From LLM Prompt to Production (2/9) - Choosing the Technology

This is part of a series of blogs:

When moving from a simple LLM prompt to a production-ready API, choosing the right deployment strategy is crucial for scalability, cost-efficiency, and maintainability. Let’s explore the various options across programming languages, cloud platforms, and deployment methods.

Programming Language Options

As of 2025, three languages dominate all the projects across the globe. Let us look at the pros and cons, and choose the right one for our project.

TypeScript/Node.js

TypeScript offers rapid development and a rich ecosystem, making it popular for API development.

// Basic LLM API handler
export const handler = async (event: APIGatewayEvent) => {
  const response = await fetch("https://api.anthropic.com/v1/messages", {
    method: "POST",
    headers: {
      "Content-Type": "application/json",
      "x-api-key": process.env.ANTHROPIC_API_KEY,
    },
    body: JSON.stringify({
      model: "claude-3-sonnet-20240229",
      messages: [{ role: "user", content: event.body }],
      max_tokens: 1000,
    }),
  });

  return {
    statusCode: 200,
    body: JSON.stringify(await response.json()),
  };
};

Java

Java provides enterprise-grade performance and extensive tooling, ideal for large-scale applications.

@RestController
public class LLMController {
    @Autowired
    private LLMService llmService;

    @PostMapping("/api/chat")
    public ResponseEntity<LLMResponse> processPrompt(@RequestBody PromptRequest request) {
        LLMResponse response = llmService.callAnthropic(request.getMessage());
        return ResponseEntity.ok(response);
    }
}

// Service class
@Service
public class LLMService {
    public LLMResponse callAnthropic(String message) {
        // HTTP client implementation for Anthropic API
        return anthropicClient.sendMessage(message);
    }
}

Golang

Go excels in performance, simplicity, and resource efficiency—perfect for API services.

Based on my experience with each of these, I would strongly recommend GoLang. Don’t shy away from learning a new language. That takes less than a week. But it is going to save months in your product life cycle, and will assure you a huge cost in deployment.

package main

import (
    "encoding/json"
    "net/http"
    "github.com/aws/aws-lambda-go/lambda"
    "github.com/aws/aws-lambda-go/events"
)

type LLMRequest struct {
    Message string `json:"message"`
}

type LLMResponse struct {
    Content string `json:"content"`
    Usage   Usage  `json:"usage"`
}

func handler(request events.APIGatewayProxyRequest) (events.APIGatewayProxyResponse, error) {
    var req LLMRequest
    json.Unmarshal([]byte(request.Body), &req)

    // Call Anthropic API
    response := callAnthropicAPI(req.Message)

    return events.APIGatewayProxyResponse{
        StatusCode: 200,
        Body:       string(response),
    }, nil
}

func main() {
    lambda.Start(handler)
}

Cloud Platform Deployment Options

Another important component is choosing the right cloud platform.

Note: If you are still thinking of deploying on premise, well.. you can skip this section, or the entire blog, or I would recommend that you should skip the entire project!!

AWS Deployment Methods

AWS provides several options for deploying your projects. Each of them has a value and you should understand their pros and cons - to make a meaningful choice.

EC2 Servers Traditional server deployment with full control over the environment.

# docker-compose.yml for EC2
version: "3.8"
services:
  llm-api:
    build: .
    ports:
      - "8080:8080"
    environment:
      - ANTHROPIC_API_KEY=${ANTHROPIC_API_KEY}

ECS Containers Managed container orchestration with auto-scaling capabilities.

{
  "family": "llm-api-task",
  "containerDefinitions": [
    {
      "name": "llm-api",
      "image": "your-account.dkr.ecr.us-west-2.amazonaws.com/llm-api:latest",
      "memory": 512,
      "cpu": 256,
      "essential": true,
      "portMappings": [
        {
          "containerPort": 8080,
          "protocol": "tcp"
        }
      ]
    }
  ]
}

Lambda Serverless Event-driven, pay-per-request model with automatic scaling.

# serverless.yml
service: llm-api
runtime: go1.x
functions:
  chat:
    handler: bin/main
    events:
      - http:
          path: /chat
          method: post
          cors: true
    environment:
      ANTHROPIC_API_KEY: ${env:ANTHROPIC_API_KEY}

Google Cloud Platform

Google has another set of unique offerings. Let us look at them.

