DEV Community: Rahul singh Shekhawat

Why LLM-as-a-Judge Fails on Code (And How We Built a 4-Layer Hybrid Engine)

Rahul singh Shekhawat — Fri, 21 Aug 2026 11:58:15 +0000

Why Single-LLM Evaluators Produce 80%+ False Alarms on Generated Code — and How a Hybrid Engine Fixes It

Over the past two years, the AI industry converged on a single standard for evaluating generative outputs: LLM-as-a-Judge.

The pitch was simple: instead of writing brittle regex rules or cosine-similarity heuristics, prompt GPT-4 to read the model's output and score its accuracy on a scale of 1 to 5.

In practice, when engineering teams deploy LLM judges to monitor production workloads—especially Code Generation and Retrieval-Augmented Generation (RAG)—the entire paradigm can collapse.

Teams routinely encounter 80%+ false positive rates, multi-second latencies, runaway token bills, and silent prompt-injection vulnerabilities.

In this deep dive, we break down why single-model evaluators fail, explore the Implementation Detail Paradox, and walk through how we architected Observyze's 4-Layer Hybrid Hallucination Engine to achieve <100ms latency, 10x lower cost, and 95%+ precision.

1. The "Implementation Detail Paradox" in Code Evaluation

Traditional evaluation frameworks often treat all tasks as RAG tasks: they check whether the output is strictly entailed by the input prompt.

If a claim or variable in the output is not present in the input, the evaluator may mark it as an unsupported fabrication.

Consider a simple developer request:

User Prompt

Write a Python function to fetch user data from a JSON endpoint and calculate average order value.

Generated Output

import httpx
import statistics

def calculate_aov(api_url: str) -> float:
    response = httpx.get(api_url, timeout=10.0)
    response.raise_for_status()
    orders = response.json().get("orders", [])
    if not orders:
        return 0.0
    return statistics.mean(order["total"] for order in orders)

A naive LLM judge looks at the prompt and notices:

The user never mentioned the httpx library.
The user never mentioned the statistics.mean function.
The user never specified a 10.0-second timeout parameter.
The user never explicitly specified the orders structure.

Because these details were not in the prompt, the evaluator may flag them as "Hallucinated / Unsupported Facts", assigning a failing hallucination score of 0.85+.

In reality, the code can be syntactically valid, idiomatic, and perfectly reasonable.

🚨 The Core Rule of Code Evaluation

In code generation, introducing valid implementation details that are not present in the prompt is NOT a hallucination—it is the entire point of programming.

A useful evaluator therefore needs to distinguish between:

Valid implementation details
Invalid dependencies
Fabricated APIs
Nonexistent packages
Syntax errors
Unsupported factual claims
Genuine hallucinations

This is where a single generic LLM judge starts to break down.

2. The 4-Layer Hybrid Engine Architecture

To solve this fundamental problem, Observyze replaces the single-model approach with a multi-tiered pipeline that separates deterministic static analysis, specialized cross-encoder natural language inference, live evidence grounding, and multi-model consensus.

┌────────────────────────────────────────────────────────────────────────┐
│                   Incoming LLM Request & Output                        │
└───────────────────────────────────┬────────────────────────────────────┘
                                    │
          ┌─────────────────────────┴─────────────────────────┐
          ▼                                                   ▼
  [Task: Code Generation]                             [Task: RAG / Search]
          │                                                   │
  ┌───────────────────────────────┐                   ┌───────────────────────────────┐
  │ Layer 1: Deterministic Static │                   │ Layer 2: DeBERTa-v3 NLI       │
  │ AST Parsing & PyPI Validator  │                   │ Fast Local Cross-Encoder      │
  │ Latency: <5ms | Cost: $0.00   │                   │ Latency: <60ms | Cost: $0.00  │
  └───────────────┬───────────────┘                   └───────────────┬───────────────┘
                  │                                                   │
      ┌───────────┴───────────┐                           ┌───────────┴───────────┐
      ▼                       ▼                           ▼                       ▼
 [Valid Code]        [Syntax/Import Err]         [Clean/Entailed]      [Contradicted/Thin]
  Score: 0.0          Escalate to Judge           Score: 0.0                  │
                                                                              ▼
                                                              ┌───────────────────────────────┐
                                                              │ Layer 3: Live Grounding Web   │
                                                              │ Crawler (SSRF-Guarded)        │
                                                              └───────────────┬───────────────┘
                                                                              │
                                                                              ▼
                                                              ┌───────────────────────────────┐
                                                              │ Layer 4: Multi-Model Consensus│
                                                              │ (GPT-4o, Claude 3.5, Gemini)  │
                                                              └───────────────────────────────┘

