Tech Stack Management

3 weeks back while I was working on a Kubernetes project, saw a very common bad practice, i.e. image size is 8 GB which could have been just 436 MB (Multi-stage Build)!!!! After working on such numerous projects, here I have made a list of best practices for containers: Use Minimal Base Images: Start with minimal base images like Alpine Linux or Distroless to reduce the attack surface and improve container startup times. Single Concern per Container: Each container should have only one responsibility. If an application has multiple components (e.g., web server, database etc), they should be split into separate containers and managed as separate services. Stateless Applications: Design your applications to be stateless as much as possible. This allows Kubernetes to easily scale, restart, or replace containers without losing data. Liveness and Readiness Probes: Use liveness probes to let Kubernetes know when to restart a container and readiness probes to know when a container is ready to start accepting traffic. Resource Limits: Set resource requests and limits for CPU and memory to ensure that the container gets its required resources and doesn't consume more than it should. Security: Run containers with a non-root user. Use network policies to control communication between pods. Regularly scan container images for vulnerabilities. Use Kubernetes RBAC (Role-Based Access Control) to limit permissions. Immutable Containers: Avoid making changes to running containers. Instead, create a new container image and deploy it. This ensures consistency across environments. Use Labels and Annotations: Use labels for organizing and selecting groups of resources. Annotations can be used to store additional metadata. Configurations and Secrets: Use ConfigMaps for non-sensitive configuration data and Secrets for sensitive data. Avoid hardcoding configurations in the container image. Logging and Monitoring: Ensure that your applications log to the standard output and standard error streams. This allows Kubernetes to handle and redirect the logs appropriately. Integrate with monitoring tools like Prometheus to keep an eye on the health and performance of your containers. Regularly Update and Patch: Regularly update your container images to include security patches and updates. Use image scanning tools to identify and fix vulnerabilities. Graceful Shutdown: Ensure that your applications handle the SIGTERM signal and shut down gracefully. This allows them to finish processing current requests and release resources before shutting down. Avoid Using latest Tag: Be explicit with container image tags. Avoid using the latest tag as it can lead to unpredictable deployments. Storage Considerations: If your application needs persistent storage, use Persistent Volumes (PV) and Persistent Volume Claims (PVC) in Kubernetes. Ensure that the storage solution you choose is compatible with the dynamic nature of containerized deployments. Follow Sandip Das for more!

You didn't build a 'modern data warehouse.' You built the world's most expensive junkyard. I recently audited a client's infrastructure with 120+ tables loading every night. -Not one person could explain what half of them were for. -Dashboards contradicted each other. -Analysts were burning hours tracing dependencies instead of building anything useful. -If few engineers left everything would crumble. On the top the cloud bill and data volume is growing. Here’s the problem no one wants to admit Your ELT isn’t broken. It’s bloated. We’ve confused “accessibility” with “intent” Tools made it easy to load everything so we did. And over time, your data warehouse turned into a junkyard. The Hidden Cost of Loading Everything Every table you load has a cost: Storage & compute. (That Snowflake bill didn’t double by accident.) Engineering time. (Maintaining pipelines no one uses.) Trust. (Conflicting numbers, different definitions, zero confidence.) Everything you add to tech stack can and most likely will become a liability faster then the asset. The result? You’re sitting on a goldmine of data but its usless Creating 1. Start with Decisions, Not Sources Every dataset should answer a business question. If you can’t tie it to a KPI, a metric, or a decision it doesn’t deserve a pipeline. Rule: “If we can’t name who uses it or what decision it drives, delete it.” 2. Audit Everything You Load Run a two-hour audit of your pipelines. Tag each as: Must-Have: Directly drives a core KPI or compliance requirement. Nice-to-Have: Useful but infrequently used. Archive: Unused in 90+ days. When we did this for a Healthech and finance client, 40% of their pipelines had no active usage. They were maitaining duplicate pipelines, cost a fortune but nobody haven't even looked at 3. Connect Every Pipeline to the P&L Nobody cares many pipelines you’ve built they care how it impacts margin. Every data initiative should tie to one of three levers: Cost reduction (cloud spend, engineering time) Revenue enablement (forecast accuracy, churn prevention) Risk reduction (audit accuracy, compliance) If it doesn’t hit one of these? It’s noise 4. Assign Ownership The most expensive part of pipeline and ETLs isn’t compute. It’s ambiguity. No one owns the data. No one knows who built it. No one knows what engineer responsible for it. Assign a data steward per domain responsible for purpose, lineage, and consumers. Accountability drives cleanup faster than any governance tool. 5. Enforce an “ROI Gate” Before Every New Ingestion Before any new source is added, ask: “What decision does this support, and what’s the expected ROI?” You’ll kill 50% of waste before it even starts. Every unnecessary dataset or tools you load burns margin, trust, time and complexity. Most likely a liability. Build lean, trusted, scalable and AI-ready data architecture. Stop loading everything. Start loading with intent.

