Top 100 Micro-SaaS Ideas for Developers with Minimal Startup Costs for High-Traffic Technical Portals

Leveraging Niche Technical Portals for Micro-SaaS Growth

The landscape of online businesses is increasingly fragmented, offering fertile ground for highly specialized Micro-SaaS ventures. For developers and e-commerce founders, identifying and serving these niches can yield significant returns with minimal upfront investment. This post outlines 100 Micro-SaaS ideas, focusing on those that can be built and scaled by targeting specific technical communities and high-traffic technical portals. The core strategy revolves around providing indispensable tools, data, or services that solve a precise problem for a defined audience, often leveraging existing platforms or APIs.

I. SEO & Growth Focused Micro-SaaS Ideas

These ideas center on enhancing visibility, traffic, and conversion rates for technical websites and e-commerce platforms. They often involve data analysis, automation, or specialized content generation.

A. Technical SEO Tools

1. Schema Markup Generator for Technical Docs: Automate the creation of structured data (JSON-LD) for API documentation, code snippets, and technical tutorials. This helps search engines understand and rank complex technical content more effectively.

<?php
// Example: Generating JSON-LD for a PHP function
function generateSchemaForFunction(string $functionName, string $description, array $parameters): string {
    $schema = [
        '@context' => 'https://schema.org',
        '@type' => 'WebPage',
        'name' => "Documentation for {$functionName}()",
        'description' => $description,
        'mainEntity' => [
            '@type' => 'SoftwareSourceCode',
            'name' => $functionName,
            'description' => $description,
            'programmingLanguage' => 'PHP',
            'codeRepository' => 'https://github.com/your/repo', // Placeholder
            'executableCode' => "function {$functionName}(" . implode(', ', array_keys($parameters)) . ") { ... }",
            'parameter' => array_map(function($paramName, $paramDetails) {
                return [
                    '@type' => 'HowToDirection',
                    'name' => $paramName,
                    'text' => $paramDetails['description'] ?? '',
                    'stepValue' => $paramDetails['type'] ?? 'mixed'
                ];
            }, array_keys($parameters), $parameters)
        ]
    ];
    return json_encode($schema, JSON_PRETTY_PRINT);
}

$params = [
    'userId' => ['type' => 'int', 'description' => 'The ID of the user to retrieve.'],
    'includeDetails' => ['type' => 'bool', 'description' => 'Whether to fetch detailed user information.']
];

echo generateSchemaForFunction('getUserProfile', 'Retrieves a user\'s profile information.', $params);
?>

2. Technical Content Keyword Difficulty Tool: A specialized tool that analyzes keyword difficulty specifically for technical terms, programming languages, frameworks, and niche software. It would go beyond generic SEO tools by understanding the context of developer search queries.

# Conceptual Python script for keyword difficulty analysis (simplified)
import requests
import json

def get_keyword_difficulty(keyword: str, api_key: str) -> dict:
    # This is a placeholder. A real tool would integrate with SERP analysis APIs
    # and potentially use NLP to understand technical context.
    # Example: Analyzing search volume, competitor density for technical terms.
    print(f"Analyzing difficulty for technical keyword: '{keyword}'")
    
    # Simulate API call to a hypothetical technical SEO API
    # url = f"https://api.techseo.com/v1/difficulty?keyword={keyword}&apiKey={api_key}"
    # response = requests.get(url)
    # data = response.json()
    
    # Simulated data
    simulated_data = {
        "keyword": keyword,
        "difficulty_score": 75, # Scale of 0-100
        "search_volume": 1500,
        "top_competitors": ["Stack Overflow", "GitHub", "Official Docs"],
        "technical_relevance_score": 0.9
    }
    return simulated_data

# Example Usage
API_KEY = "YOUR_TECHSEO_API_KEY" # Replace with actual API key
keyword = "python async await tutorial"
difficulty_info = get_keyword_difficulty(keyword, API_KEY)
print(json.dumps(difficulty_info, indent=2))

3. Code Snippet Performance Analyzer: A tool that analyzes the performance implications of common code snippets across different languages and frameworks. It could provide insights into execution time, memory usage, and potential bottlenecks for developers.

4. API Documentation Link Checker: A service that continuously crawls technical portals and checks the validity and accessibility of all external and internal links within API documentation. Essential for maintaining the integrity of developer resources.

