SaaS products, serverless often supports individual application capabilities and the background work surrounding them. It can help your team handle uneven customer activity while keeping requests, integrations, and routine operations manageable.
1. Tenant-Aware APIs and Application Services
Serverless APIs can handle focused tasks such as updating a project’s status, retrieving subscription settings, or saving a customer’s preferences. These endpoints can use on-demand compute without your team maintaining dedicated application servers for them.
Scaling execution doesn’t establish tenant isolation. Your application still needs to authenticate users, determine their tenant, authorize each action, and restrict access to that tenant’s data.
Tenant context should come from a verified identity, with those restrictions enforced wherever the request accesses resources.
These boundaries also extend into customer-facing analytics. Qrvey supports tenant-specific analytics experiences, with data access governed by tenant and user permissions. Each customer can work with its own dashboards and custom fields within a shared analytics platform.
Qrvey’s tenant-aware analytics model, illustrated with separate dashboards and custom fields for Tenants A, B, and C.
2. Background Jobs and Asynchronous Processing
Work that doesn’t need an immediate result can run in the background while customers continue using the application:
- Document processing: Extract information from uploaded contracts or generate customer documents.
- Imports and exports: Validate incoming records or prepare downloads from an embedded analytics experience.
- Email generation: Prepare and send account notifications or scheduled digests.
Give users a job status or completion notification. For tasks that exceed a function’s execution limits, use an appropriate managed job runtime or divide the work into smaller units.
3. Webhooks and Third-Party Integrations
Serverless endpoints can receive updates from CRM, billing, payment, support, and partner platforms. For example, a payment provider’s failed-renewal webhook could trigger an account-status update and a customer notification.
The integration should verify the sender using the provider’s supported authentication mechanism, such as webhook signatures, and map the external account to the correct tenant. Longer processing can move to a queue after the event is securely accepted, helping the endpoint respond promptly.
4. Scheduled SaaS Operations
Recurring operations often need to run independently of customer activity. Scheduled functions or jobs can reconcile invoices, synchronize customer records, aggregate usage for billing, or perform maintenance such as removing expired temporary files.
For a usage-based SaaS product, a nightly job might calculate each tenant’s consumption for the billing period. Define reporting windows and time zones explicitly, and track completed work so retries or overlapping runs don’t count the same usage twice. Monitor missed runs as well as failed ones.
5. Event-Driven Onboarding and Notifications
Once a new customer’s account record exists, a customer-created event can initiate several onboarding activities:
- Account configuration: Apply the selected plan and default settings.
- Welcome messaging: Send setup instructions to the account administrator.
- CRM synchronization: Create or update the customer’s commercial record.
- Provisioning: Prepare the resources required for the customer’s workspace.
Independent tasks can run separately, while dependent steps need coordination. Track onboarding progress so your application only marks the workspace ready when essential setup is complete; a delayed CRM update shouldn’t necessarily prevent the customer from getting started.
Serverless Architecture for Data and Analytics
Serverless services help move data from application events into systems where it can be stored, analyzed, and presented to users. Functions can validate records, call APIs, and coordinate processing, while specialized data services handle heavier transformations and analytical workloads.
A typical flow is: Application/Event Sources → Serverless Compute and Messaging → Data Store/Warehouse → Analytics Layer → End User
1. Event-Driven Data Ingestion
An uploaded file, database change, or application event can initiate ingestion. For example, when a customer uploads transaction records to object storage, a function can check the file’s structure and route valid data into a processing queue. This starts the pipeline when new data arrives, without waiting for a scheduled scan.
2. Data Transformation and Enrichment
Before data reaches its destination, functions can perform focused preparation tasks:
- Validate records: Check required fields and flag incomplete entries.
- Standardize values: Normalize dates, identifiers, and measurement units.
- Enrich data: Retrieve related account or product information through an API.
For large joins or complex transformations, use a dedicated data-processing service. Functions can initiate and coordinate that work while the appropriate processing engine executes it.
3. Real-Time and Streaming Processing
Serverless functions can consume batches of records from a managed event stream. A logistics application, for example, could process shipment-status updates and write the latest status to a data store.