Here are the most expensive Kubernetes mistakes (that nobody talks about). I’ve spent 12+ years in DevOps and I’ve seen K8s turn into a money pit when engineering teams don’t understand how infra decisions hit the bill. Not because the team is bad. But because Kubernetes makes it way too easy to burn cash silently. 𝐇𝐞𝐫𝐞 𝐚𝐫𝐞 𝐭𝐡𝐞 𝐫𝐞𝐚𝐥 𝐦𝐢𝐬𝐭𝐚𝐤𝐞𝐬 that don’t show up in your monitoring tools: 1. 𝐎𝐯𝐞𝐫𝐩𝐫𝐨𝐯𝐢𝐬𝐢𝐨𝐧𝐞𝐝 𝐧𝐨𝐝𝐞𝐬 "𝐣𝐮𝐬𝐭 𝐢𝐧 𝐜𝐚𝐬𝐞". Engineers love to play it safe. So they add buffer CPU and memory for traffic spikes that rarely happen. ☠️ What you get: idle nodes running 24/7, racking up your cloud bill. ✓ 𝐅𝐢𝐱: Use vertical pod autoscaling and limit ranges properly. Educate teams on real usage patterns vs. “just in case” setups. 2. 𝐏𝐞𝐫𝐬𝐢𝐬𝐭𝐞𝐧𝐭 𝐯𝐨𝐥𝐮𝐦𝐞𝐬 𝐭𝐡𝐚𝐭 𝐧𝐞𝐯𝐞𝐫 𝐝𝐢𝐞. You delete the app. But the storage stays. Forever. Cloud providers won’t remind you. They’ll just keep billing you. ✓ 𝐅𝐢𝐱: Use “reclaimPolicy: Delete” where safe. And audit your PVs like your AWS bill depends on it. Because it does. 3. 𝐋𝐨𝐠𝐠𝐢𝐧𝐠 𝐞𝐯𝐞𝐫𝐲𝐭𝐡𝐢𝐧𝐠... 𝐚𝐭 𝐞𝐯𝐞𝐫𝐲 𝐥𝐞𝐯𝐞𝐥. Verbose logging might help you debug. But writing 1TB+ of logs daily to expensive storage? That’s just bad economics. ✓ 𝐅𝐢𝐱: Route logs smartly. Don’t store what you won’t read. Consider tiered logging or low-cost storage for historical data. 4. 𝐔𝐬𝐢𝐧𝐠 𝐒𝐒𝐃𝐬 𝐰𝐡𝐞𝐫𝐞 𝐇𝐃𝐃𝐬 𝐰𝐨𝐮𝐥𝐝 𝐝𝐨. Yes, SSDs are fast. But do you really need them for staging environments or batch jobs? ✓ 𝐅𝐢𝐱: Use storage classes wisely. Match performance to actual workload needs, not just default configs. 5. 𝐈𝐠𝐧𝐨𝐫𝐢𝐧𝐠 𝐢𝐧𝐭𝐞𝐫𝐧𝐚𝐥 𝐭𝐫𝐚𝐟𝐟𝐢𝐜 𝐞𝐠𝐫𝐞𝐬𝐬. You’re not just paying for internet egress. Internal service-to-service comms can spike costs, especially in multi-zone clusters. ✓ 𝐅𝐢𝐱: Optimize service placement. Use node affinity and avoid chatty microservices spraying traffic across zones. 6. 𝐍𝐞𝐯𝐞𝐫 𝐫𝐞𝐯𝐢𝐬𝐢𝐭𝐢𝐧𝐠 𝐲𝐨𝐮𝐫 𝐚𝐮𝐭𝐨𝐬𝐜𝐚𝐥𝐞𝐫 𝐜𝐨𝐧𝐟𝐢𝐠𝐬. Initial HPA/VPA configs get set and never touched again. Meanwhile, your workloads have changed completely. ✓ 𝐅𝐢𝐱: Treat autoscaling like code. Revisit, test, and tune configs every sprint. Truth is most K8s cost overruns aren't infra problems. They're visibility problems. And cultural ones. If your engineering teams aren’t accountable for infra spend, it’s just a matter of time before you’re bleeding cash. ♻️ 𝐏𝐋𝐄𝐀𝐒𝐄 𝐑𝐄𝐏𝐎𝐒𝐓 𝐒𝐎 𝐎𝐓𝐇𝐄𝐑𝐒 𝐂𝐀𝐍 𝐋𝐄𝐀𝐑𝐍.