# Example Bash script using curl to check link status
URL="https://api.example.com/docs/v1/users"
STATUS_CODE=$(curl -Is "$URL" | head -n 1 | awk '{print $2}')

if [ "$STATUS_CODE" = "200" ]; then
    echo "Link $URL is OK (Status: $STATUS_CODE)"
elif [ "$STATUS_CODE" = "404" ]; then
    echo "Link $URL is Broken (Status: $STATUS_CODE)"
else
    echo "Link $URL has unexpected status: $STATUS_CODE"
fi

5. Technical Backlink Analysis Tool: Focuses on identifying backlinks from high-authority technical websites (e.g., programming blogs, developer forums, open-source project pages) to help developers understand their content’s reach and authority.

B. E-commerce Technical Optimization

6. Shopify App Performance Auditor: Analyzes the impact of installed Shopify apps on site speed, Core Web Vitals, and overall user experience. Provides actionable recommendations for optimization or removal.

// Conceptual JavaScript for Shopify App Performance Audit (client-side)
function auditAppPerformance(appName) {
    console.log(`Auditing performance for Shopify app: ${appName}`);
    
    // Simulate checks for common performance impacts:
    // 1. Script loading time & impact on LCP/FID
    // 2. CSS file size & impact on CLS
    // 3. Network requests initiated by the app
    // 4. DOM element manipulation frequency
    
    const scriptLoadTime = Math.random() * 500 + 100; // ms
    const cssImpact = Math.random() * 0.2; // CLS impact score
    const networkRequests = Math.floor(Math.random() * 10) + 1;
    
    console.log(`- Script Load Time: ${scriptLoadTime.toFixed(2)}ms`);
    console.log(`- CLS Impact Score: ${clsImpact.toFixed(2)}`);
    console.log(`- Network Requests: ${networkRequests}`);
    
    if (scriptLoadTime > 300 || cssImpact > 0.1 || networkRequests > 5) {
        console.warn(`App "${appName}" may be negatively impacting performance. Consider optimization or removal.`);
    } else {
        console.log(`App "${appName}" appears to have minimal performance impact.`);
    }
}

// Example Usage
auditAppPerformance("Awesome Product Reviews App");
auditAppPerformance("Fast Checkout Button");

7. WooCommerce Product Data Validator: Ensures product data (SKUs, prices, attributes, descriptions) adheres to best practices for SEO and user experience. Can identify missing alt text, inconsistent pricing, or poorly formatted descriptions.

8. Headless Commerce SEO Optimizer: A tool specifically designed to help optimize SEO for headless commerce setups, focusing on SSR, pre-rendering, and proper meta tag management for dynamic content.

9. E-commerce Image Optimization Service: Automates the compression, resizing, and format conversion (e.g., WebP) of product images, crucial for fast loading times on e-commerce sites.

10. Conversion Rate Optimization (CRO) A/B Test Idea Generator for Technical Products: Suggests A/B test hypotheses specifically for technical products or services, based on user behavior patterns observed in developer communities.

II. Developer Tooling & Utilities Micro-SaaS

These ideas focus on providing essential tools that streamline development workflows, improve code quality, or simplify complex technical tasks.

A. Code & Development Workflow Enhancements

11. Real-time Code Collaboration Platform (Niche Language/Framework): A specialized version of existing tools, focusing on a less common language (e.g., Elixir, Rust) or a specific framework (e.g., SvelteKit, Nuxt.js), offering tailored features.

12. Automated API Documentation Generator (OpenAPI/Swagger): Scans codebases (e.g., Node.js, Python Flask/Django) and automatically generates or updates OpenAPI/Swagger specifications.

# Conceptual Python script using a library like drf-yasg for Django REST Framework
# Assumes you have Django and drf-yasg installed and configured.

from rest_framework import viewsets, serializers
from rest_framework.response import Response
from drf_yasg.utils import swagger_auto_schema
from drf_yasg import openapi

# --- Sample Models and Serializers ---
class Item:
    def __init__(self, id, name, description):
        self.id = id
        self.name = name
        self.description = description

class ItemSerializer(serializers.Serializer):
    id = serializers.IntegerField()
    name = serializers.CharField(max_length=100)
    description = serializers.CharField()

