What is Function as a Service (FaaS) and how does it work?

FaaS.fast enables running code without managing servers. Write functions, deploy them, and they execute automatically in response to events. The platform handles scaling, availability, and infrastructure. You pay only for actual execution time, not idle servers. Perfect for APIs, data processing, webhooks, scheduled tasks, and event-driven architectures.

What programming languages does FaaS.fast support?

FaaS.fast supports JavaScript/Node.js, Python, Go, Java, C#/.NET, Ruby, and custom runtimes through containers. Each language includes popular frameworks and libraries pre-installed. Functions deploy in seconds regardless of language, and the platform handles dependencies automatically. Switch languages per function based on use case requirements.

How does FaaS.fast handle scaling and performance?

FaaS.fast scales automatically from zero to thousands of concurrent executions. It uses intelligent cold start optimization to minimize latency, keeps frequently-used functions warm, and distributes traffic globally. Performance monitoring shows execution times, error rates, and concurrency metrics. Handle traffic spikes effortlessly without capacity planning.

What event sources can trigger FaaS.fast functions?

FaaS.fast functions respond to HTTP requests, scheduled cron jobs, message queues (SQS, RabbitMQ), database changes, file uploads, webhooks from external services, and custom events. Multiple triggers can invoke the same function. Event-driven architecture enables building complex workflows from simple, focused functions.

How much does FaaS.fast cost compared to traditional servers?

FaaS.fast is typically 70% cheaper than traditional servers for variable workloads. You pay only for execution time in milliseconds, not for idle capacity. No charges when functions aren't running. Generous free tier includes millions of requests monthly. Cost scales linearly with usage, making it ideal for both small projects and enterprise workloads.

FaaS.fast - Function as a Service Platform

The Problem We're Solving

Functions deserve their own execution model

❌ The Old Way (Monoliths & Containers)

• Bundle entire application for deployment-change one function, redeploy 50MB container
• Cold starts measured in seconds-containers boot slowly, users wait
• Functions share state-memory leaks in one function crash entire application
• Manual connection pooling-manage database connections, HTTP clients, cache connections yourself
• Deployment complexity-build Docker images, manage registries, orchestrate rollouts

✅ The FaaS.fast Way (Pure Functions)

• Deploy individual functions-5KB deploys in 100ms, independent versioning per function
• Sub-50ms cold starts-optimized runtimes boot instantly, warm instances reused intelligently
• Complete isolation-every invocation in separate execution context, no shared state bugs
• Automatic connection management-platform maintains connection pools, reuses across invocations
• Zero deployment complexity-push code, platform handles optimization, distribution, scaling

How It Works

Function lifecycle and execution model

Pure Function Execution

Functions accept input, return output-no side effects, no shared state. Each invocation runs in isolated execution context. Memory limits enforced per function (128MB-10GB). CPU allocated proportionally. Timeouts prevent runaway processes. Stateless by design-state stored externally.

Cold Start Optimization

First invocation "cold starts"-runtime boots in under 50ms. Warm containers reused for subsequent calls. Provisioned concurrency keeps functions warm 24/7. Predictive scaling warms instances before traffic arrives. Language choice matters-Go/Rust boot faster than Java/Python.

Automatic Concurrency

Platform spawns new function instances automatically. Handle 1 or 10,000 concurrent requests-same code. Per-function concurrency limits prevent overload. Instance pooling reuses warm containers. Geographic distribution for low latency. Scales to zero when idle-pay nothing.

Function-Level Superpowers

Execution model designed for pure functions

Sub-50ms Cold Starts

Optimized runtime initialization boots functions in under 50ms. Warm container reuse for subsequent invocations. Provisioned concurrency keeps critical functions warm 24/7. Predictive warming based on traffic patterns. Language choice matters-Go boots in 10ms, Python in 100ms.

Millisecond Billing Granularity

Pay per millisecond of actual execution time, not idle capacity. First million requests free monthly. Typical function costs $0.0000002 per invocation. Memory allocated (128MB-10GB) affects pricing linearly. Test environments cost pennies. Unpredictable workloads become economical.

Automatic Connection Pooling

Platform manages database connections, HTTP clients, cache connections automatically. Connections initialized outside handler reused across invocations. Context object provides request-scoped data. Environment variables injected securely. Connection limits prevent exhaustion. No manual pool management.

Execution Isolation

Every invocation runs in isolated execution context. Memory limits enforced per function. CPU throttling prevents resource monopolization. Crashes isolated to single invocation-other requests unaffected. Security boundaries at function level. Process isolation prevents cross-contamination.

Stateless by Design

Functions don't persist state between invocations. Encourages proper architecture-state in databases, caches, object storage. Enables true horizontal scaling. Instance pooling works seamlessly. Request can be served by any instance. Simplifies reasoning about distributed systems.

Runtime Layers

Shared dependencies deployed as layers-reused across functions. Common libraries (AWS SDK, database drivers) included. Custom layers for your frameworks. Reduces deployment size from 50MB to 5KB. Faster deploys, faster cold starts. Version layers independently from function code.

Language & Runtime Support

Write functions in your favorite language

Node.js

Python

Go

Rust

Java

.NET/C#

Ruby

PHP

Why Teams Choose FaaS.fast

Function-first development advantages

Independent Function Deployment

Deploy individual functions independently-no monolith redeploys. Change authentication function without touching payment logic. Version functions separately. Blue-green deployments at function level. Rollback specific functions while others run. Team ownership per function. Ship faster with less coordination.