Most sales orgs have too many tools and not enough results. Ya'll are spending money in the wrong places. You've got a CRM. You've got a dialer. You've got an SEP. You've got conversation intelligence. You've got 14 dashboards nobody looks at. And your reps are still manually researching prospects. Still sending the same generic sequences. Still doing demos that don't convert. The stack is bloated with the WRONG things. The results are flat. Meanwhile, the modern buyer has changed. They want to self-educate. They want data. They want to move on their timeline, not yours. Or your still using what was cool 2-5 years ago because it was 'on the quadrant' If your tech stack isn't built around AI, data-driven decisions, and meeting buyers where they are — you're already behind. Tech has changed. There are some new up and comers that are just rocking right now 𝗧𝗛𝗘 𝗖𝗔𝗧𝗘𝗚𝗢𝗥𝗜𝗘𝗦 𝗧𝗛𝗔𝗧 𝗔𝗖𝗧𝗨𝗔𝗟𝗟𝗬 𝗠𝗢𝗩𝗘 𝗧𝗛𝗘 𝗡𝗘𝗘𝗗𝗟𝗘 These aren't tools I just talk about. I actually use them with my team. One of the few "influencers" left that's still in the trenches building. I get asked all the time my 'stack' so kicking the year off with some of my favorites. Here's where I'm focused for 2026: 𝗗𝗮𝘁𝗮 & 𝗘𝗻𝗿𝗶𝗰𝗵𝗺𝗲𝗻𝘁 Bad data = wasted activity. Period. If your reps are calling wrong numbers and emailing dead addresses, no amount of "more dials" fixes that. Using: Cargo, ZoomInfo, TitanX 𝗪𝗼𝗿𝗸𝗳𝗹𝗼𝘄𝘀 & 𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗼𝗻 The manual stuff that eats your team's time — follow-ups, research triggers, multi-touch sequences — needs to run without humans babysitting it. Using: Swan AI, Trigify 𝗗𝗲𝗺𝗼 𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗼𝗻 Your AEs are doing the same demo 47 times a month. Most of those demos are qualification calls in disguise. What if prospects could experience the product on their own time? 24/7. Customized to their use case. Scalable. Repeatable best practices baked in. Better qualified prospects. Shorter cycles. Higher conversion. Using: Consensus 𝗔𝗰𝗰𝗼𝘂𝗻𝘁 𝗗𝗶𝘀𝗰𝗼𝘃𝗲𝗿𝘆 "Find me companies that just raised a Series B, are hiring SDRs, and use Salesforce." That level of specificity used to take hours. Now it takes seconds. Using: Exa 𝗧𝗛𝗘 𝗕𝗜𝗚𝗚𝗘𝗥 𝗣𝗢𝗜𝗡𝗧 Stop buying tools because they're "cool" or because your competitor has them. Buy tools that solve a specific constraint in your funnel. If connect rate is killing you — fix the data. If cycle time is too long — fix the demo process. If reps are drowning in admin — fix the workflows. Constraint first. Tool second. The orgs that win in 2026 will be the ones using AI to make smarter decisions, move faster, and meet buyers where they actually are. Most orgs have it backwards. They buy the tool and then try to find a problem for it to solve. That's how you end up with 47 logins and the same results. Be intentional with your stack, ya'll.

