Top 10 Developer Tooling and Productivity SaaS Ideas to Launch in 2026 in Highly Competitive Technical Niches

1. AI-Powered Code Review & Refactoring Assistant

The sheer volume of code being written daily necessitates intelligent automation for quality assurance and maintainability. This SaaS would go beyond simple linting, offering context-aware suggestions for performance optimizations, security vulnerabilities, and adherence to architectural patterns. Think of it as a senior engineer embedded in every pull request.

Core Functionality:

• Static Analysis with Semantic Understanding: Leverage large language models (LLMs) trained on vast code repositories to understand code logic, not just syntax.
• Automated Refactoring Suggestions: Propose concrete code changes to improve readability, reduce complexity (e.g., cyclomatic complexity), and enhance performance.
• Security Vulnerability Detection: Identify common security flaws (e.g., SQL injection, XSS, insecure deserialization) with explanations and remediation advice.
• Performance Bottleneck Identification: Pinpoint inefficient algorithms, excessive database queries, or suboptimal I/O operations.
• Style & Convention Enforcement: Beyond basic linting, enforce project-specific or team-wide architectural guidelines.
• Integration with CI/CD: Seamless integration with GitHub, GitLab, Bitbucket, Jenkins, etc., to provide feedback directly within the development workflow.

Technical Stack Considerations:

• Backend: Python (Flask/Django) or Node.js (Express) for API development.
• AI/ML: Python with libraries like TensorFlow, PyTorch, Hugging Face Transformers for LLM integration. Fine-tuning open-source models (e.g., CodeLlama, StarCoder) on proprietary datasets could be a differentiator.
• Database: PostgreSQL for structured data, potentially a vector database (e.g., Pinecone, Weaviate) for semantic code similarity searches.
• Frontend: React or Vue.js for a dynamic user interface.
• Infrastructure: Docker, Kubernetes for scalability, cloud providers like AWS/GCP/Azure for compute and storage.

Example API Endpoint (Conceptual):

from flask import Flask, request, jsonify
# Assume 'code_analyzer' is a module with LLM integration
from code_analyzer import analyze_code, suggest_refactoring, detect_security_issues

app = Flask(__name__)

@app.route('/analyze', methods=['POST'])
def analyze_pull_request():
    data = request.get_json()
    code_diff = data.get('code_diff') # e.g., a git diff string
    language = data.get('language')
    project_rules = data.get('project_rules') # Custom rules/patterns

    if not code_diff or not language:
        return jsonify({"error": "Missing 'code_diff' or 'language'"}), 400

    analysis_results = {
        "refactoring_suggestions": suggest_refactoring(code_diff, language, project_rules),
        "security_vulnerabilities": detect_security_issues(code_diff, language),
        "performance_insights": analyze_code(code_diff, language, "performance")
    }

    return jsonify(analysis_results)

if __name__ == '__main__':
    app.run(debug=True, port=5000)

2. Intelligent API Gateway & Mocking Service

As microservice architectures become the norm, managing API contracts, ensuring consistent behavior, and facilitating parallel development becomes critical. This SaaS would offer a dynamic API gateway with advanced mocking capabilities that can adapt to evolving API schemas.

Core Functionality:

• Schema-Driven Routing: Route requests based on OpenAPI/Swagger specifications.
• Dynamic Mocking: Generate realistic mock responses based on schema definitions, including data type validation and conditional logic (e.g., return 500 error if `status` field is “failed”).
• Contract Testing Integration: Automatically generate contract tests or validate incoming requests against defined schemas.
• Traffic Simulation: Simulate various load conditions and error scenarios for testing.
• Request/Response Transformation: Modify requests/responses on the fly to adapt to different service versions or client needs.
• Observability: Centralized logging, tracing, and metrics for all API traffic.

Technical Stack Considerations:

• Gateway Core: Envoy Proxy, Nginx with Lua scripting, or a custom Go/Rust implementation for high performance.
• Mocking Engine: Custom logic or integration with libraries like Prism.js or Mockoon.
• Schema Management: PostgreSQL or a dedicated schema registry.
• Configuration: YAML or JSON for defining routes, mocks, and transformations.
• Observability: Prometheus for metrics, Elasticsearch/Loki for logs, Jaeger/Tempo for tracing.