# --- Sample ViewSet ---
class ItemViewSet(viewsets.ViewSet):
    serializer_class = ItemSerializer
    
    # Define parameters for the list endpoint
    item_list_params = [
        openapi.Parameter('limit', openapi.IN_QUERY, description="Number of items to return", type=openapi.TYPE_INTEGER, default=10),
    ]

    @swagger_auto_schema(manual_parameters=item_list_params, responses={200: ItemSerializer(many=True)})
    def list(self, request):
        """
        Retrieve a list of items.
        """
        limit = request.query_params.get('limit', 10)
        items = [Item(i, f"Item {i}", f"Description for item {i}") for i in range(1, int(limit) + 1)]
        serializer = ItemSerializer(items, many=True)
        return Response(serializer.data)

    @swagger_auto_schema(responses={200: ItemSerializer()})
    def retrieve(self, request, pk=None):
        """
        Retrieve a specific item by its ID.
        """
        item = Item(pk, f"Item {pk}", f"Description for item {pk}")
        serializer = ItemSerializer(item)
        return Response(serializer.data)

# In your Django urls.py, you would typically register this ViewSet with DefaultRouter
# from django.urls import path, include
# from rest_framework.routers import DefaultRouter
# from .views import ItemViewSet
#
# router = DefaultRouter()
# router.register(r'items', ItemViewSet, basename='item')
# urlpatterns = [
#     path('api/', include(router.urls)),
#     # ... other urls
# ]
# drf-yasg will then generate the /swagger/ endpoint automatically.

13. Cloud Function/Lambda Cost Estimator: A tool that helps developers estimate the cost of running serverless functions based on execution time, memory allocation, and invocation count. Supports AWS Lambda, Google Cloud Functions, Azure Functions.

14. Git Branching Strategy Visualizer: Helps teams visualize and enforce complex Git branching strategies (e.g., Gitflow, GitHub Flow) with clear diagrams and potential integrations with CI/CD pipelines.

15. Dockerfile Optimizer: Analyzes Dockerfiles and suggests optimizations for build speed, image size, and security (e.g., multi-stage builds, layer caching, minimizing dependencies).

# Example Dockerfile analysis for optimization suggestions
# This is a conceptual script, a real tool would parse Dockerfiles and apply rules.

DOCKERFILE_PATH="./Dockerfile"

echo "Analyzing Dockerfile: $DOCKERFILE_PATH"

# Rule 1: Check for multi-stage builds
if ! grep -q "FROM .* AS" "$DOCKERFILE_PATH"; then
    echo "[WARN] Consider using multi-stage builds to reduce final image size."
fi

# Rule 2: Check for apt-get update && apt-get install in the same RUN command
if grep -q "RUN apt-get update" "$DOCKERFILE_PATH" && grep -q "RUN .* apt-get install" "$DOCKERFILE_PATH"; then
    # More sophisticated check needed to ensure they are in the SAME RUN command
    echo "[INFO] Ensure 'apt-get update' and 'apt-get install' are in the same RUN command to leverage layer caching."
fi

# Rule 3: Check for unnecessary files copied before build dependencies are installed
# (Requires more complex parsing)
echo "[INFO] Review COPY commands. Ensure they are placed after build dependencies are installed if possible."

# Rule 4: Check for use of specific base images (e.g., alpine for smaller size)
if grep -q "FROM ubuntu" "$DOCKERFILE_PATH"; then
    echo "[SUGGESTION] Consider using Alpine Linux as a base image for smaller footprints."
fi

echo "Analysis complete."

16. Regex Tester & Generator for Specific Use Cases: A highly specialized regex tool focused on common patterns in log files, configuration files, or specific data formats relevant to developers.

17. Environment Variable Management Tool: Simplifies the management and deployment of environment variables across different environments (dev, staging, prod) for various platforms (Docker, Kubernetes, serverless).

18. Code Style & Linting Profile Manager: Allows developers to create, share, and manage custom linting and code style configurations for popular languages (e.g., ESLint, Prettier, Black, RuboCop).

19. Dependency Vulnerability Scanner (Niche Packages): Focuses on identifying vulnerabilities in less common or highly specific package ecosystems (e.g., specific Python libraries for scientific computing, niche JavaScript frameworks).

20. API Mocking Service for Specific Protocols: Provides mock API endpoints that adhere to specific protocols beyond REST/GraphQL, such as gRPC, SOAP, or custom TCP protocols, for testing purposes.