The core idea is simple:

Use the cheapest and most deterministic mechanism possible before escalating to an expensive LLM judge.

3. Layer 1: Deterministic Code Validation

Zero-LLM Fast Path

Before invoking any expensive LLM, Observyze passes Python code blocks through a local deterministic validator.

Syntax Verification

Code is parsed using Python's ast.parse().

Syntax errors can be caught in microseconds without relying on an LLM to interpret whether the code is syntactically valid.

Import Resolution

Imported package names are cross-referenced against:

Python's built-in sys.stdlib_module_names
A curated registry of verified PyPI packages

Fabricated Package Detection

If a model generates a nonexistent package such as:

import ai_super_db_v3

the validator can flag it immediately as a genuine code hallucination.

This is fundamentally different from saying:

"The user didn't mention httpx, therefore httpx is hallucinated."

The system is instead checking whether the implementation contains an objectively invalid dependency.

Fast Path

When code passes deterministic validation, it can receive a clean score without spending LLM tokens.

Latency: <5ms
Cost: $0.00

The result is a faster and more predictable path for a large class of generated-code evaluations.

4. Layer 2: Local Cross-Encoder NLI

For RAG and prose outputs, Observyze atomizes the text into discrete factual claims and evaluates each claim against the available grounding context using a DeBERTa-v3 NLI cross-encoder.

Unlike a simple binary pass/fail evaluator, Observyze implements a 3-class mathematical verdict.

1. Entailed — Supported

The claim is directly confirmed by the grounding documents.

Penalty: 0.0

2. Contradicted — Lie

The output directly contradicts the source documents.

Penalty: 1.0

This is treated as a true hallucination.

3. Neutral — Missing Context

The source does not contain enough information to determine whether the claim is true or false.

Penalty: 0.5

This is flagged for review rather than automatically treated as a lie.

Why the distinction matters

Missing evidence is not the same thing as a false statement.

An evaluator that treats every unsupported statement as a hallucination can systematically create false alarms.

5. Layer 3: Live Evidence Grounding

Sometimes grounding information contains redirect references rather than the actual source content.

Examples include:

Google Search grounding redirect tokens
Perplexity citations
External documentation links
Redirect URLs

Observyze's SSRF-guarded crawler can resolve and retrieve the underlying pages in real time.

The retrieved content is then used as additional grounding evidence for the evaluation pipeline.

This gives the evaluator an opportunity to verify claims against the underlying source instead of relying only on the initial trace context.

6. Layer 4: Multi-Model Consensus

When claims remain ambiguous or a high-stakes contradiction is detected, Observyze escalates the trace to a multi-model consensus panel.

The architecture can use up to three frontier models:

Claude 3.5 Sonnet
GPT-4o
Gemini 1.5 Pro

Instead of trusting a single judge, the engine compares their evaluations.

The system can then calculate:

Model agreement
Variance
Confidence
Final verdict
Highlighted evidence references

This creates a final reasoning layer for cases where deterministic analysis and local NLI are not sufficient.

7. Why Hybrid Evaluation Works Better

The principle behind the architecture is straightforward:

Don't ask an LLM to solve a problem that deterministic software can solve more reliably.

For example:

Problem	Evaluation mechanism
Python syntax	AST parser
Package validity	Import/package validation
Claim vs. evidence	NLI
Missing evidence	NLI + evidence retrieval
External source verification	Grounding crawler
Ambiguous or high-stakes reasoning	Multi-model consensus

Instead of sending every trace to a large language model, the system routes each task to the most appropriate evaluation layer.