Example Nginx Configuration Snippet (for routing):

http {
    # ... other configurations ...

    upstream user_service_v1 {
        server 192.168.1.10:8080;
    }

    upstream user_service_v2 {
        server 192.168.1.11:8080;
    }

    server {
        listen 80;
        server_name api.example.com;

        location /users/ {
            # Example: Route based on header for versioning
            if ($http_x_api_version = "v2") {
                proxy_pass http://user_service_v2;
                proxy_set_header X-API-Version "v2";
                proxy_set_header Host $host;
                proxy_set_header X-Real-IP $remote_addr;
                proxy_set_header X-Forwarded-For $proxy_add_x_forwarded_for;
                proxy_set_header X-Forwarded-Proto $scheme;
                break;
            }

            # Default to v1
            proxy_pass http://user_service_v1;
            proxy_set_header X-API-Version "v1";
            proxy_set_header Host $host;
            proxy_set_header X-Real-IP $remote_addr;
            proxy_set_header X-Forwarded-For $proxy_add_x_forwarded_for;
            proxy_set_header X-Forwarded-Proto $scheme;
        }

        # ... other locations for different services ...
    }
}

3. Automated Database Schema Migration & Rollback Tool

Database migrations are a frequent source of production incidents. A robust, automated tool that handles schema changes, versioning, and safe rollbacks across different database technologies is highly valuable.

Core Functionality:

• Multi-Database Support: PostgreSQL, MySQL, SQL Server, Oracle, MongoDB, etc.
• Declarative Schema Definitions: Define desired schema states, not just incremental changes.
• Automated Migration Generation: Generate SQL or NoSQL migration scripts from schema diffs.
• Zero-Downtime Migration Strategies: Implement techniques like blue-green deployments for databases, online schema changes.
• Automated Rollback: Generate and execute rollback scripts automatically upon failure.
• Version Control Integration: Store migration scripts in Git alongside application code.
• Pre- and Post-Migration Hooks: Execute custom scripts before and after migrations.

Technical Stack Considerations:

• Language: Go or Java for performance and cross-platform compatibility.
• Database Connectors: Use native drivers for each supported database.
• Schema Diffing: Libraries for comparing database schemas (e.g., `pg_diff` for PostgreSQL).
• State Management: A dedicated table in each target database to track migration versions.
• CLI Tool: A robust command-line interface for developers and CI/CD pipelines.

Example CLI Command (Conceptual):

# Initialize a new migration project
dbmigrate init --driver postgres --dsn "postgresql://user:password@host:port/dbname"

# Create a new migration file
dbmigrate create add_users_table

# Apply pending migrations
dbmigrate up

# Rollback the last migration
dbmigrate down

# Generate a migration script from schema diff
dbmigrate diff --output migrations/003_add_products_table.sql

4. Real-time Performance Monitoring & Anomaly Detection for E-commerce

E-commerce businesses live and die by their performance. This SaaS would provide deep, real-time insights into application performance, user experience metrics, and infrastructure health, with a focus on identifying and alerting on anomalies that impact conversions.

Core Functionality:

• Frontend Performance: Real User Monitoring (RUM) for page load times, Core Web Vitals, JavaScript errors.
• Backend Performance: APM for request latency, error rates, database query times, external API calls.
• Infrastructure Monitoring: CPU, memory, disk I/O, network traffic for servers and databases.
• E-commerce Specific Metrics: Cart abandonment rates, checkout completion times, product view to add-to-cart conversion.
• AI-Powered Anomaly Detection: Automatically identify deviations from normal performance patterns (e.g., sudden spike in latency, drop in conversion rate).
• Root Cause Analysis: Correlate frontend, backend, and infrastructure metrics to pinpoint the source of issues.
• Alerting: Configurable alerts via Slack, PagerDuty, email.

Technical Stack Considerations:

• Data Collection: Agents (e.g., OpenTelemetry SDKs) for backend, JavaScript snippets for frontend.
• Time-Series Database: Prometheus, InfluxDB, TimescaleDB for storing metrics.
• Log Aggregation: Elasticsearch/OpenSearch, Loki.
• Tracing: Jaeger, Tempo.
• Anomaly Detection: Python with scikit-learn, Prophet, or specialized anomaly detection libraries.
• Frontend: React/Vue.js for dashboard.
• Backend: Go or Java for high-throughput data ingestion and processing.