B. Data & Database Tools

21. SQL Query Performance Analyzer (Cloud DB Specific): Analyzes SQL query performance specifically for cloud databases like Aurora, BigQuery, or Snowflake, providing tailored optimization tips.

-- Example SQL query analysis for PostgreSQL (conceptual)
-- A real tool would parse EXPLAIN ANALYZE output and provide actionable insights.

EXPLAIN ANALYZE
SELECT
    u.id,
    u.username,
    COUNT(o.id) AS order_count
FROM
    users u
JOIN
    orders o ON u.id = o.user_id
WHERE
    u.created_at > '2023-01-01'
GROUP BY
    u.id, u.username
ORDER BY
    order_count DESC
LIMIT 10;

/*
Conceptual Analysis Output Interpretation:
- Look for Sequential Scans on large tables (users, orders) if indexes are missing.
- Check for high costs associated with HashAggregate or Sort operations.
- Identify inefficient joins (e.g., Nested Loop joins on large datasets).
- Suggest adding indexes on 'users.created_at' and 'orders.user_id'.
- Recommend optimizing the GROUP BY clause if possible.
*/

22. NoSQL Data Modeling Assistant: Helps developers design efficient data models for NoSQL databases (MongoDB, Cassandra, DynamoDB) based on query patterns and access requirements.

23. Database Schema Migration Tool (Cross-DB): Facilitates schema migrations between different database types (e.g., MySQL to PostgreSQL, SQL to MongoDB), handling data type conversions and structural differences.

24. Data Anonymization Service for Developers: Provides tools to anonymize sensitive data within databases or datasets, essential for testing, development, and compliance (GDPR, CCPA).

25. Real-time Database Monitoring Dashboard (Niche DB): Offers a specialized dashboard for monitoring the health, performance, and usage of less common databases (e.g., Redis Cluster, ScyllaDB, CockroachDB).

III. Niche Technical Community & Content Micro-SaaS

These ideas target specific developer communities or create specialized content platforms that cater to their unique needs.

A. Community & Collaboration Platforms

26. Q&A Platform for Specific Technologies: A Stack Overflow-like platform but hyper-focused on a single technology (e.g., Kubernetes, TensorFlow, WebAssembly).

27. Developer Event Aggregator (Geo-Targeted): Aggregates and lists technical meetups, conferences, and workshops within a specific geographic region or for a particular technology stack.

28. Open Source Project Contribution Finder: Helps developers find open-source projects that match their skills and interests, focusing on projects actively seeking contributions or specific types of help.

29. Technical Mentorship Matching Service: Connects junior developers with experienced mentors within specific tech domains (e.g., backend, frontend, data science, cybersecurity).

30. Developer Portfolio Showcase Platform: A platform for developers to easily create and manage professional portfolios, highlighting projects, code contributions, and skills. Could integrate with GitHub.

B. Content & Learning Platforms

31. Interactive Tutorial Platform for Niche Languages/Frameworks: Creates interactive coding environments directly in the browser for learning specific technologies (e.g., learning Rust via interactive exercises).

// Conceptual JavaScript for an interactive tutorial platform (client-side)
function runCodeInSandbox(code, language) {
    console.log(`Attempting to run code in sandbox for: ${language}`);
    
    // In a real scenario, this would involve:
    // 1. Using a WebAssembly-based interpreter (e.g., Pyodide for Python, Rust WASM)
    // 2. Or, sending code to a secure backend execution environment.
    // 3. Capturing stdout, stderr, and execution results.
    
    // Simulate execution
    let output = `Simulated output for ${language}:\n`;
    try {
        // For simplicity, let's assume a direct eval for demonstration,
        // BUT THIS IS EXTREMELY DANGEROUS IN PRODUCTION.
        // A real sandbox is mandatory.
        if (language === 'javascript') {
            output += eval(code); // DANGEROUS - DO NOT USE IN PRODUCTION
        } else {
            output += `Execution of ${language} code not supported in this demo.`;
        }
        console.log("Execution successful (simulated).");
    } catch (error) {
        output += `Error: ${error.message}`;
        console.error("Execution failed (simulated).");
    }
    return output;
}

const pythonCode = `
def greet(name):
    return f"Hello, {name}!"
print(greet("Developer"))
`;

const jsCode = `
function add(a, b) { return a + b; }
console.log(add(5, 10));
`;

console.log(runCodeInSandbox(pythonCode, 'python')); // Would require Pyodide or similar
console.log(runCodeInSandbox(jsCode, 'javascript'));

32. Curated Newsletter for Emerging Tech Trends: A highly focused newsletter summarizing the latest developments, research papers, and industry news in a specific emerging field (e.g., AI ethics, quantum computing, blockchain interoperability).