8. Benchmark Comparison

Metric	Raw GPT-4o Evaluator	Observyze Hybrid Engine
Average Latency	2,850ms	<85ms Fast Path / ~450ms Consensus
Cost per 10k Traces	$150–$250	$4.20
Fast-Path Evaluation	0%	95%+
False Positive Rate — Code	82.4%	<1.2%
Prompt Injection Defense	Vulnerable to adversarial system overrides	Isolated sandbox contract & PII redaction

The major architectural advantage is that most traces can potentially be resolved through fast, deterministic or local evaluation before requiring an expensive frontier-model call.

9. Integrate Observyze Hallucination Detection in 3 Lines of Code

You can enable real-time hybrid evaluation without rewriting your application logic.

Python

from observyze import Observyze, observe

obs = Observyze(api_key="ob_live_...")

@observe(eval_hallucination=True, task_type="code")
def generate_code_pipeline(prompt: str) -> str:
    response = client.chat.completions.create(
        model="gpt-4o",
        messages=[{"role": "user", "content": prompt}]
    )
    return response.choices[0].message.content

Once enabled, each trace can be automatically evaluated through the hybrid hallucination pipeline based on its task type.

10. From Detection to Automated Action

Detection is only useful when the system can act on the result.

Observyze can connect evaluation results to operational workflows such as:

Slack alerts
Webhooks
Circuit breakers
Monitoring workflows
Application-level policies

This enables teams to move from:

Detect hallucination
        ↓
Evaluate confidence
        ↓
Trigger policy
        ↓
Alert or intervene

For example, a high-confidence hallucination can trigger an operational workflow before the problematic response reaches a production user.

11. The Bigger Picture

The problem with LLM-as-a-Judge isn't that LLMs are useless evaluators.

The problem is using an LLM as the only evaluator.

A production evaluation system needs to combine:

Deterministic checks
        +
Specialized ML models
        +
Evidence retrieval
        +
LLM reasoning
        +
Operational safeguards

The result is an evaluation architecture that can be:

Faster on common paths
Cheaper at scale
More explainable
More resistant to naive false positives
Better suited to different task types
Capable of escalating difficult cases

Conclusion

LLM evaluation shouldn't be a choice between:

"Use an LLM"

and:

"Don't use an LLM."

The better question is:

"Where does an LLM actually add value?"

For code generation, deterministic analysis should handle deterministic problems.

For factual grounding, specialized NLI can provide a fast first layer.

For missing evidence, retrieval can provide additional context.

For ambiguous or high-stakes cases, frontier models can provide deeper reasoning.

That's the philosophy behind Observyze's 4-Layer Hybrid Hallucination Engine:

Use deterministic validation where possible, specialized models where appropriate, and expensive LLM reasoning only when it is actually needed.

The goal isn't simply to detect more hallucinations.

It's to build an evaluation system that engineering teams can actually trust, understand, and operate in production.

Keeping 100% of LLM trace data in your VPC (GDPR / HIPAA Compliant Observability)

Rahul singh Shekhawat — Tue, 21 Jul 2026 08:13:13 +0000

Most LLM monitoring tools use an API proxy—meaning every raw user prompt and system prompt goes through their servers. For banking, healthcare, and enterprise startups, this is a security blocker.

We solved this by building a two-layer privacy system.

Direct Routing Bypass (enableProxyRedirect: false) Bypasses the SaaS proxy gateway completely. Traffic goes straight from your server to OpenAI/Anthropic.
Local SDK-side PII Scrubbing (enablePiiRedaction: true) Evaluates the prompt in-memory on your VPC and redacts emails, SSNs, credit cards, and API keys before the telemetry log is sent asynchronously.

Code snippet to set it up:

typescript
import { Observyze } from '@observyze/sdk';
const obs = new Observyze({
apiKey: process.env.OBSERVYZE_API_KEY,
enableProxyRedirect: false, // direct model calls
enablePiiRedaction: true, // local VPC sanitization
});
Read the complete architectural overview: https://observyze.com/blog/zero-data-leak-compliance-mode

Why AI Agents Are Hard to Debug (And What We’re Missing)

Rahul singh Shekhawat — Tue, 24 Mar 2026 05:52:45 +0000

AI agents are getting powerful.