Building your finance tech stack? Here’s a major mistake I see finance leaders make: It’s not overspending. It’s not picking the wrong vendor. It’s something far more fundamental. As monday.com’s CFO, I evaluate all large software purchases - and I’ve noticed a consistent pattern: Many teams obsess over features and discounts, but ignore the 6 factors that actually determine long-term success. Here’s what actually matters at $1B+ ARR and beyond: 👇 1. Don’t Evaluate the Tool in Isolation – But Within the Ecosystem For large purchases, you want to see the full picture - not just the tool itself. Ask: How will this new platform integrate with your existing ones? Most teams evaluate tools in isolation, but real value comes from integrations. Silos destroy efficiency and visibility. The question isn't "Is this the best tool?" but "Is this the best tool FOR OUR ECOSYSTEM?" 2. Benchmark Against Companies at Your Scale It’s a yellow flag if other enterprise organizations aren't using a tool we're considering. When evaluating NetSuite as our ERP, we did research on $500M+ ARR companies to understand their implementation challenges. When we chose Zip for procurement, we looked at companies with similar global reach. The tools that work at $100M ARR don’t necessarily work at $1B+. 3. Assess Implementation Complexity Realistically I am not a fan of solutions that require massive teams just to babysit them. User-friendly and quick internal adoption wins over heavy customization every time. Avoid tools that “promise everything” but deliver nothing for 12+ months. 4. Test for Scalability Early Most finance teams discover scalability issues after it’s too late. Ask: Can the tool scale with us from 100 to 1,000 users without breaking?  We are building our tech stack with enterprise-grade solutions because they grow with us. 5. Strategic Consolidation Beats Best-of-Breed Fewer vendors means better negotiating leverage, simpler operations, and cleaner data flows. At monday, we're ruthless about removing fragmentation that isn’t necessary. 6. Keep Your Stack Evolving with an AI-First Mindset Our finance tech stack is not static. We're constantly updating with a focus on AI capabilities. I suggest you do the same. Every finance leader should reevaluate their tech stack through an AI lens today. *** In summary: The most expensive procurement mistake isn’t overpaying. It’s buying tools that:  1. Can’t grow with you 2. Create siloed data environments 3. Lack AI capabilities in this new AI-first era This mindset has helped us scale efficiently - and avoid million-dollar mistakes.   What’s one finance tool you regret, or swear by? Drop it below👇

Buying Clay won’t get you more leads. Buying Gong won’t make your sales team better on calls. Just like: Buying a set of Wüsthofs won’t make you a better chef. Buying that new Titleist driver? Yeah… it’s not going to magically straighten your slice. Too often we buy tools hoping they’ll solve our problems. But tools don’t solve problems. Processes do. And the best Revenue and Rev Ops leaders I know all follow a playbook when it comes to tooling: 1. Start with the problem, not the tool You need a list—not of tools you want to try, but of business problems you need to solve. Some common ones I hear: "We need to improve our pipeline conversion rate" "We need better forecasting data" "We need to stay in closer touch with customers post-sale" Then you can go hunting for tools that solve those problems. But if you’re just chasing every shiny new AI-powered tool? You’re going to waste time, budget, and team attention. Trust me, the 100th AI SDR tool still sounds pretty cool but it might not be what you need for your business at the current time. 2. Use a structured, data-driven evaluation process “I can see us using this” is not a business case. You need a scorecard. How easy is it to implement? How hard will it be to drive adoption? What’s the expected ROI? Does it integrate with our current workflow and tech stack? The best teams run their tooling like procurement pros. Gut feel isn’t enough, especially when budgets are tight and the stakes are high. 3. No process = no payoff Let’s say you buy the tool. Now what? Without enablement, accountability, and integration into daily workflows, that tool is going to sit on the shelf (just like that $500 driver in your garage). At minimum, you need: -Training plans -Change management -Clear documentation -Leadership support -An incentive or consequence to drive usage If you don’t have a process to make the tool work, you’ve bought shelfware. 4. Continuously re-evaluate your stack We’re in an era where AI is creating entirely new categories almost overnight. Point solutions are becoming features. New platforms are emerging weekly. And you can’t afford to run the same stack just because it worked last year. Great revenue leaders are constantly pruning and optimizing, aligning tools with the evolving needs of the team and the business. The bottom line is software doesn’t make you better. Process does. So before you pull the trigger on the next tool, ask yourself: “Do we have the infrastructure, alignment, and plan to make this successful?” Because trust me, your new Titleist is still going to slice 20 yards right unless you’ve put in the reps (or booked some lessons).