33. Technical Book/Article Summarizer: Uses AI to generate concise summaries of lengthy technical books, research papers, or blog posts, saving developers time.

34. Glossary/Wiki for Niche Technical Jargon: A dedicated resource defining and explaining terms, acronyms, and concepts specific to a particular technology or industry domain.

35. Cheat Sheet Generator for APIs/Frameworks: Automatically generates printable or digital cheat sheets for popular APIs and frameworks, summarizing key functions, endpoints, and syntax.

IV. Infrastructure & Operations Micro-SaaS

These tools address challenges in deploying, managing, and monitoring technical infrastructure, often with a focus on cloud-native or specific operational environments.

A. Cloud & DevOps Tools

36. Cloud Cost Optimization Dashboard (Multi-Cloud): Provides a unified view of cloud spending across AWS, Azure, and GCP, with recommendations for cost savings.

37. Kubernetes Cluster Health Monitor: A specialized tool for monitoring the health, performance, and security of Kubernetes clusters, going beyond basic metrics.

# Example Kubernetes health check configuration (Conceptual)
apiVersion: v1
kind: Pod
metadata:
  name: health-checker-pod
spec:
  containers:
  - name: checker
    image: your-custom-health-checker-image # Image with your monitoring logic
    command: ["/app/monitor", "--cluster-name", "my-prod-cluster"]
    env:
    - name: KUBERNETES_API_SERVER
      value: "https://kubernetes.default.svc"
    # Add probes for the checker container itself
    livenessProbe:
      httpGet:
        path: /healthz
        port: 8080
      initialDelaySeconds: 15
      periodSeconds: 20
    readinessProbe:
      httpGet:
        path: /readyz
        port: 8080
      initialDelaySeconds: 5
      periodSeconds: 10
  # ServiceAccount for accessing Kubernetes API if needed
  serviceAccountName: health-checker-sa
---
apiVersion: rbac.authorization.k8s.io/v1
kind: ClusterRole
metadata:
  name: health-checker-role
rules:
- apiGroups: [""]
  resources: ["nodes", "pods", "services", "endpoints", "namespaces"]
  verbs: ["get", "list", "watch"]
- apiGroups: ["apps"]
  resources: ["deployments", "statefulsets", "daemonsets"]
  verbs: ["get", "list", "watch"]
- apiGroups: ["batch"]
  resources: ["jobs", "cronjobs"]
  verbs: ["get", "list", "watch"]
# Add more rules as needed for specific checks (e.g., ingresses, persistentvolumes)
---
apiVersion: v1
kind: ServiceAccount
metadata:
  name: health-checker-sa
  namespace: default # Or the namespace where the pod runs
---
apiVersion: rbac.authorization.k8s.io/v1
kind: ClusterRoleBinding
metadata:
  name: health-checker-binding
subjects:
- kind: ServiceAccount
  name: health-checker-sa
  namespace: default # Or the namespace where the pod runs
roleRef:
  kind: ClusterRole
  name: health-checker-role
  apiGroup: rbac.authorization.k8s.io

38. Infrastructure as Code (IaC) Linting & Security Scanner: Analyzes Terraform, CloudFormation, or Ansible code for security misconfigurations, compliance issues, and best practice violations.

39. CI/CD Pipeline Optimizer: Identifies bottlenecks and inefficiencies in CI/CD pipelines (e.g., Jenkins, GitLab CI, GitHub Actions) and suggests optimizations for faster build and deployment times.

40. Serverless Application Monitoring Tool: Provides deep insights into the performance, errors, and costs of serverless applications, correlating events across different functions and services.

B. Security & Compliance Tools

41. Web Application Firewall (WAF) Rule Generator for Niche Attacks: Creates custom WAF rulesets tailored to protect against specific, emerging threats targeting particular technologies or frameworks.

42. API Security Scanner: Automatically scans APIs for common security vulnerabilities like injection flaws, broken authentication, excessive data exposure, and rate limiting issues.

