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Clozon Technologies

Clozon Technologies helps Startups and SMB's businesses adopt and benefit from cloud computing. DevOps

About Clozon Technologies:

Clozon Technologies helps businesses adopt and benefit from cloud computing. Our focus areas are AWS (Amazon Web Services) , Google Cloud Platform , Microsoft Azure , DevOps and MS Office 365. We can help :

* If you are evaluating or planning to move to AWS , Google Cloud Platform or MS Azure

* If you need setup and support services for AWS , Azure or Google Cloud paltform , DevOps and Office 365

* If you are keen to utilize cloud computing to reduce your IT costs but not sure where to start

* If you need top quality cloud consulting at a reasonable price.

* If you want to build your cloud computing strategy and approach including ROI and TCO

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Specialities :
Consulting , Architect , Implement and Support cloud systems and services.

05/10/2026

𝒀𝒐𝒖𝒓 𝑮𝒆𝒏𝑨𝑰 𝒇𝒆𝒂𝒕𝒖𝒓𝒆 𝒉𝒂𝒔 𝒂 𝒄𝒐𝒔𝒕 𝒄𝒖𝒓𝒗𝒆 𝒏𝒐𝒃𝒐𝒅𝒚 𝒑𝒖𝒕 𝒐𝒏 𝒕𝒉𝒆 𝒓𝒐𝒂𝒅𝒎𝒂𝒑.

At 100 users, it's a rounding error on your AWS bill. At 10,000, it can become the biggest line on it, and the first time you see it is on the invoice.

Most teams do plan the AI launch. The plan just doesn't include cost per request. Here's how it usually plays out:

1. Cost is modelled per user, but GenAI cost scales per request, per token and per agent step
2. Usage grows after launch, with more requests per user, longer prompts and more retrieved context
3. Every request goes to the most expensive model, because that's the one that worked in the demo
4. All AI spend sits in one AWS line item, so nobody can say which feature or customer drives it
5. The first alert is the invoice

The fix isn't to ship less AI. It's to put guardrails on the cost curve before the feature scales: attribution, per-request cost tracking, model routing, per-tenant caps and budget alarms.

Swipe through the linkedin post slides for the full breakdown.

𝒀𝒐𝒖𝒓 𝑮𝒆𝒏𝑨𝑰 𝒇𝒆𝒂𝒕𝒖𝒓𝒆 𝒉𝒂𝒔 𝒂 𝒄𝒐𝒔𝒕 𝒄𝒖𝒓𝒗𝒆 𝒏𝒐𝒃𝒐𝒅𝒚 𝒑𝒖𝒕 𝒐𝒏 𝒕𝒉𝒆 𝒓𝒐𝒂𝒅𝒎𝒂𝒑. At 100 users, it's a rounding error on your AWS bill. At 10,000, it can become the bigg...

29/09/2026

𝐀𝐝𝐝𝐢𝐧𝐠 𝐚𝐧𝐨𝐭𝐡𝐞𝐫 𝐞𝐧𝐠𝐢𝐧𝐞𝐞𝐫 𝐭𝐨 𝐨𝐧-𝐜𝐚𝐥𝐥 𝐰𝐨𝐧’𝐭 𝐟𝐢𝐱 𝐭𝐡𝐞 𝐚𝐥𝐞𝐫𝐭 𝐭𝐡𝐚𝐭 𝐰𝐚𝐤𝐞𝐬 𝐞𝐯𝐞𝐫𝐲𝐨𝐧𝐞 𝐮𝐩.

For early-stage startups, recurring incidents quietly compete with the product roadmap.

A rotation makes response someone’s responsibility. Prevention needs an owner too.

When the same issue returns every week, it deserves engineering time in the next sprint.

Check out the post on linkedin. The carousel breaks down what repeat pages reveal and where a small team can start.

𝐀𝐝𝐝𝐢𝐧𝐠 𝐚𝐧𝐨𝐭𝐡𝐞𝐫 𝐞𝐧𝐠𝐢𝐧𝐞𝐞𝐫 𝐭𝐨 𝐨𝐧-𝐜𝐚𝐥𝐥 𝐰𝐨𝐧’𝐭 𝐟𝐢𝐱 𝐭𝐡𝐞 𝐚𝐥𝐞𝐫𝐭 𝐭𝐡𝐚𝐭 𝐰𝐚𝐤𝐞𝐬 𝐞𝐯𝐞𝐫𝐲𝐨𝐧𝐞 𝐮𝐩. For early-stage startups, recu...

