You’ve probably seen the advice a hundred times: “Just get into tech!” As if “tech” were a single door you walk through. In reality, you’re standing at a crossroads with five different paths—cybersecurity, cloud engineering, DevOps, data analytics, and AI/ML—each with wildly different entry requirements, salary ceilings, and day-to-day realities.

The stakes feel high because they are. Pick the wrong specialization and you could spend 18 months learning skills that don’t match your personality, only to burn out or plateau in a role you hate.

This guide cuts through the hype. No “all paths are equally valid” hedging. You’ll get honest assessments of each field’s difficulty, salary potential, job availability, and—most importantly—who actually thrives in each one.

Quick Comparison: The Five Major IT Paths

Before diving deep, here’s the snapshot you need. Bookmark this table—you’ll reference it later.

Field Entry Salary Senior Salary Time to Entry Job Growth
Cybersecurity $70K-$99K $130K-$210K+ 6-12 months 33% (2024-2034)
Cloud Engineering $100K-$130K $150K-$260K 9-18 months High demand
DevOps/SRE $95K-$120K $145K-$200K+ 12-24 months 22% YoY growth
Data Analytics $53K-$68K $115K-$150K 6-12 months Strong
AI/ML Engineering $120K-$150K $170K-$200K+ 18-36 months 25%+ projected

Source notes: Salary data compiled from ZipRecruiter, Glassdoor, and Robert Half’s 2026 Salary Guide. Job growth projections from the Bureau of Labor Statistics.

Now let’s break down what these numbers actually mean for your career.

Cybersecurity: The Fastest Entry with Constant Demand

What You’ll Actually Do

Forget the hoodie-wearing hacker stereotype. Entry-level cybersecurity is mostly:

  • Monitoring security dashboards and SIEM alerts
  • Investigating phishing attempts and suspicious activity
  • Running vulnerability scans and documenting findings
  • Writing incident reports and following playbooks
  • Communicating with non-technical stakeholders about risks

You’re essentially a detective with a compliance checklist. The exciting penetration testing and red team work? That comes 3-5 years in, if you pursue it.

Why It’s Hot Right Now

The math is simple: 514,000+ cybersecurity job openings in the U.S. alone, with a 33% projected growth rate through 2034 according to the BLS. Companies can’t hire fast enough.

Better yet, 53% of employers are increasing starting pay for cyber talent—a rare bright spot in a cautious hiring market. The persistent shortage means leverage during negotiations.

The Entry Path

This is where cybersecurity shines: it’s one of the fastest specializations to break into.

The 6-9 month path:

  1. Get CompTIA Security+ ($404 exam, 2-3 months of study)
  2. Build a home lab with basic security tools
  3. Practice on platforms like TryHackMe or HackTheBox
  4. Apply for SOC Analyst or Security Analyst roles

The Security+ cert signals you understand fundamentals. Pair it with demonstrated hands-on practice and you’re competitive for entry-level positions.

Who Actually Thrives

You’ll love cybersecurity if you:

  • Enjoy puzzles and investigation (finding the anomaly in the data)
  • Can stay calm under pressure (incidents don’t wait for convenient timing)
  • Don’t mind repetitive monitoring work (80% of the job)
  • Communicate well with non-technical people (explaining risk is half the job)

You’ll struggle if you:

  • Need immediate variety and novelty
  • Hate documentation and process
  • Want to code all day (security is more analysis than development)

Honest take: The entry-level grind is real. Alert fatigue, false positives, and shift work are common. But the ceiling is high, and the field’s not going anywhere.

For a deeper dive, check our cybersecurity career transition guide.

Cloud Engineering: Highest Entry Salary, Steeper Learning Curve

What You’ll Actually Do

Cloud engineers design, build, and maintain infrastructure on platforms like AWS, Azure, or Google Cloud. Your days involve:

  • Writing infrastructure-as-code (Terraform, CloudFormation)
  • Configuring and securing cloud services
  • Optimizing costs (cloud bills can explode quickly)
  • Troubleshooting deployment and networking issues
  • Collaborating with development teams on architecture

It’s a blend of traditional system administration and modern automation. You’re not racking servers—you’re writing code that provisions them.

Why It Pays So Well

Cloud engineers command premium salaries because nearly every company relies on cloud infrastructure. The median salary hits $150,000, with senior architects pushing $260,000 at top companies.

The demand is structural. Cloud spending keeps growing, and someone needs to manage it all. AWS skills alone appeared in 14% of tech job postings in 2025—up from 12% the year before.

The Entry Path

Cloud engineering has a steeper barrier than cybersecurity. Here’s the realistic timeline:

The 9-18 month path:

  1. Get foundational IT experience (help desk, sysadmin, or self-taught)
  2. Earn AWS Cloud Practitioner or Azure Fundamentals (1-2 months)
  3. Move to Solutions Architect Associate or equivalent (2-4 months)
  4. Build real projects on free tier accounts
  5. Learn Terraform or another IaC tool
  6. Target junior cloud engineer or cloud support roles

The challenge: most “entry-level” cloud jobs still expect some professional experience. Your best path might be transitioning from IT support or system administration rather than entering fresh.

Who Actually Thrives

You’ll love cloud engineering if you:

  • Enjoy automation and efficiency (making repetitive tasks disappear)
  • Think in systems and architecture
  • Like learning constantly (cloud services change weekly)
  • Want high compensation relatively early in your career

You’ll struggle if you:

  • Prefer hands-on physical work
  • Dislike ambiguity (cloud problems can be opaque)
  • Want narrow, predictable responsibilities

Honest take: The money is real, but so is the complexity. This isn’t the easiest path for complete beginners—consider it a strong second move after gaining IT fundamentals.