Example JavaScript for RUM (Conceptual):

// Assume 'rum_collector_api' is the endpoint for your SaaS
const RUM_COLLECTOR_API = 'https://api.your-rum-saas.com/v1/metrics';

function sendRumData(data) {
    navigator.sendBeacon(RUM_COLLECTOR_API, JSON.stringify(data));
}

// Measure page load time
window.addEventListener('load', () => {
    const loadTime = performance.now();
    const pageData = {
        timestamp: Date.now(),
        pageUrl: window.location.href,
        loadTimeMs: loadTime,
        // Add other relevant metrics like FCP, LCP, FID
    };
    sendRumData(pageData);
});

// Track JavaScript errors
window.onerror = function(message, source, lineno, colno, error) {
    const errorData = {
        timestamp: Date.now(),
        pageUrl: window.location.href,
        errorMessage: message,
        source: source,
        lineNumber: lineno,
        columnNumber: colno,
        stackTrace: error && error.stack ? error.stack : 'N/A'
    };
    sendRumData(errorData);
    return true; // Prevent default error handling
};

// Track e-commerce specific events (simplified)
document.body.addEventListener('click', (event) => {
    if (event.target.closest('.add-to-cart-button')) {
        sendRumData({
            timestamp: Date.now(),
            eventType: 'addToCart',
            productId: event.target.dataset.productId,
            pageUrl: window.location.href
        });
    }
    // ... similar logic for checkout button clicks ...
});

5. Automated Security Vulnerability Scanning & Remediation for Cloud-Native Apps

Securing cloud-native applications (containers, Kubernetes, serverless) is complex. This SaaS would automate the discovery and remediation of vulnerabilities across the entire application lifecycle, from code to deployed infrastructure.

Core Functionality:

• Container Image Scanning: Detect known vulnerabilities (CVEs) in OS packages and application dependencies.
• Kubernetes Security Posture Management: Audit cluster configurations, RBAC policies, network policies for misconfigurations.
• IaC Scanning: Analyze Terraform, CloudFormation, Ansible for security flaws.
• Runtime Security: Monitor container and pod behavior for suspicious activity.
• Dependency Management: Track and alert on outdated or vulnerable libraries.
• Automated Remediation: For certain classes of vulnerabilities (e.g., updating a base image, modifying a K8s manifest), provide automated fixes.
• Compliance Reporting: Generate reports for standards like CIS, SOC 2, GDPR.

Technical Stack Considerations:

Technical Stack Considerations:

• Scanning Engines: Integration with tools like Trivy, Clair, Kube-bench, Checkov.
• Backend: Go or Rust for performance-intensive scanning and agent development.
• Database: PostgreSQL for storing scan results, vulnerability data.
• Cloud Integrations: AWS SDK, Azure SDK, GCP SDK for interacting with cloud provider APIs.
• Kubernetes API Client: Client libraries for Go, Python, or Java.
• Frontend: React/Vue.js for dashboard and reporting.

Example Trivy Command (Container Scanning):

# Scan a Docker image for vulnerabilities
trivy image --severity HIGH,CRITICAL your-dockerhub-repo/your-app:latest

# Scan a Kubernetes cluster configuration
trivy k8s --cluster --report summary

# Scan Terraform files for misconfigurations
trivy config --terraform path/to/your/terraform/

6. Intelligent Log Management & Anomaly Detection Platform

Logs are a goldmine of information, but often buried under noise. This SaaS would provide advanced log aggregation, parsing, searching, and crucially, AI-driven anomaly detection to surface critical issues before they escalate.

Core Functionality:

• Centralized Log Aggregation: Collect logs from various sources (servers, containers, applications, cloud services).
• Schema-on-Read Parsing: Automatically parse unstructured logs into structured data.
• Powerful Search & Filtering: Fast, flexible querying capabilities (similar to ELK stack).
• Log Pattern Recognition: Identify recurring log messages and group similar entries.
• AI-Powered Anomaly Detection: Detect unusual log volumes, error patterns, or security-related events.
• Alerting: Trigger alerts based on search queries or detected anomalies.
• Cost Optimization: Intelligent data tiering and retention policies.

Technical Stack Considerations:

• Ingestion: Fluentd, Logstash, Vector.dev.
• Storage: Elasticsearch/OpenSearch, Loki, ClickHouse.
• Processing/Analysis: Python/Go for anomaly detection algorithms (e.g., clustering, time-series analysis).
• Frontend: Grafana, Kibana, or a custom React/Vue.js dashboard.
• Scalability: Distributed systems architecture, Kafka for buffering.