However, event-driven ingestion alone doesn’t guarantee an instantly updated dashboard. Batching, processing time, storage availability, and analytics refresh behavior all affect how quickly users see changes. AWS Lambda’s Kinesis integration, for example, includes configurable batching behavior.
4. Scheduled Analytics and Batch Workflows
Some reporting depends on a complete period of data. A nightly workflow might collect daily transactions, launch an aggregation job, and make the completed results available for reporting.
An orchestration service can coordinate dependencies, retries, and completion checks. This helps ensure downstream reporting starts after the required processing succeeds, while substantial computations run on infrastructure suited to their demands.
5. Connecting Processed Data to an Analytics Layer
Once processed data is available, customers need a secure way to explore it, create reports, and act on the results within your application.
Qrvey provides the customer-facing analytics layer for multi-tenant SaaS products. It combines dashboards, reports, self-service analytics, governed AI, and workflow automation, with JavaScript embedding to bring these experiences into your product.
This reduces the engineering work required to build and maintain those capabilities in-house.
Built on a cloud-native Kubernetes architecture, Qrvey runs in your cloud environment and integrates with your existing services, security, and serverless infrastructure.
Your team can retain its serverless application architecture while using Qrvey to deliver analytics to customers.
When Should You Use Serverless Architecture?
Use serverless when a workload benefits from on-demand execution, managed scaling, and less infrastructure maintenance. Start by assessing its traffic, execution time, state, and response requirements. The following comparison helps identify promising candidates and workloads that need closer evaluation.
| Workload characteristic | Good fit | Weaker fit or closer evaluation |
|---|---|---|
| Traffic and utilization | Bursty or unpredictable demand, with quieter periods when compute capacity can decrease. | Constant high utilization, where provisioned capacity may offer better economics. |
| Event-driven workloads | Independent tasks triggered by uploads, database changes, or business events. | Tightly coupled processing that requires frequent coordination or transactions across several services. |
| APIs and response times | APIs with variable usage and response targets the selected platform can consistently meet. | Strict ultra-low-latency requirements that leave little tolerance for startup delays or additional network calls. |
| Scheduled jobs | Periodic synchronization, billing reconciliation, usage aggregation, or maintenance tasks. | Continuous processing with few idle periods, reducing the benefit of scaling down between executions. |
| Queued and background processing | Document generation, notifications, and other bounded tasks that can run asynchronously. | Long-running, stateful processing that needs durable orchestration, checkpoints, or a different execution model. |
| Webhooks and integrations | Receiving external events, validating them, and queuing follow-up work. | Workflows with many synchronous service calls, where communication overhead dominates execution time. |
| File and data processing | Validating files, transforming records, and initiating downstream processing when data arrives. | Workloads requiring specialized compute, extensive infrastructure control, or resources beyond the selected runtime’s limits. |
These distinctions depend on the service and hosting plan. Long-running workflows may suit durable orchestration, and serverless containers can support workloads that exceed a short-lived function’s practical scope. Always-ready instances can reduce startup latency, although keeping capacity available changes the cost calculation.
A hybrid architecture often provides the most practical fit. A SaaS product could use serverless functions to receive webhooks, validate uploads, and send notifications while running persistent application services or containerized components for sustained processing.
For example, serverless functions could prepare incoming customer records while a separate customer-facing analytics layer handles interactive dashboards and reporting.
Each component can use infrastructure suited to its responsibilities, with clear interfaces connecting the workflow.
Before committing, test representative traffic and failure scenarios. Measure response times, processing backlogs, downstream capacity, recovery behavior, and total operating cost. Event-driven designs also need deliberate handling of retries, duplicate events, and consistency between services.
Challenges and Limitations of Serverless Architecture
While serverless architecture offers many benefits for B2B SaaS products, there are a few considerations to keep in mind. These include:
- Vendor lock-in: Serverless architecture can lead to vendor lock-in, as companies become reliant on a specific cloud provider’s services. To mitigate this risk, companies should choose a cloud provider that offers a wide range of serverless services. A commitment to open standards will help future considerations as well should you ever need to make a change.
- Cold starts: When idle compute scales to zero, the next request may experience startup latency. For latency-sensitive workloads, evaluate prewarmed or always-ready instances, accounting for their additional cost.