Compute Engine

# GCP deployment.yaml
apiVersion: apps/v1
kind: Deployment
metadata:
  name: llm-api
spec:
  replicas: 3
  selector:
    matchLabels:
      app: llm-api
  template:
    spec:
      containers:
        - name: llm-api
          image: gcr.io/project/llm-api:latest
          ports:
            - containerPort: 8080

Cloud Run (Serverless)

This is a combination of serverless and containers; and has its own advantages.

# Cloud Run optimized Dockerfile
FROM golang:1.21-alpine AS builder
WORKDIR /app
COPY . .
RUN go build -o main .

FROM alpine:latest
RUN apk --no-cache add ca-certificates
COPY --from=builder /app/main .
CMD ["./main"]

Cloud Functions

This is purely serverless - inspired by AWS Lambda functions, and provides many of those features.

package llmapi

import (
    "encoding/json"
    "net/http"
    "github.com/GoogleCloudPlatform/functions-framework-go/functions"
)

func init() {
    functions.HTTP("ProcessLLM", processLLM)
}

func processLLM(w http.ResponseWriter, r *http.Request) {
    // LLM processing logic
    json.NewEncoder(w).Encode(response)
}

Microsoft Azure

That is Microsoft after all! But not as bad is Windows.

Virtual Machines

They have their own range of virtual machines

# ARM template snippet
"resources":
  [
    {
      "type": "Microsoft.Compute/virtualMachines",
      "apiVersion": "2021-03-01",
      "name": "llm-api-vm",
      "properties": { "hardwareProfile": { "vmSize": "Standard_B2s" } },
    },
  ]

Container Instances

And you can deploy containers as well.

# Azure Container Instance
apiVersion: 2018-10-01
location: eastus
properties:
  containers:
    - name: llm-api
      properties:
        image: your-registry.azurecr.io/llm-api:latest
        resources:
          requests:
            cpu: 1
            memoryInGb: 1.5
        ports:
          - port: 8080

Azure Functions

Just like GCP’s CloudFunctions, Azure has its own services inspired by AWS Lambda. Provides several of those features, but not enough.

package main

import (
    "context"
    "encoding/json"
    "github.com/Azure/azure-functions-go-worker/worker"
)

func ProcessLLMRequest(ctx context.Context, req worker.HTTPRequest) worker.HTTPResponse {
    // Process LLM request
    return worker.HTTPResponse{
        StatusCode: 200,
        Body:       responseJSON,
    }
}

Performance and Cost Comparison

That is a wide range of choices, let us compare each of them on the standard parameters

Platform	Method	Cold Start	Scaling	Cost Efficiency
AWS Lambda	Serverless	<100ms (Go)	Instant	Excellent
GCP Cloud Run	Serverless	<200ms	Fast	Good
Azure Functions	Serverless	<300ms	Fast	Good
ECS/GKE	Containers	N/A	Moderate	Fair
EC2/Compute	Servers	N/A	Manual	Poor

Recommendation: AWS Lambda with Golang

After evaluating all options, AWS Lambda with Golang emerges as the optimal choice for production such LLM APIs:

Why Golang?

Performance: Sub-100ms cold starts and excellent runtime performance
Resource Efficiency: Lower memory footprint reduces Lambda costs
Simplicity: Minimal dependencies and straightforward deployment
Concurrency: Built-in goroutines handle concurrent requests efficiently

Why AWS Lambda?

Cost Optimization: Pay only for actual usage, not idle time
Auto-scaling: Handles traffic spikes without configuration
Integration: Seamless connectivity with API Gateway, CloudWatch, and other AWS services
Reliability: Built-in redundancy and automatic failover

This combination delivers the perfect balance of performance, cost-efficiency, and operational simplicity for production LLM APIs, making it the clear winner for modern API deployments.

Building production-ready LLM systems requires navigating dozens of architectural decisions, each with far-reaching implications. At Yantratmika Solutions, we’ve helped organizations avoid the common pitfalls and build systems that scale. The devil, as always, is in the implementation details.