Opentofu: The New Era of Infrastructure as Code (IaC)

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In an era where cloud technology and application development are rapidly evolving, managing infrastructure has become more important than ever. Opentofu is one of the tools designed to address this challenge efficiently.  What is Opentofu?  Opentofu is an open-source project created to elevate the concept of Infrastructure as Code (IaC). It focuses on helping developers manage infrastructure easily by writing code, replacing manual configurations or complex tools.  Difference from Other IaC Tools  While other IaC tools, like Terraform, are already widely popular, Opentofu stands out due to its simpler usage and ability to scale without relying heavily on third-party tools or modules. This makes managing infrastructure across various cloud platforms more efficient.  Key Features of Opentofu  Open Source  Easy to Use  Supports Multiple Cloud Providers  Scalable : It can easily scale to meet project requirements without complicated setups.  Limitations Feature Expansion : Since Opentofu is open source, new features take time to be developed and added.  Why Choose Opentofu?  In today’s world, using IaC to manage infrastructure is not new, but Opentofu excels in flexibility and adaptability. It especially stands out in simplifying infrastructure setup and management across different environments.  Installation Instructions Opentofu offers various installation methods. You can choose the installation method that best suits your operating system through the following link:  https://opentofu.org/docs/intro/install/  Working With Opentofu The workflow in Opentofu consists of just three simple steps:  Write Plan Apply Conclusion  Opentofu is a tool that fits the needs of the modern era, focusing on managing infrastructure with code. Not only does it simplify infrastructure management, but it also increases flexibility and efficiency for developers and DevOps teams.  Start using Opentofu today to streamline your infrastructure management and experience a new way of working with IaC! References:  https://opentofu.org/docs/intro/install/  https://opentofu.org/docs/intro/core-workflow/  Looking for a DevOps solution that automates your workflow and reduces business costs? SCB TechX helps you modernize your delivery pipeline and bring high-quality products to market faster, building a foundation for long-term growth. For service inquiries, please contact us at https://bit.ly/4etA8YmLearn more: https://bit.ly/3H7W9zm

Cost-Saving Strategies for LLM APIs in Production

Cost-Saving Strategies for LLM APIs in Production

In the rapidly evolving world of AI, Large Language Models (LLMs) have become the beating heart of many applications. But the cost of calling these models is a major challenge for companies that want to use them. You might encounter situations where LLM expenses shoot up to $5,000 in just a few days—or even a few hours. Sometimes this happens because two agents start talking to each other and get stuck in an infinite loop. Cost management is therefore critical to keep AI deployments sustainable and scalable.This article explores strategies and tools to help reduce LLM costs effectively. 1. Choose the right model for the jobThe price differences across LLMs come from several factors—especially the number of parameters, which roughly correlates with capability and compute demands. The more parameters, the more complex and costly the model is to run. If your goal is to control spend, it’s essential to understand the price-performance trade-offs of each model. A clear example: GPT-5 is up to 25 timesmore expensive than GPT-4o mini for input tokens alone. On the flip side, open-source models like Mistral 7B don’t charge per API call, but self-hosting introduces hidden costs such as hardware and maintenance. LLM price comparison (per 1M tokens), as of September 8, 2025 LLM Router & Model Cascading:Instead of sending every request to the most expensive model, use a cheaper model to estimate task complexity first. For simple tasks, route to lower-cost models like GPT-4o mini or Mistral; escalate to GPT-5 only for complex or high-accuracy needs. This “cascading” approach can start with simple rules (e.g., if the question includes “calculate” or “in-depth analysis,” route to a stronger model) or use a lightweight model to score complexity and decide whether to escalate. 2. Reduce input volumeBecause you pay per token sent, shrinking inputs is one of the most effective levers. Token compression with LLM Lingua:Open-source tools like LLM Lingua can compress prompts by up to ~20 times by removing redundancy while preserving meaning—lowering the volume that expensive models must process. Send less by default (lazy-loading):Don’t pass an entire email or document if only a snippet is needed. Send subject lines or short excerpts first; let the LLM request more only if needed. This “lazy-loading” pattern ensures you pay only for genuinely necessary context. Summarization & chunking:Use a cheaper LLM to summarize large inputs before handing them to a stronger model for the core task. Proper chunking (splitting content into well-scoped parts) preserves context without forcing the model to read entire documents. 3. System-level alternatives & strategies Use non-LLM tools where possible:For straightforward tasks (e.g., finding an “unsubscribe” link), simple code or regex is far cheaper than calling an LLM. Caching:Store frequent Q&A pairs. For similar queries later, return cached answers instantly—saving both time and money. Self-hosting or user-hosted LLMs (Web LLM):In some cases, running models yourself—or in the user’s browser—reduces API costs and improves privacy. Weigh this against ongoing expenses: hardware, maintenance, and electricity. Web LLMs can download multi-GB models into the browser and run them locally without sending data to a server. Agent memory management:Agent apps often feed the entire conversation history back into the model each turn. Adopt Conversation Summary Memory (summarize older content) or Summary Buffer Memory (keep recent details, summarize the rest) to keep contexts tight. 4. Monitoring & guardrailsUnderstanding where your LLM spending comes from is essential. Track cost per user and per action:OpenAI’s dashboard shows overall spend, but you’ll need finer-grained telemetry to see which users, features, or workflows drive cost. Use observability tools:Build your own with platforms like Tinybird, or adopt purpose-built tools such as Langfuse and Helicone. Capture fields like: User ID, timestamp, input/output tokens, cost, model, and action label. This visibility helps pinpoint waste. Set usage limits:Configure API usage caps to prevent runaway costs (e.g., infinite agent loops) from snowballing. Reducing LLM costs isn’t purely a technical problem—it also requires sound process design and product thinking. By picking the right models, trimming inputs, leaning on cheaper alternatives where appropriate, and rigorously monitoring usage, you can build high-performing AI applications while keeping spend sustainable.   Follow SCB TechX for the latest technology insights and stay ahead with strategies that give your business a competitive edge in the digital era. Facebook: https://www.facebook.com/scbtechx inkedIn: https://www.linkedin.com/company/scb-tech-x/?viewAsMember=true Price reference: OpenAI Pricing: https://platform.openai.com/docs/pricing