Example Logstash Configuration Snippet (Parsing Apache Logs):

input {
  file {
    path => "/var/log/apache2/access.log"
    start_position => "beginning"
    sincedb_path => "/dev/null" # For demonstration, use persistent path in production
  }
}

filter {
  grok {
    match => { "message" => "%{COMBINEDAPACHELOG}" }
  }
  date {
    match => [ "timestamp", "dd/MMM/yyyy:HH:mm:ss Z" ]
  }
  # Add GeoIP lookup for IP addresses
  geoip {
    source => "clientip"
  }
}

output {
  elasticsearch {
    hosts => ["http://localhost:9200"]
    index => "apache-logs-%{+YYYY.MM.dd}"
  }
  stdout { codec => rubydebug }
}

7. Automated Infrastructure Drift Detection & Remediation

Infrastructure as Code (IaC) is powerful, but manual changes or unexpected modifications can lead to configuration drift, causing instability and security risks. This SaaS would continuously monitor infrastructure state against its desired IaC definition.

Core Functionality:

• IaC Integration: Connects to Git repositories containing Terraform, CloudFormation, Ansible, Pulumi code.
• Cloud Provider APIs: Regularly queries cloud provider APIs (AWS, Azure, GCP) to get the actual state of resources.
• Drift Detection: Compares actual state with the state defined in IaC.
• Automated Remediation: Option to automatically apply IaC changes to correct drift or alert for manual intervention.
• Policy Enforcement: Define and enforce infrastructure compliance policies (e.g., all S3 buckets must be private).
• Reporting: Dashboards and reports detailing drift incidents and remediation actions.

Technical Stack Considerations:

• Language: Go or Python for interacting with cloud APIs and IaC tools.
• Cloud SDKs: AWS SDK, Azure SDK, GCP SDK.
• IaC Parsers: Libraries to parse Terraform (`hclwrite`), CloudFormation, etc.
• State Storage: PostgreSQL or a similar relational database to store detected drifts and remediation status.
• Scheduler: Cron jobs or a dedicated scheduler like Airflow for periodic checks.
• Alerting: Integration with Slack, PagerDuty.

Example Python Script Snippet (Conceptual Drift Check):

import boto3
import json
from hcl2 import loads # Example for parsing HCL

# Assume 'get_terraform_state' reads your Terraform state file
# Assume 'get_aws_s3_bucket_config' fetches actual AWS bucket config

def check_s3_bucket_drift(bucket_name, terraform_config):
    aws_s3 = boto3.client('s3')
    try:
        actual_config = aws_s3.get_bucket_acl(Bucket=bucket_name) # Simplified example
        # Compare actual_config with terraform_config['s3_bucket'][bucket_name]['acl']
        # This comparison logic needs to be robust, handling differences in representation
        if not configs_match(actual_config, terraform_config):
            print(f"Drift detected for S3 bucket: {bucket_name}")
            # Trigger remediation or alert
            return True
    except Exception as e:
        print(f"Error checking bucket {bucket_name}: {e}")
    return False

def load_terraform_config(tfstate_path):
    with open(tfstate_path, 'r') as f:
        # In a real scenario, you'd parse the actual Terraform state file
        # For simplicity, let's assume a parsed structure
        # This is a placeholder, actual parsing is complex
        return {
            "s3_bucket": {
                "my-secure-bucket": {"acl": "private"}
            }
        }

if __name__ == "__main__":
    tf_state = load_terraform_config("path/to/terraform.tfstate")
    buckets_to_check = ["my-secure-bucket"] # Get from TF state or config

    for bucket in buckets_to_check:
        check_s3_bucket_drift(bucket, tf_state)

8. AI-Assisted Test Case Generation & Optimization

Writing comprehensive test suites is time-consuming. This SaaS would leverage AI to analyze application code and requirements, automatically generating relevant test cases and identifying redundant or inefficient tests.

Core Functionality:

• Code Analysis: Analyze code paths, branches, and complexity to identify areas needing testing.
• Requirements Analysis: Parse user stories or specifications to generate functional test cases.
• Test Data Generation: Create realistic and varied test data, including edge cases.
• Test Optimization: Identify flaky tests, redundant tests, and suggest improvements to reduce execution time.
• Test Framework Integration: Generate tests compatible with popular frameworks (e.g., Pytest, JUnit, Jest).
• Mutation Testing Support: Help assess the effectiveness of the test suite.