We can now build systems that:

Call APIs
Use tools
Chain multiple LLM steps
Make decisions autonomously

But there’s one problem I keep running into:

When something goes wrong, it’s incredibly hard to understand why.

The Illusion of Observability

Today, we have tools that provide:

Logs
Traces
Token usage
Cost tracking

These are useful.

But in practice, they answer only one question:

👉 “What happened?”

Not:

👉 “Why did it happen?”

A Simple Example

Imagine an AI agent:

Takes user input
Calls an API
Processes the response
Generates final output

Now suppose the final answer is wrong.

Where did it fail?

Was the prompt incorrect?
Did the tool return unexpected data?
Did the model misinterpret context?
Did a previous step introduce noise?

Most of the time, you’re left manually digging through logs.

The Real Problem: Debugging, Not Logging

We don’t just need better logs.

We need:

Step-by-step replay of workflows
Visibility into intermediate decisions
Clear identification of failure points
Understanding of how context evolves

In short:

We need debugging tools for AI systems, not just observability tools.

What’s Missing Today

From my experience, current workflows rely heavily on:

Manual inspection
Trial and error
Adding more logging
Using evals to detect issues

But even then:

👉 You still don’t get a clear answer to why something failed.

A Different Way to Think About It

Instead of asking:

“How do we log more?”

We should ask:

“How do we make AI systems debuggable?”

That means:

Replaying executions like a timeline
Highlighting where things diverged
Understanding cause → effect relationships
Reducing guesswork

Where I’m Heading

I’ve been exploring this space and working on something focused on:

Debugging multi-step AI workflows
Understanding root causes of failures
Improving trust in AI systems

Still early, but the goal is simple:

Help developers understand why their AI behaves the way it does.

Open Questions

If you’re working with AI:

How do you debug failures today?
Do you feel current tools are enough?
What’s the most frustrating part of working with AI systems?

Would love to hear your thoughts.

If you're interested in AI systems, cloud architecture, or the projects I'm building, you can explore more of my work here:

🌐 Portfolio: https://rahulshekhawat.dev

I'm always happy to connect with developers, founders, and anyone building in the AI space.

Why your AWS bill is a "Crime Scene" (and how I built an AI to prove it)

Rahul singh Shekhawat — Tue, 10 Mar 2026 18:32:39 +0000

I spent the weekend building a fun tool that uses AI to analyze cloud spending patterns.

In this post, I break down the 3 most common 'zombie' resources I found while testing:

Idle NAT Gateways
Unattached Managed Disks
Over-provisioned RDS instances
If you want to see where you stand, I put the tool live at: https://subtrackhub.com/roast

It's free, no signup, and it's brutally honest.

How We Built SubTrackHub: Solving the $40B Cloud Waste Problem 🚀

Rahul singh Shekhawat — Sun, 15 Feb 2026 14:14:43 +0000

Why we built SubTrackHub

The "billing fog" in modern cloud infrastructure is real. We analyzed 500+ AWS accounts and found that roughly 30-40% of spend is wasted on resources no one is using.

As a developer, there's nothing more frustrating than realizing you've been paying $100/mo for a t3.medium instance you forgot to delete three months ago.

The Technical Challenge

Building a "read-only" auditor that works across multi-cloud environments (AWS, GCP, Azure) and SaaS (GitHub, Vercel, Clerk) requires a robust integration strategy.

1. The Multi-Cloud "Deep Scan"

We didn't just want to pull high-level billing data. We built deep scans that look at:

CPU Utilization: Flagging instances with <5% average use over 7 days.
Orphaned EBS Volumes: Identifying storage billed for non-existent VMs.
Network Waste: Finding idle NAT Gateways and unattached Elastic IPs.

2. The Identity Gap

We built a GitHub Seat Optimizer that checks for "Ghost Users"—people who are being paid for in your organization but haven't made a commit or logged in for 30+ days.