25/09/2026

𝐘𝐨𝐮𝐫 𝐚𝐩𝐩𝐥𝐢𝐜𝐚𝐭𝐢𝐨𝐧 𝐜𝐨𝐝𝐞 𝐡𝐚𝐬𝐧'𝐭 𝐜𝐡𝐚𝐧𝐠𝐞𝐝. 𝐒𝐨 𝐰𝐡𝐲 𝐢𝐬 𝐲𝐨𝐮𝐫 𝐀𝐈 𝐟𝐞𝐚𝐭𝐮𝐫𝐞 𝐬𝐮𝐝𝐝𝐞𝐧𝐥𝐲 𝐩𝐫𝐨𝐝𝐮𝐜𝐢𝐧𝐠 𝐰𝐨𝐫𝐬𝐞 𝐫𝐞𝐬𝐮𝐥𝐭𝐬?

No application code changed. Yet production is behaving differently.

Your existing CI/CD pipeline may be doing its job. But who is managing the release of prompts, models, and AI configurations?

As SaaS startups move from AI experiments to production, these changes need the same engineering discipline as application code:

• Version control and traceability
• Automated evaluation before deployment
• Controlled rollout and monitoring
• Fast rollback when something goes wrong

This is where LLMOps extends your existing DevOps and CI/CD practices.

Checkout the below linkedin post to know how to bring AI components into a reliable release process without building an unnecessarily complex MLOps platform.

𝐘𝐨𝐮𝐫 𝐚𝐩𝐩𝐥𝐢𝐜𝐚𝐭𝐢𝐨𝐧 𝐜𝐨𝐝𝐞 𝐡𝐚𝐬𝐧'𝐭 𝐜𝐡𝐚𝐧𝐠𝐞𝐝. 𝐒𝐨 𝐰𝐡𝐲 𝐢𝐬 𝐲𝐨𝐮𝐫 𝐀𝐈 𝐟𝐞𝐚𝐭𝐮𝐫𝐞 𝐬𝐮𝐝𝐝𝐞𝐧𝐥𝐲 𝐩𝐫𝐨𝐝𝐮𝐜𝐢𝐧𝐠 𝐢𝐧𝐜....

22/09/2026

𝐘𝐨𝐮𝐫 𝐝𝐚𝐬𝐡𝐛𝐨𝐚𝐫𝐝𝐬 𝐚𝐫𝐞 𝐚𝐥𝐥 𝐠𝐫𝐞𝐞𝐧. 𝐘𝐨𝐮𝐫 𝐀𝐈 𝐩𝐫𝐨𝐝𝐮𝐜𝐭 𝐦𝐢𝐠𝐡𝐭 𝐬𝐭𝐢𝐥𝐥 𝐛𝐞 𝐟𝐚𝐢𝐥𝐢𝐧𝐠 𝐮𝐬𝐞𝐫𝐬.
Traditional monitoring was built to answer one question. Are the systems up and response times within range? It has nothing to say about whether the model is still giving good answers, whether a provider pushed a silent update overnight, or whether one feature just doubled your cloud bill.

Most early-stage AI startups find out the answer is "no" only after it shows up as churn.

Swipe thru the slides in the below linkedin post to find out , what usually isn't being tracked, but should be.

𝐘𝐨𝐮𝐫 𝐝𝐚𝐬𝐡𝐛𝐨𝐚𝐫𝐝𝐬 𝐚𝐫𝐞 𝐚𝐥𝐥 𝐠𝐫𝐞𝐞𝐧. 𝐘𝐨𝐮𝐫 𝐀𝐈 𝐩𝐫𝐨𝐝𝐮𝐜𝐭 𝐦𝐢𝐠𝐡𝐭 𝐬𝐭𝐢𝐥𝐥 𝐛𝐞 𝐟𝐚𝐢𝐥𝐢𝐧𝐠 𝐮𝐬𝐞𝐫𝐬. Traditional monitoring was built to a...

18/09/2026

𝐒𝐡𝐢𝐩𝐩𝐢𝐧𝐠 𝐭𝐡𝐞 𝐀𝐈 𝐟𝐞𝐚𝐭𝐮𝐫𝐞 𝐰𝐚𝐬 𝐭𝐡𝐞 𝐞𝐚𝐬𝐲 𝐩𝐚𝐫𝐭.

Most teams can get an AI feature into production in a sprint or two. Wiring it into an actual operational plan , the part that keeps it working, keeps costs sane, and catches it when it breaks, usually doesn't happen until something already has.

Here's what that gap actually looks like once the feature is live:

1. Token spend scales with usage, and nobody set a cap or an alert

2. Model answers drift over time, and nobody's tracking quality

3. Support starts getting "weird AI answer" tickets with no logs to debug them

4. A prompt or model update breaks something, and there's no rollback path

5. Something goes wrong in production, and there's no one whose job it is to catch it

None of this shows up in the demo. All of it shows up about a month after launch.

Swipe through the linkedin post to see the five things that close these gaps before they cost you.