- Debugging serverless functions: Debugging serverless functions can be more challenging than debugging traditional server-based applications. Given the distribution across multiple servers, inspecting functions can pose a challenge. To mitigate this challenge, companies should use a cloud provider that offers good debugging tools and support.
- State and long-running workflow complexity: Short-lived functions generally keep persistent state in external services. Use dedicated orchestration to coordinate complex workflows and select an appropriate runtime for tasks that exceed execution limits.
- Cost can become harder to predict at scale: Sustained execution, data transfer, API calls, logging, and supporting services can accumulate charges. Monitor the complete workflow’s costs, set budget alerts, and compare provisioned compute for consistently busy workloads.
How Qrvey Uses Serverless Services
Overall, serverless architecture is a good choice for B2B SaaS products. It can help companies to reduce costs, improve scalability, and speed up development. Additionally, embedded services for SaaS companies such as analytics and reporting give SaaS providers an advantage over their competition by enhancing product value and customer experience.
When we built the first version of Qrvey, our embedded analytics product, we chose to lead with innovative technology. We selected AWS’s serverless offerings to create the infrastructure for our solution.
We wanted our infrastructure to take advantage of on-demand services for the lowest hosting costs possible.
Our solution has evolved again. While we still use serverless technology when our customers chose to host on AWS, our best embedded analytics software also uses container technology to deploy to Azure, GCP (coming soon), on-prem and others.
Ultimately, what matters most is empowering our SaaS customers to embed self-service analytics.
And doing so in a way that development teams can personalize to any person or use case.
Building with on-demand technology gives us the modern technology stack to make that possible.
FAQs
No. Serverless is one way of running applications in the cloud. Instead of provisioning and managing application servers directly, developers use managed services where the cloud provider handles much of the underlying compute infrastructure and scaling.
No. Servers still run the application, but developers do not provision or manage them directly. The cloud provider handles the underlying infrastructure while the development team focuses on application logic, configuration, security, and integrations.
Yes, but it is not always the most practical approach. Many applications use a hybrid architecture where serverless functions handle event-driven or variable workloads while persistent services, containers, databases, or other infrastructure handle workloads that benefit from a different execution model.
Supported languages depend on the platform and runtime. Major serverless services commonly support languages such as JavaScript or TypeScript, Python, Java, C#, and Go, while container-based serverless platforms can support a broader range of application runtimes.
Serverless applications typically rely on centralized logs, metrics, distributed tracing, alerts, and request or event identifiers to follow activity across multiple services. Good observability becomes especially important because one user action may pass through several functions, queues, APIs, and managed services before the workflow completes.
No. Qrvey can operate alongside a broader SaaS architecture rather than requiring the entire product to follow one infrastructure model. SaaS teams can continue using the mix of serverless services, containers, databases, and other components that fit their workloads while integrating Qrvey as the customer-facing analytics layer.
Yes. Serverless functions and data services can ingest, validate, transform, or prepare application data before it reaches the analytics layer. Qrvey can then provide dashboards, reporting, self-service analytics, governed AI, and workflow capabilities around that data inside the SaaS product.
Serverless services can help ingest, process, and move data, but they do not automatically provide the customer-facing analytics experience on top of that data. Qrvey adds dashboards, reporting, self-service analytics, governed AI, and workflow automation, reducing the amount of analytics infrastructure and product functionality your engineering team needs to build and maintain itself.
David is the Chief Technology Officer at Qrvey, the leading provider of embedded analytics software for B2B SaaS companies. With extensive experience in software development and a passion for innovation, David plays a pivotal role in helping companies successfully transition from traditional reporting features to highly customizable analytics experiences that delight SaaS end-users.
Drawing from his deep technical expertise and industry insights, David leads Qrvey’s engineering team in developing cutting-edge analytics solutions that empower product teams to seamlessly integrate robust data visualizations and interactive dashboards into their applications. His commitment to staying ahead of the curve ensures that Qrvey’s platform continuously evolves to meet the ever-changing needs of the SaaS industry.
David shares his wealth of knowledge and best practices on topics related to embedded analytics, data visualization, and the technical considerations involved in building data-driven SaaS products.