The "Savage" Advantage: AI-Driven Remediation

Unlike other tools that just show you a graph of your waste, we built Savage AI Roadmaps.

It’s one thing to see a leak; it's another to fix it. Our AI engine generates an exact sequence of CLI commands and terraform adjustments for every leak we find. You don't have to go digging through the AWS Console—you just copy, paste, and save.

Check it out!

If you're a dev or founder tired of paying the "Shadow IT Tax," check out our internal guides or see the tool in action:

👉 View SubTrackHub
👉 Read our Guide: How to find Idle AWS Instances

About the Author

I'm Rahul Singh Shekhawat, a Cloud Engineer and Full-Stack Developer passionate about building scalable SaaS products, AI systems, and cloud infrastructure.

🌐 Portfolio: https://rahulshekhawat.dev

🚀 Product: https://subtrackhub.com

SaaS developer, DevOps engineer, or startup founder Are Bleeding Money on Subscriptions (Here’s How to Fix It)

Rahul singh Shekhawat — Tue, 20 Jan 2026 20:25:33 +0000

If you’re a SaaS developer, DevOps engineer, or startup founder, chances are you’re paying for tools you no longer use.
Cloud services, CI/CD tools, monitoring platforms, AI subscriptions — modern development stacks are powerful, but they’re also silently expensive.
In this article, we’ll break down:
Why developers lose money on subscriptions
Why existing tools fail dev teams
How SubTrackHub helps track and optimize SaaS + cloud spend

💸 The Hidden Subscription Problem in Modern Dev Teams
Today’s development workflow relies heavily on subscription-based tools:

Cloud providers (AWS, GCP, Azure)
Hosting platforms (Vercel, Netlify, Render)
Dev tools (GitHub, Jira, Datadog, Sentry)
AI tools (Copilot, OpenAI, Claude)
Team tools (Slack, Notion, Figma)
What usually goes wrong?
Free trials auto-convert
Old tools stay active after migrations
Multiple billing accounts exist
No single dashboard shows everything
📌 Result:
Developers only notice the issue when the monthly invoice spikes.

🧠 Why Most Subscription Tracking Tools Don’t Work for Developers
Most subscription management tools are:

Built for finance teams, not developers
Manual and spreadsheet-driven
Lacking real cloud or usage context
Focused on reporting, not prevention
Developers need:

Automation
Usage-aware insights
Cloud-native visibility
Minimal manual work
That’s exactly the gap SubTrackHub is designed to fill.

🚀 What Is SubTrackHub?
SubTrackHub is a developer-first subscription and cloud spend tracking platform.

Our mission is simple:
Give developers full visibility and control over SaaS and cloud subscriptions before money is wasted.

⚙️ Key Features of SubTrackHub
✅ Unified Subscription Dashboard
Track all your:

SaaS tools
Cloud services
Developer platforms
From a single dashboard.
🔍 Usage-Aware Insights
Not just what you pay, but:

Which subscriptions are actually used
Which are idle or underutilized
Where downgrades or removals make sense
🤖 AI-Powered Cost Optimization
SubTrackHub uses AI to:

Detect unused subscriptions
Identify cost anomalies
Recommend optimizations
Create a clear cost-reduction roadmap
🧩 Built for Teams & Multi-Tenant SaaS
Designed for:

Startups
Growing SaaS teams
Agencies managing multiple projects
🧪 Why We’re Building This Now
The developer ecosystem has changed:

AI tools are exploding
Cloud bills are harder to predict
Teams are smaller but tool-heavy
But cost visibility hasn’t evolved with developer workflows.

👉 Cost control should be proactive, not reactive.
👨‍💻 Built by Developers, for Developers
SubTrackHub is built by developers who:

Run SaaS products
Deploy on AWS and GCP
Manage DevOps pipelines
Have personally overpaid for tools 😅

This isn’t a finance-first product.
It’s developer-first by design.