Technical Stack Considerations:

• Backend: Python (Flask/Django) for AI/ML integration.
• AI/ML: LLMs (fine-tuned CodeLlama, GPT variants) for code understanding and generation, potentially reinforcement learning for optimization.
• Static Analysis Tools: Integration with linters and code complexity tools.
• Database: PostgreSQL to store generated tests, metadata, and analysis results.
• Frontend: React/Vue.js for user interaction and visualization of test coverage/generation.

Example Python Code for Test Generation (Conceptual):

from openai import OpenAI # Assuming use of OpenAI API
import ast # Python's Abstract Syntax Trees module

# Assume 'client' is initialized with your OpenAI API key
client = OpenAI(api_key="YOUR_API_KEY")

def analyze_function_for_tests(func_code_string):
    try:
        tree = ast.parse(func_code_string)
        function_node = None
        for node in ast.walk(tree):
            if isinstance(node, ast.FunctionDef):
                function_node = node
                break

        if not function_node:
            return "Could not find function definition."

        function_name = function_node.name
        # Extract function signature and docstring for context
        signature = ast.unparse(function_node.args)
        docstring = ast.get_docstring(function_node)

        prompt = f"""
        Analyze the following Python function and generate relevant unit tests using pytest.
        Consider edge cases, different input types, and potential error conditions.

        Function Signature:
        def {function_name}({signature}):

        Docstring:
        {docstring if docstring else "No docstring provided."}

        Function Body:
        {ast.unparse(function_node.body)}

        Generate pytest test cases for the function '{function_name}'.
        Output only the Python code for the tests.
        """

        response = client.chat.completions.create(
            model="gpt-4", # Or a code-specific model
            messages=[
                {"role": "system", "content": "You are a helpful assistant that generates Python unit tests."},
                {"role": "user", "content": prompt}
            ],
            temperature=0.5,
        )
        return response.choices[0].message.content

    except Exception as e:
        return f"Error generating tests: {e}"

# Example Usage:
function_code = """
def divide(a, b):
    \"\"\"Divides two numbers. Raises ValueError for division by zero.\"\"\"
    if b == 0:
        raise ValueError("Cannot divide by zero")
    return a / b
"""

generated_tests = analyze_function_for_tests(function_code)
print(generated_tests)

9. Collaborative Development Environment as a Service (CDEaaS)

Setting up consistent, reproducible development environments is a perennial challenge. This SaaS would provide pre-configured, cloud-hosted development environments that developers can access instantly, ensuring consistency and speeding up onboarding.

Core Functionality:

• Pre-configured Environments: Environments tailored for specific stacks (e.g., Node.js/React, Python/Django, Java/Spring).
• Containerized Workspaces: Based on Docker or similar technologies for isolation and reproducibility.
• IDE Integration: Support for VS Code (via Remote Development extensions), JetBrains IDEs.
• Version Control Integration: Seamless connection to Git repositories.
• Collaboration Features: Real-time code sharing, pair programming, integrated chat.
• On-Demand Provisioning: Spin up new environments in minutes.
• Resource Management: Control compute, memory, and storage allocated to each environment.

Technical Stack Considerations:

• Orchestration: Kubernetes for managing containerized environments.
• Containerization: Docker.
• Cloud Infrastructure: AWS EC2/EKS, GCP Compute Engine/GKE, Azure VMs/AKS.
• IDE Backend: VS Code Server, Theia IDE.
• Networking: Secure access via VPN or specialized gateways.
• Frontend: Web-based interface for environment management (React/Vue.js).

Example Dockerfile for a Dev Environment:

# Base image with common tools
FROM ubuntu:22.04

# Install essential packages
RUN apt-get update && apt-get install -y \
    git \
    curl \
    wget \
    vim \
    zsh \
    build-essential \
    python3 \
    python3-pip \
    nodejs \
    npm \
    && rm -rf /var/lib/apt/lists/*

# Install VS Code Server
ENV SKIP_VSCE_INSTALLATION=true
RUN curl -fsSL https://code-server.dev/install.sh | sh -s -- --prefix=/usr/local --method=standalone
RUN /usr/local/bin/code-server --install-extension ms-vscode-remote.remote-ssh \
    && /usr/local/bin/code-server --install-extension ms-python.python \
    && /usr/local/bin/code-server --install-extension dbaeumer.vscode-eslint