Granular Resource Allocation

Configure memory/CPU per function. Image processing gets 3GB RAM, API routes get 256MB. Optimize costs at function level-pay for exactly what each function needs. Critical paths get provisioned concurrency, batch jobs scale to zero. Resource allocation matches actual requirements, not guesses.

Fault Isolation

Bug in one function doesn't crash entire app. Memory leak isolated to single function. Failed invocations don't affect other requests. Automatic retries for transient failures. Dead letter queues capture failed events. Circuit breakers prevent cascade failures. Resilience by architecture.

Polyglot Runtime Support

Write each function in optimal language. Python for ML models, Go for APIs, Rust for performance-critical paths, Node.js for rapid prototyping. Mix languages in same application. Choose runtime per function. No forcing entire codebase into single language/framework. Best tool for each job.

Real-World Serverless

How teams build with FaaS.fast

Image Processing at Imgur Scale

Photo sharing platform needed to resize, optimize, and watermark millions of uploaded images daily. FaaS.fast functions triggered on S3 uploads, processed images in parallel. Scaled from 1K to 500K images/hour during viral posts automatically. Cost: $0.02 per 1000 images versus $5K/month for dedicated servers. Zero infrastructure management. Processing time under 200ms per image.

Chatbot Backend for Enterprise

Customer service chatbot needed to handle unpredictable traffic patterns-quiet at night, slammed during business hours. FaaS.fast functions processed NLP queries, integrated with CRM systems, generated responses. Scaled from 0 to 50K concurrent conversations during product launches. Cost dropped 92% versus always-on Kubernetes cluster. Development team of 3 instead of planned 10-no DevOps engineers needed.

Email Campaign System for SaaS

Marketing platform sent millions of personalized emails for thousands of customers. FaaS.fast functions generated custom content, applied templates, tracked opens and clicks. Processed 10M emails/day across 200+ edge locations for optimal deliverability. Pay-per-execution model meant customers only paid for emails actually sent. Zero cold email blocks thanks to distributed IPs. Campaign deployment: 30 seconds, not 3 days.

Payment Processing for FinTech

Payment gateway processed transactions for thousands of merchants with strict latency requirements. FaaS.fast functions validated transactions, applied fraud detection ML models, routed to payment processors. Sub-50ms response times globally thanks to edge deployment. PCI compliance built-in-no infrastructure to certify. Handled Black Friday surge (20x normal traffic) without single timeout. Automatic retry logic ensured zero failed transactions.

IoT Data Pipeline for Smart Cities

Smart city platform ingested sensor data from 50K devices-traffic cameras, air quality monitors, parking meters. FaaS.fast functions processed telemetry streams, aggregated metrics, triggered alerts. Handled 10M events/minute during peak hours, scaled to near-zero overnight. Event-driven architecture meant functions only ran when data arrived. Infrastructure cost: $200/month for millions of daily events. Traditional approach would've required $15K/month in servers.

Webhook Handler for Integration Platform

iPaaS platform received webhooks from 500+ external services, transformed data, routed to customer workflows. FaaS.fast functions parsed webhooks, validated signatures, applied transformations, triggered downstream actions. Elastic scaling handled traffic spikes when integrated services had incidents. 99.99% uptime despite zero infrastructure management. Added 50 new integrations in 6 months-previous architecture would've taken 18 months due to ops complexity.

Function Optimization Best Practices

Build high-performance, cost-efficient functions

Minimize Cold Start Latency

Keep deployment packages under 10MB. Lazy-load dependencies only when needed. Initialize connections outside handler-reused across invocations. Use lightweight languages (Go, Rust) for critical paths. Provisioned concurrency for latency-sensitive functions. Warmup pings keep functions hot.

Optimize Memory Allocation

Memory directly correlates with CPU allocation. Higher memory = faster execution. Test functions at different memory tiers-sometimes 1GB at $X costs less than 512MB at 2X runtime. Use profiling to find optimal memory size. Right-size per function-don't default to 128MB for everything.

Leverage Execution Context

Container reused across invocations-initialize expensive resources once. Database connections, HTTP clients, S3 clients initialized outside handler persist. Check if connection exists before recreating. /tmp directory persists between invocations (512MB limit). Use context for request-scoped data.

Design for Idempotency

Functions may execute multiple times for single event due to retries. Use unique request IDs to detect duplicates. Make operations idempotent-safe to repeat. Check if work already done before proceeding. Prevents double-charging, duplicate records, inconsistent state. Defensive coding prevents distributed systems bugs.

Control Concurrency Limits

Set per-function concurrency limits to prevent overwhelming downstream systems. Database with 100 connection limit? Set function concurrency to 80. Protects backends from stampedes. Reserved concurrency guarantees availability. Throttled invocations retry automatically. Balance between parallelism and backend protection.

Monitor Function Metrics

Track invocation count, duration, errors, throttles per function. Cold start frequency reveals optimization opportunities. Memory usage shows over/under-provisioning. Set alarms on error rates, duration anomalies. Distributed tracing shows function chains. Cost explorer breaks down spend per function. Observability enables optimization.

Go Serverless Today

From infrastructure headaches to pure business logic

FaaS.fast is part of the NextGen.fast ecosystem, bringing enterprise-grade serverless computing to developers worldwide. Write functions, not infrastructure. Scale infinitely, pay per millisecond.

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