🛣️ What’s Next for SubTrackHub?
We’re actively working on:

Deeper cloud integrations
Smarter AI-driven insights
Team-level visibility improvements
Automation for unused resources
We’re also collecting early developer feedback to shape the roadmap.
🙌 Want to Try SubTrackHub?
If you’re:

A SaaS founder
A developer juggling multiple tools
A DevOps or cloud engineer
Someone tired of surprise invoices
👉 Check out SubTrackHub
👉 Share your feedback
👉 Tell us what subscription hurts the most

💬 Discussion
What’s one subscription you forgot you were paying for?

Drop it in the comments 👇
Let’s learn from each other.

SaaS developer, DevOps engineer, or startup founder Are Bleeding Money on Subscriptions (Here’s How to Fix It)

Rahul singh Shekhawat — Tue, 13 Jan 2026 20:20:30 +0000

💸 The Hidden Subscription Problem in Modern Dev Teams
Today’s development workflow relies heavily on subscription-based tools:

Cloud providers (AWS, GCP, Azure)
Hosting platforms (Vercel, Netlify, Render)
Dev tools (GitHub, Jira, Datadog, Sentry)
AI tools (Copilot, OpenAI, Claude)
Team tools (Slack, Notion, Figma)
What usually goes wrong?
Free trials auto-convert
Old tools stay active after migrations
Multiple billing accounts exist
No single dashboard shows everything

📌 Result:
Developers only notice the issue when the monthly invoice spikes.

🧠 Why Most Subscription Tracking Tools Don’t Work for Developers
Most subscription management tools are:

Built for finance teams, not developers
Manual and spreadsheet-driven
Lacking real cloud or usage context
Focused on reporting, not prevention

Developers need:

Automation
Usage-aware insights
Cloud-native visibility
Minimal manual work

That’s exactly the gap SubTrackHub is designed to fill.

🚀 What Is SubTrackHub?
SubTrackHub is a developer-first subscription and cloud spend tracking platform.

Our mission is simple:
Give developers full visibility and control over SaaS and cloud subscriptions before money is wasted.

⚙️ Key Features of SubTrackHub
✅ Unified Subscription Dashboard
Track all your:

SaaS tools
Cloud services
Developer platforms
From a single dashboard.

🔍 Usage-Aware Insights
Not just what you pay, but:

Which subscriptions are actually used
Which are idle or underutilized
Where downgrades or removals make sense

🤖 AI-Powered Cost Optimization
SubTrackHub uses AI to:

Detect unused subscriptions
Identify cost anomalies
Recommend optimizations
Create a clear cost-reduction roadmap

🧩 Built for Teams & Multi-Tenant SaaS
Designed for:

Startups
Growing SaaS teams
Agencies managing multiple projects

🧪 Why We’re Building This Now
The developer ecosystem has changed:

AI tools are exploding
Cloud bills are harder to predict
Teams are smaller but tool-heavy

But cost visibility hasn’t evolved with developer workflows.

👉 Cost control should be proactive, not reactive.
👨‍💻 Built by Developers, for Developers
SubTrackHub is built by developers who:

Run SaaS products
Deploy on AWS and GCP
Manage DevOps pipelines

Have personally overpaid for tools 😅

This isn’t a finance-first product.
It’s developer-first by design.

🛣️ What’s Next for SubTrackHub?
We’re actively working on:

Deeper cloud integrations
Smarter AI-driven insights
Team-level visibility improvements
Automation for unused resources
We’re also collecting early developer feedback to shape the roadmap.

🙌 Want to Try SubTrackHub?
If you’re:

A SaaS founder
A developer juggling multiple tools
A DevOps or cloud engineer

Someone tired of surprise invoices
👉 Check out SubTrackHub
👉 Share your feedback
👉 Tell us what subscription hurts the most

💬 Discussion
What’s one subscription you forgot you were paying for?

Drop it in the comments 👇
Let’s learn from each other.

[Boost]

Rahul singh Shekhawat — Thu, 15 May 2025 06:57:44 +0000

Rahul singh Shekhawat

May 15 '25

Adding EC2 Start/Stop to an Uptime Monitoring Tool – The Why & How

#news #webdev #devops #aws

2 min read

Adding EC2 Start/Stop to an Uptime Monitoring Tool – The Why & How

Rahul singh Shekhawat — Thu, 15 May 2025 05:38:50 +0000

Hey Devs 👋

As I was building PulseGuard, I realized something...