If you’re working with Kubernetes, here are 6 scaling strategies you should know — and when to use each one. Before we start — why should you care about scaling strategies? Because when Kubernetes apps face unpredictable demand, you need scaling mechanisms in place to keep them running smoothly and cost-effectively. Here are 6 strategies worth knowing: 1. Human Scaling ↳ Manually adjust pod counts using kubectl scale. ↳ Direct but not automated. When to use ~ For debugging, testing, or small workloads where automation isn’t worth it. 2. Horizontal Pod Autoscaling (HPA) ↳ Changes pod count based on CPU/memory usage. ↳ Adds/removes pods as workload fluctuates. When to use ~ For stateless apps with variable load (e.g., web apps, APIs). 3. Vertical Pod Autoscaling (VPA) ↳ Adjusts CPU/memory requests for existing pods. ↳ Ensures each pod gets the right resources. When to use ~ For steady workloads where pod count is fixed, but resource needs vary. 4. Cluster Autoscaling ↳ Adds/removes nodes based on pending pods. ↳ Ensures pods always have capacity to run. When to use ~ For dynamic environments where pod scheduling fails due to lack of nodes. 5. Custom Metrics Based Scaling ↳ Scale pods using application-specific metrics (e.g., queue length, request latency). ↳ Goes beyond CPU/memory. When to use ~ For workloads with unique performance signals not tied to infrastructure metrics. 6. Predictive Scaling ↳ Uses ML/forecasting to scale in advance of demand. ↳ Tries to prevent traffic spikes before they happen. When to use ~ For workloads with predictable traffic patterns (e.g., sales events, daily peaks). Now know this — scaling isn’t one-size-fits-all. The best teams often combine multiple strategies (for example, HPA + Cluster Autoscaling) for resilience and cost efficiency. What did I miss? • • • If you found this useful.. 🔔 Follow me (Vishakha) for more Cloud & DevOps insights ♻️ Share so others can learn as well

If I were launching a B2B SaaS tomorrow, this is the exact stack I'd use until $30M ARR. After testing 200+ GTM tools and overseeing dozens of GTM stacks, I've got a clear perspective on what drives results and what creates unnecessary complexity. The reality is that success isn't determined by a single tool. It's about creating the right combination of tools, aligning them to your processes, and identifying hidden gaps or redundancies within the stack. That's often where the greatest opportunities exist to lower costs, improve efficiency, and drive better outcomes. Here's the tech stack I'd go with for a PLG B2B SaaS. 1️⃣ CRM → HubSpot Would be my operational centre. I'd start with Sales Hub (90% off in year 1), add Marketing Hub around $1M ARR, and Data Hub later. 2️⃣ Database → Apollo.io Would be my go-to database for sourcing companies and contacts, as it's currently the best combination of data quality, coverage, and pricing. 3️⃣ Email Outreach → Instantly.ai For volume, micro and signal-based email outbound campaigns 4️⃣ LinkedIn Outreach → HeyReach For Tier 1 accounts. 5️⃣ Attribution → Fibbler I'd introduce LinkedIn Ads after PMF, so attribution becomes important. 6️⃣ Data Enrichment → Clay Would use it from day 1 for enrichment, qualification, signals and outbound workflows. 7️⃣ Customer Support → Fin Ai I'd automate as much support as possible from the start. 8️⃣ Payments → Stripe Simple, reliable, and trustworthy. 9️⃣ Website Deanonymisation → Warmly, Τo understand my traffic and also retarget them through outbound. After 1k website visitors. 🔟 Product Analytics → Amplitude After the first 200-300 signups. 1️⃣1️⃣ Product Data Sync → Polytomic To sync usage data directly into HubSpot. 1️⃣2️⃣ Design → Figma For landing pages, ads, decks. 1️⃣3️⃣ Newsletter → beehiiv To nurture my audience. Wouldn't be my first priority. 1️⃣4️⃣ SEO → Semrush After PMF. 1️⃣5️⃣ Workflow Automation → n8n To connect the entire stack and automate repetitive workflows. 1️⃣6️⃣ Partnerships → PartnerStack After PMF. 1️⃣7️⃣ Content & Research → Claude Code Research, content, workflows, analysis. If I was running a Sales-Led motion, I'd also add: • Scheduling & Routing → Chili Piper (after I have 20 people on my sales team, until then, I'd just use Cal.com and HubSpot routing) • Cold Calling → Nooks • Proposals → Qwilr • Conversation Intelligence → Ergo Great companies aren't built by collecting software. They're built by combining the right systems, data, and execution at the right time. Follow Marios Charalampous for weekly GTM tips and insights