# Conceptual Python script using requests to test API security (OWASP Top 10 focus)
import requests
import json

BASE_URL = "https://api.example.com/v1"
TARGET_ENDPOINT = "/users" # Example endpoint

def test_injection_vulnerability(endpoint: str, param_name: str):
    print(f"Testing for SQL Injection on {endpoint} parameter '{param_name}'...")
    
    # Basic SQLi payloads
    payloads = [
        "' OR '1'='1",
        "' OR '1'='1' --",
        "1; DROP TABLE users; --", # Highly destructive, use with extreme caution
        "admin'--",
        "'; EXEC xp_cmdshell('dir'); --" # Example for SQL Server
    ]
    
    for payload in payloads:
        test_url = f"{BASE_URL}{endpoint}"
        params = {param_name: payload}
        
        try:
            response = requests.get(test_url, params=params, timeout=5)
            # Heuristics: Check for error messages, unexpected data, or status codes
            if "SQL syntax error" in response.text or \
               "You have an error in your SQL syntax" in response.text or \
               response.status_code == 500: # Generic error code
                print(f"[VULNERABLE] Payload '{payload}' triggered an error or unexpected response.")
                return True
        except requests.exceptions.RequestException as e:
            print(f"Request failed for payload '{payload}': {e}")
            
    print("No obvious SQL injection vulnerabilities detected with basic payloads.")
    return False

# Example Usage
# test_injection_vulnerability(TARGET_ENDPOINT, "user_id") 
# This is a very basic example. Real tools use more sophisticated techniques.
print("Conceptual API Security Scan - SQL Injection Test")
print("Note: Actual execution requires a live, vulnerable API and careful ethical considerations.")

43. Dependency Management Security Auditor: Scans project dependencies (npm, pip, Maven, Composer) for known vulnerabilities and suggests secure alternatives or patches.

44. Compliance Report Generator (GDPR, SOC2): Automates the generation of reports required for compliance with regulations like GDPR or standards like SOC2, based on system configurations and logs.

45. Secrets Management & Rotation Service: Securely stores and automatically rotates sensitive credentials (API keys, database passwords) across different services and environments.

V. Data & Analytics Micro-SaaS

These ideas focus on collecting, processing, and visualizing data, often for specific technical or business intelligence purposes.

A. Specialized Data Collection & Processing

46. Log Analysis & Anomaly Detection: Ingests logs from various sources (servers, applications, cloud services) and uses machine learning to detect anomalies and potential issues.

# Conceptual Python script for log anomaly detection using a simple statistical method
import numpy as np
from collections import Counter

def detect_log_anomalies(log_lines: list[str], threshold_factor: float = 3.0) -> list[str]:
    """
    Detects anomalies based on frequency of log messages.
    Assumes log_lines are pre-processed to extract meaningful message types.
    """
    message_counts = Counter(log_lines)
    
    # Calculate mean and standard deviation of message frequencies
    frequencies = list(message_counts.values())
    if not frequencies:
        return []
        
    mean_freq = np.mean(frequencies)
    std_dev_freq = np.std(frequencies)
    
    anomalies = []
    for message, freq in message_counts.items():
        if freq > mean_freq + threshold_factor * std_dev_freq:
            anomalies.append(f"Anomaly detected: Message '{message}' occurred {freq} times (expected ~{mean_freq:.2f}).")
            
    return anomalies

# Example Usage
sample_logs = [
    "INFO: User logged in",
    "WARN: Disk space low",
    "INFO: Request processed",
    "ERROR: Database connection failed",
    "INFO: User logged in",
    "INFO: Request processed",
    "INFO: Request processed",
    "INFO: Request processed",
    "INFO: Request processed",
    "INFO: Request processed",
    "WARN: Disk space low",
    "INFO: User logged in",
    "ERROR: Database connection failed",
    "ERROR: Database connection failed",
    "ERROR: Database connection failed",
    "ERROR: Database connection failed",
    "ERROR: Database connection failed",
    "ERROR: Database connection failed",
    "ERROR: Database connection failed",
    "ERROR: Database connection failed",
    "ERROR: Database connection failed",
    "ERROR: Database connection failed",
]

anomalous_messages = detect_log_anomalies(sample_logs)
if anomalous_messages:
    print("Detected Anomalies:")
    for anomaly in anomalous_messages:
        print(f"- {anomaly}")