Most monitoring tools focus only on uptime — but cloud control matters too.

If I could monitor my server and start or stop the EC2 instance from the same dashboard, that would save time (and cost). So I built it.

Why Add EC2 Controls?

Avoid logging into the AWS Console every time you need to reboot
Turn off staging/dev servers when not in use
Save $$$ on idle EC2 time
React quickly when something goes wrong

⚙️ How It Works (Under the Hood)

Instead of using Lambda, PulseGuard uses IAM Assume Role to securely access your AWS account.

You create a role in your AWS account with permissions like ec2:StartInstances, StopInstances, etc.
That role is assumable by PulseGuard's AWS identity
When you press the start/stop button in the dashboard, PulseGuard assumes the role, executes the action, and returns the result

No permanent credentials are shared, and all activity is logged.

Bonus: It Integrates with Monitoring

You can monitor your server's uptime and control it — from the same dashboard.

It’s especially useful for short-lived dev or test environments.

Is It Secure?

Yes. Here's how:

You control the IAM role, permissions, and which instances are allowed
PulseGuard uses AssumeRole with external ID for added security
Only minimal EC2 permissions are needed (start/stop/reboot)
Your servers are never exposed directly

If you're tired of switching between monitoring tools and AWS just to reboot or shut down your EC2 instances, give PulseGuard a try.

Would love feedback, ideas, or just to connect if you're building something similar 🚀

aws #cloud #monitoring #ec2 #devops #buildinpublic #indiehacker #nodejs #pulseguard

I Built a Smart Uptime Monitoring Tool with Plans Starting at $0 — Meet PulseGuard

Rahul singh Shekhawat — Sun, 11 May 2025 10:32:13 +0000

Hey folks!

I'm Rahul, and after months of building, testing, and tweaking, I'm super proud to share PulseGuard with you — a smart, AI-powered uptime and performance monitoring tool built with simplicity, affordability, and developers in mind.

❓ Why I Built PulseGuard
As a cloud engineer and developer, I noticed a pattern:

Most uptime monitoring tools are either overpriced or overkill for indie hackers, startups, and small businesses.

Many charge $20+/mo just to monitor a few endpoints — and that's before you get any intelligent insights or SSL monitoring.

So I built PulseGuard to solve this:
Smart monitoring, clear alerts, and pricing that doesn’t hurt.

⚙️ What PulseGuard Does
Here’s what you get with PulseGuard:

Website & API Monitoring – Get notified the moment your website or endpoint goes down

SSL Monitoring – Never miss certificate expiry alerts

AI-Powered Alerts – Fewer false positives, smarter insights

Server Performance Metrics – Track server health and CPU/memory usage

Developer API – Automate and integrate PulseGuard with your tools

Modern Dashboard – Clean, fast, and distraction-free

AWS EC2 Control – Start/Stop your EC2 instances from the PulseGuard UI

Server Monitoring with Remote Control – Track server status and control basic actions

Explore these features via our Live Dashboard and Docs.

💸 Pricing Plans (Built for Real People): https://pulseguard.in/pricing

No credit card required

Full access even on the free plan

Transparent pricing, no surprises

🌍 Who Should Use PulseGuard?
Indie Hackers & Side Project Builders

Freelancers & Web Agencies

Startups on a Budget

DevOps & Cloud Engineers needing EC2 + monitoring in one place

Developers tired of bloated tools

🚀 Try It Now (and Let Me Know What You Think!)
You can be up and running in minutes:
👉 https://pulseguard.in

No lock-in

Smart alerts

EC2 control + monitoring in a single dashboard
🧵 Follow the Journey
I’m building this in public and sharing everything I learn — the wins, the bugs, and the lessons.

Let’s connect and grow together!

buildinpublic #uptime #monitoring #webdev #saas #indiehacker #cloudengineering #ec2 #aws #developer #pulseguard