# Configure Zsh and Oh My Zsh (optional)
RUN sh -c "$(curl -fsSL https://raw.githubusercontent.com/ohmyzsh/ohmyzsh/master/tools/install.sh)" "" --unattended
RUN echo "alias ll='ls -alF'" >> ~/.zshrc

# Set up application-specific tools (e.g., Node.js version manager)
RUN curl -o- https://raw.githubusercontent.com/nvm-sh/nvm/v0.39.7/install.sh | bash
RUN export NVM_DIR="$HOME/.nvm" && [ -s "$NVM_DIR/nvm.sh" ] && \. "$NVM_DIR/nvm.sh" && nvm install --lts && nvm use --lts

# Set working directory
WORKDIR /app

# Expose code-server port
EXPOSE 8080

# Command to run code-server
CMD ["code-server", "--auth", "none", "--disable-telemetry", "--port", "8080", "/app"]

10. Intelligent CI/CD Pipeline Optimizer

CI/CD pipelines are the backbone of modern development, but they can become slow, flaky, and expensive. This SaaS would analyze pipeline execution data to identify bottlenecks, suggest optimizations, and automate intelligent test selection or parallelization.

Core Functionality:

• Pipeline Analytics: Track execution times, failure rates, resource consumption for each stage.
• Bottleneck Identification: Pinpoint slow-running jobs or stages.
• Intelligent Test Parallelization: Automatically distribute tests across multiple workers based on historical run times.
• Test Selection/Flakiness Detection: Identify and quarantine flaky tests, or intelligently select tests to run based on code changes.
• Resource Optimization: Suggest optimal instance types or scaling configurations for CI/CD runners.
• Cost Analysis: Track CI/CD spending and identify areas for cost savings.
• Integration: Connects with GitHub Actions, GitLab CI, Jenkins, CircleCI.

Technical Stack Considerations:

• Data Ingestion: APIs of CI/CD platforms, webhooks.
• Data Storage: Time-series database (Prometheus, InfluxDB) for metrics, PostgreSQL for relational data.
• Analysis Engine: Python with libraries like Pandas, Scikit-learn for statistical analysis and machine learning.
• Orchestration: Potentially Kubernetes for managing analysis jobs.
• Frontend: React/Vue.js for dashboards and reporting.

Example GitHub Actions Workflow Snippet (Conceptual):

name: CI/CD Pipeline Analysis

on:
  push:
    branches: [ main ]
  pull_request:
    branches: [ main ]
  workflow_run: # Triggered after a job completes
    workflows: ["Build and Test"]
    types:
      - completed

jobs:
  analyze_pipeline:
    runs-on: ubuntu-latest
    if: github.event_name == 'workflow_run' && github.event.workflow_run.event == 'push' # Only analyze on push events from the main workflow

    steps:
    - name: Checkout code
      uses: actions/checkout@v4

    - name: Fetch workflow run data
      id: fetch_data
      run: |
        # In a real scenario, you'd use the GitHub API to fetch job logs and timings
        # For this example, we'll simulate data
        echo "::set-output name=run_id::${{ github.event.workflow_run.id }}"
        echo "::set-output name=run_url::${{ github.event.workflow_run.html_url }}"
        # Simulate job timings (replace with actual API calls)
        echo "job_timings_json='{\"build\": 60, \"test_unit\": 120, \"test_integration\": 300, \"deploy\": 90}'" >> $GITHUB_OUTPUT

    - name: Run Pipeline Optimizer Analysis
      env:
        GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
        WORKFLOW_RUN_ID: ${{ steps.fetch_data.outputs.run_id }}
        JOB_TIMINGS: ${{ steps.fetch_data.outputs.job_timings_json }}
      run: |
        # This script would send the data to your SaaS backend for analysis
        echo "Analyzing workflow run: $WORKFLOW_RUN_ID"
        echo "Job timings: $JOB_TIMINGS"
        # Example: curl -X POST -H "Content-Type: application/json" -d "{\"run_id\": \"$WORKFLOW_RUN_ID\", \"timings\": $JOB_TIMINGS}" https://api.your-ci-optimizer.com/analyze
        echo "Analysis complete. Suggestions will be available on the dashboard."

    # Optional: Add a job to suggest optimizations based on analysis
    # This could involve modifying test commands or runner configurations