Jenkins & GitHub Actions: The Secret Duo for Smarter DevOps

Jenkins and GitHub Actions

Jenkins has long been the superstar of automation. But when it carries all the weight on its own, things can get a little overwhelming. Wouldn’t it be great if Jenkins had a reliable partner to share the workload? That’s where GitHub Actions steps in. Today, we invited Khun Putter, Platform Services Engineer to share how GitHub Actions can complement Jenkins and make DevOps smarter, faster, and lighter. Jenkins is one of the most popular tools for automation. However, relying on it to handle everything can create unnecessary strain. GitHub Actions offers a great way to offload some of these tasks, especially those directly tied to GitHub: Pull Request Automation Run lint and basic unit tests Check code style and perform security scans Auto-label and assign reviewers Release Automation Generate release notes automatically Bump versions and create tags Publish packages to npm, PyPI, or Docker Hub Repository Management Delete merged branches Auto-sync forks Schedule jobs (e.g., cleanup, dependency updates) Example of How GitHub Actions Can Be Used By letting GitHub Actions handle tasks like linting, unit testing, code scanning, and security scanning, Jenkins no longer has to shoulder everything. This not only eases Jenkins’ workload but also gives development teams faster feedback. Advantages Faster feedback for developers Reduced development time Lower cost, GitHub Actions requires no extra servers or maintenance fees Limitations Less suitable for highly complex workloads Limited flexibility for managing secrets and security GitHub Actions reduces the workload of Jenkins by handling simpler tasks, like code testing and release creation, allowing Jenkins to focus on more complex tasks. It also helps teams receive feedback faster and reduces system maintenance costs. Using both tools together in a Hybrid Approach will maximize efficiency.Looking for a DevOps solution that automates your workflow and reduces business costs? SCB TechX helps you modernize your delivery pipeline and bring high-quality products to market faster, building a foundation for long-term growth. For service inquiries, please contact us at https://bit.ly/4etA8YmLearn more: https://bit.ly/3H7W9zm

Airflow 3 is Available!!! Why We Should Upgrade and What’s New 

Apache Airflow 3 has officially reached General Availability (GA) – and it’s ready for production use!

Multi-Cloud and Hybrid Cloud Strategies

Have you ever wondered why so many people are turning to the Cloud these days? It’s because it helps us work faster and more conveniently, plus we don’t have to worry about managing servers ourselves anymore.

Vibe Coding: When DevOps Codes in “Human Language”

Have you ever found yourself staring at endless Groovy scripts, Terraform configurations, or Ansible playbooks—thousands of lines of complex code—just to manage your CI/CD pipelines and provision infrastructure on AWS or Azure? These tasks can be intellectually stimulating, but they are also time-consuming and energy-draining.

AI-Powered DevOps: Claude Code — A Teammate That Never Complains

Anyone working in DevOps knows our jobs are full of endless tasks — from complex, brain-intensive work to small, repetitive chores that eat up time but are still necessary. Things like deploying applications, analyzing logs, investigating issues, or monitoring systems.
But today, AI-Powered DevOps is making our working lives so much easier.

Work Smarter and Faster with Jira Automation

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Did you know that Jira can also be automated to save time and improve efficiency? By using Jira API together with Webhook, you can cut down on manual work and speed things up significantly…

Speed Up with Low-Code & No-Code DevOps – Shared by a Big Sister Who’s Been There

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Everything changed when I learned about Low-Code and No-Code DevOps. It was like having an assistant that makes the difficult tasks much easier and more accessible.

Is Your Kubernetes Cluster Truly Elastic? A Look into KEDA’s Proactive Power

TechX DevOps Platform KEDA Journey

This article describes KEDA, a Kubernetes add-on that scales applications based on actual external workloads, rather than traditional CPU/Memory metrics. It highlights its key features of Proactive Scaling and Scale to Zero to enhance efficiency and optimize costs…

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