15/09/2026

𝐄𝐯𝐞𝐫𝐲 𝐒𝐭𝐚𝐫𝐭𝐮𝐩 𝐡𝐚𝐬 "𝟗𝟗.𝟗% 𝐮𝐩𝐭𝐢𝐦𝐞" 𝐰𝐫𝐢𝐭𝐭𝐞𝐧 𝐢𝐧𝐭𝐨 𝐭𝐡𝐞𝐢𝐫 𝐭𝐞𝐫𝐦𝐬. 𝐀𝐥𝐦𝐨𝐬𝐭 𝐧𝐨𝐧𝐞 𝐡𝐚𝐬 𝐭𝐞𝐬𝐭𝐞𝐝 𝐢𝐭.

Founders write the SLA number early , usually buried in the terms of service, or promised outright in an enterprise contract to close a deal. It sounds like maturity. But an SLA isn't a claim, it's a bet that your infrastructure will behave a certain way under pressure and most early-stage Startups have never tested it or actually been asked to prove it.

The gap shows up in a few predictable places:

1. Failover exists in the architecture diagram, not in a real drill
2. On-call is not a documented runbook
3. There's is no uptime history to hand a prospect's security team when they ask
4. Nobody's mapped the single point of failure that's still sitting in the stack

None of this is visible , until an incident hits, or a deal stalls in vendor review because you can't back up the number.

Have you actually tested your failover, or are you assuming it works?

𝐄𝐯𝐞𝐫𝐲 𝐒𝐭𝐚𝐫𝐭𝐮𝐩 𝐡𝐚𝐬 "𝟗𝟗.𝟗% 𝐮𝐩𝐭𝐢𝐦𝐞" 𝐰𝐫𝐢𝐭𝐭𝐞𝐧 𝐢𝐧𝐭𝐨 𝐭𝐡𝐞𝐢𝐫 𝐭𝐞𝐫𝐦𝐬. 𝐀𝐥𝐦𝐨𝐬𝐭 𝐧𝐨𝐧𝐞 𝐡𝐚𝐬 𝐭𝐞𝐬𝐭𝐞𝐝 𝐢𝐭. Founders write t...

11/09/2026

𝐅𝐨𝐮𝐧𝐝𝐞𝐫𝐬, 𝐰𝐡𝐞𝐧'𝐬 𝐭𝐡𝐞 𝐥𝐚𝐬𝐭 𝐭𝐢𝐦𝐞 𝐲𝐨𝐮 𝐚𝐬𝐤𝐞𝐝 𝐲𝐨𝐮𝐫 𝐝𝐚𝐭𝐚 𝐭𝐞𝐚𝐦 𝐚 𝐪𝐮𝐞𝐬𝐭𝐢𝐨𝐧 𝐚𝐧𝐝 𝐠𝐨𝐭 𝐚𝐧 𝐚𝐧𝐬𝐰𝐞𝐫 𝐢𝐧𝐬𝐭𝐞𝐚𝐝 𝐨𝐟 "𝐰𝐞'𝐫𝐞 𝐬𝐭𝐢𝐥𝐥 𝐟𝐢𝐱𝐢𝐧𝐠 𝐭𝐡𝐞 𝐩𝐢𝐩𝐞𝐥𝐢𝐧𝐞"?

For most early-stage SaaS and AI startups, the honest answer is "I don't remember." A schema changes, a script breaks, a dashboard goes stale and the whole week disappears into fixing pipelines instead of the business ever getting the answers it's waiting on.

Enterprise Data engineers spend a median of 44% of their time just maintaining pipelines and not building anything new. Scale that down to a 10–100 person startup with one or two data people, and you're effectively running your data function at half capacity.

The fix isn't more headcount. It's treating pipelines like production software incorporating version control, automated tests, real observability, infrastructure as code.

Checkout the full post on LinkedIn.

𝐅𝐨𝐮𝐧𝐝𝐞𝐫𝐬, 𝐰𝐡𝐞𝐧'𝐬 𝐭𝐡𝐞 𝐥𝐚𝐬𝐭 𝐭𝐢𝐦𝐞 𝐲𝐨𝐮 𝐚𝐬𝐤𝐞𝐝 𝐲𝐨𝐮𝐫 𝐝𝐚𝐭𝐚 𝐭𝐞𝐚𝐦 𝐚 𝐪𝐮𝐞𝐬𝐭𝐢𝐨𝐧 𝐚𝐧𝐝 𝐠𝐨𝐭 𝐚𝐧 𝐚𝐧𝐬𝐰𝐞𝐫 𝐢𝐧𝐬.....