When your CRM becomes the linchpin of your entire tech stack, it’s like building a Jenga tower on a single block—it’s only a matter of time before it all comes tumbling down.  Ever had that moment of dread when one CRM update sends ripples through your entire tech stack, causing chaos in Marketing, Sales, and Support? 🫠 The problem lies in over-reliance on a single tool to manage every aspect, turning minor issues into major disruptions. The negative impact of CRM over reliance is clear: ❌ Major Data Silo: Information is trapped within the CRM, making cross-functional collaboration a nightmare. ❌ Scalability Issues: As your business grows, so does the tech debt, making future updates & integrations more complex and costly. So, what’s the solution?  ⚙️ Architect a Distributed Tech Ecosystem: Design your tech stack with specialized tools for different functions. Your CRM should be one of many interconnected tools, not the central hub for everything. Understand that your CRM isn’t a data warehouse or a CDP, so dont architect your system to treat it as such. ⚙️ Implement Data Flow Strategies: Integrate a customer data platform (CDP) to establish a single, unified customer view, and/or use a reverse ETL tool like Hightouch with a data warehouse to distribute that single source of truth data across your tech stack. This ensures your data is not only organized but also activated in a way that supports GTM Strategies. ⚙️ Focus on System Orchestration: Build your tech stack with integration platforms (like Workato, Tray, Cargo, Zapier, Make) to help ensure data flow and interoperability between systems, reducing friction and enhancing efficiency. ⚙️ Design for Modularity and Scalability: Choose scalable, modular solutions for business functions that can evolve as your organization grows, ensuring that your tech stack remains agile and adaptable & you arent over engineering your crm to do things it was never meant to do.  Don’t let your CRM tower wobble—build a tech stack that stands strong! 💪 #RevOps #TechStack #CRM #BusinessGrowth #Integration #Efficiency #Scalability #DigitalTransformation

I spent $50K+ testing agency tools so you don't have to. Here's how I cut that in half while getting better results. The biggest mistake? Buying tools before defining what problem you're solving. Most founders copy their competitors' stacks, then wonder why their $3K/month in tools barely moves the needle. With the right framework, you can avoid that trap: 1. Define your stack pillars Stage fit: Is this stack pillar relevant at your company's current size? If you're a startup doing $10K/mo, you don't need the same CRM as an enterprise doing $5M/mo. Context matters more than features. 2. Define stack goals Get clear on what you're optimizing for: Scalability: Can it handle 10x more leads/clients without breaking? Insight/Reporting: Does it surface the right data to make better decisions? Usability: Is it easy for the actual end users (not just ops) to adopt? 3. Pick who's shipping fastest If two tools look the same now, pick the one releasing updates consistently. That's the team that'll keep future-proofing your stack. Clay ships new features constantly. That's why it's our operating system. 4. Ask the 22 questions These force you to buy based on need (full list in the infographic). My favorites: What problem am I actually solving? Can my current stack do this? What's the cost per successful outcome? Can I test on one client first? 5. Create evaluation criteria Real examples from our stack: Data quality: Apollo has 30% accuracy. Prospeo.io + LeadMagic combined = 70% finding rate for half the cost. 6. Pick your execution framework Must-Have vs Nice-to-Have. Stack Audit. Decision Checklist. Test-Before-Commit. (Full breakdown in the infographic) The difference between a $50K/mo agency and a $500K/mo agency isn't just more clients. It's having a stack that compounds efficiency instead of creating chaos. Want to stop wasting money on tools that don't move the needle? I built a free 7-day email course that walks you through our exact stack selection framework + the tools we actually use to run a lean, profitable agency. Comment "TOOLS" and I'll send it your way. Helpful? Repost ♻️ to help others avoid tool chaos.