08/09/2026

𝐘𝐨𝐮𝐫 𝐀𝐏𝐈 𝐜𝐚𝐧 𝐫𝐞𝐭𝐮𝐫𝐧 𝐚 𝟐𝟎𝟎. 𝐘𝐨𝐮𝐫 𝐀𝐈 𝐟𝐞𝐚𝐭𝐮𝐫𝐞 𝐜𝐚𝐧 𝐬𝐭𝐢𝐥𝐥 𝐛𝐞 𝐜𝐨𝐦𝐩𝐥𝐞𝐭𝐞𝐥𝐲 𝐛𝐫𝐨𝐤𝐞𝐧.

Hallucinating answers. Empty responses. A model update overnight that quietly changes how your product behaves. None of that trips a standard uptime alert , because your dashboard isn't lying, it's just not looking at the right thing.

Most founders find out their AI feature is broken from a support ticket, not a monitor. By then it's been broken for hours ,maybe days and nobody knows when it started.

Traditional on-call was built for servers going down. AI features fail differently, and if your on-call setup can't tell the difference, you're always the last to know.

Click through to LinkedIn for what that setup looks like for an early stage startup.

𝐘𝐨𝐮𝐫 𝐀𝐏𝐈 𝐜𝐚𝐧 𝐫𝐞𝐭𝐮𝐫𝐧 𝐚 𝟐𝟎𝟎. 𝐘𝐨𝐮𝐫 𝐀𝐈 𝐟𝐞𝐚𝐭𝐮𝐫𝐞 𝐜𝐚𝐧 𝐬𝐭𝐢𝐥𝐥 𝐛𝐞 𝐜𝐨𝐦𝐩𝐥𝐞𝐭𝐞𝐥𝐲 𝐛𝐫𝐨𝐤𝐞𝐧. Hallucinating answers. Empty responses. A model ...

04/09/2026

𝐓𝐡𝐫𝐞𝐞 𝐀𝐈 𝐩𝐫𝐨𝐯𝐢𝐝𝐞𝐫𝐬 𝐰𝐞𝐧𝐭 𝐝𝐨𝐰𝐧 𝐲𝐞𝐬𝐭𝐞𝐫𝐝𝐚𝐲. 𝐖𝐢𝐭𝐡𝐢𝐧 𝐡𝐨𝐮𝐫𝐬 𝐨𝐟 𝐞𝐚𝐜𝐡 𝐨𝐭𝐡𝐞𝐫.

OpenAI. Claude. Grok. Same morning.

If your product has an AI feature bolted onto one of these APIs with no fallback, this is what your Thursday looked like too , except it wasn't your code, your infra, or your team. It was a vendor's routing error, and you had zero control over the timeline.

The outage itself lasts a few hours. The support queue, the "is your product broken?" messages, and the quiet churn from users who stopped trusting the feature. The impact lasts much longer.

Most SaaS and AI startups building on a single model provider don't find out they have a single point of failure until the day it goes down.

Click through to LinkedIn for what happened yesterday and what resilient AI infrastructure actually looks like for a Startups your size.

𝐓𝐡𝐫𝐞𝐞 𝐀𝐈 𝐩𝐫𝐨𝐯𝐢𝐝𝐞𝐫𝐬 𝐰𝐞𝐧𝐭 𝐝𝐨𝐰𝐧 𝐲𝐞𝐬𝐭𝐞𝐫𝐝𝐚𝐲. 𝐖𝐢𝐭𝐡𝐢𝐧 𝐡𝐨𝐮𝐫𝐬 𝐨𝐟 𝐞𝐚𝐜𝐡 𝐨𝐭𝐡𝐞𝐫. OpenAI. Claude. Grok. Same morning. If your product has an AI featu...

01/09/2026

𝐘𝐨𝐮𝐫 𝐚𝐩𝐩 𝐧𝐞𝐯𝐞𝐫 𝐜𝐫𝐚𝐬𝐡𝐞𝐝. 𝐈𝐭 𝐣𝐮𝐬𝐭 𝐠𝐨𝐭 𝐬𝐥𝐨𝐰 𝐞𝐧𝐨𝐮𝐠𝐡 𝐟𝐨𝐫 𝐩𝐞𝐨𝐩𝐥𝐞 𝐭𝐨 𝐥𝐞𝐚𝐯𝐞.

No 500 error. No downtime alert. Just a signup flow that took a beat too long, and a founder who never saw it happen.

Slow page loads don't show up in uptime dashboards. They show up in your churn numbers, weeks later, with no clear cause attached.

For early-stage SaaS teams, this is one of the most common and most invisible reasons trials don't convert.

Swipe through the linkedin post to know where the milliseconds actually go, and what a performance audit usually finds first.

Your app never crashed. It just got slow enough for people to leave. No 500 error. No downtime alert. Just a signup flow that took a beat too long, and a founder who never saw it happen. Slow page loads don't show up in uptime dashboards. They show up in your churn numbers, weeks later, with no clea...

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