Explore more in our cloud computing career path guide.

DevOps/SRE: The Infrastructure-Development Bridge

What You’ll Actually Do

DevOps engineers automate the entire software delivery pipeline. You’ll spend time:

  • Building and maintaining CI/CD pipelines
  • Managing containerization (Docker, Kubernetes)
  • Writing scripts to automate deployments
  • Monitoring system performance and reliability
  • Collaborating between development and operations teams
  • Handling on-call rotations for production issues

SRE (Site Reliability Engineering) is a related discipline focused on system reliability at scale. The skills overlap significantly, though SRE tends toward larger organizations.

The Reality of “DevOps”

Here’s what nobody tells you: “DevOps” is more philosophy than job title. Many companies slap the label on various roles—from glorified system administrators to full-stack infrastructure developers.

That said, genuine DevOps engineering combines coding ability with infrastructure knowledge. You need to be comfortable writing automation scripts and understanding distributed systems.

DevSecOps engineers specifically saw 22% year-over-year job posting growth—security-integrated DevOps is particularly hot.

The Entry Path

DevOps has one of the longer ramps because it requires proficiency across multiple domains.

The 12-24 month path:

  1. Build Linux administration skills (seriously, learn Linux)
  2. Learn scripting (Python, Bash)
  3. Master Docker fundamentals
  4. Understand Git version control
  5. Learn a CI/CD tool (Jenkins, GitHub Actions, GitLab CI)
  6. Get cloud certified (AWS or Azure)
  7. Study Kubernetes basics
  8. Build a portfolio of automation projects

Most DevOps engineers transitioned from development or system administration backgrounds. Pure entry-level DevOps roles exist but are rare.

Who Actually Thrives

You’ll love DevOps if you:

  • Enjoy coding AND infrastructure (the bridge appeals to you)
  • Want to see immediate impact from automation
  • Thrive in fast-paced, ship-often environments
  • Handle on-call responsibilities well

You’ll struggle if you:

  • Prefer deep specialization over breadth
  • Dislike context-switching between tasks
  • Need strict work-life boundaries (on-call is common)

Honest take: DevOps pays well because it’s demanding. The on-call culture burns out many people. Make sure you’re genuinely interested in automation before committing.

See our DevOps career guide and DevOps vs SRE comparison for more details.

Data Analytics: Lowest Entry Barrier, Steady Growth

What You’ll Actually Do

Data analysts transform raw data into insights that drive business decisions. Daily work includes:

  • Querying databases with SQL
  • Cleaning and organizing datasets
  • Building dashboards and visualizations
  • Creating reports for stakeholders
  • Identifying trends and patterns
  • Presenting findings to non-technical audiences

You’re the translator between raw numbers and actionable business intelligence.

Why Consider It

Data analytics offers the most accessible entry point for career changers without technical backgrounds. The average entry-level salary of $63K-$68K is lower than other paths, but the barriers are proportionally lower too.

Companies across every industry need data analysis—it’s not limited to tech. Healthcare, finance, retail, and manufacturing all hire data analysts.

The Entry Path

This is arguably the fastest path from zero to employed.

The 6-12 month path:

  1. Learn SQL (the foundation of everything)
  2. Master Excel/Google Sheets at an advanced level
  3. Pick up a visualization tool (Tableau, Power BI)
  4. Learn basic statistics
  5. Complete the Google Data Analytics Certificate
  6. Build a portfolio with real datasets
  7. Apply for entry-level analyst roles

No coding required for entry-level positions. Python helps but isn’t mandatory.

Who Actually Thrives

You’ll love data analytics if you:

  • Enjoy finding patterns and telling stories with numbers
  • Communicate well with non-technical stakeholders
  • Have attention to detail (data quality matters)
  • Want a stable, growing field without extreme hours

You’ll struggle if you:

  • Dislike repetitive data cleaning work
  • Want the highest possible salary immediately
  • Prefer building things over analyzing things

Honest take: Data analytics is a solid, accessible path—but the salary ceiling is lower unless you move into data science or machine learning. Consider it a launchpad rather than a destination.

Check out our data analyst career path roadmap.

AI/ML Engineering: Highest Ceiling, Longest Ramp

What You’ll Actually Do

AI/ML engineers build and deploy machine learning models. The work involves:

  • Designing and training ML models
  • Processing and preparing large datasets
  • Deploying models to production
  • Monitoring model performance and drift
  • Collaborating with data scientists and product teams
  • Staying current with techniques that change constantly

This is the most technical path by far.

The Current Hype (and Reality)

AI is everywhere in 2026. Companies scramble to hire ML talent, and salaries reflect the desperation—median entry-level around $134K, mid-level hitting $170K.

But here’s the reality check: AI/ML engineering requires significant mathematical background. You need comfort with linear algebra, calculus, probability, and statistics. The entry bar is high.

AI-related job postings grew 25% year-over-year, but much of that growth favors candidates with graduate degrees or substantial professional experience.

The Entry Path

This path is lengthy and requires academic preparation.

The 18-36 month path:

  1. Build strong Python programming skills
  2. Study mathematics (linear algebra, calculus, statistics)
  3. Take ML courses (Andrew Ng’s courses are canonical starting points)
  4. Work through projects with real datasets
  5. Consider a master’s degree or bootcamp (many employers prefer advanced degrees)
  6. Build a GitHub portfolio of ML projects
  7. Target ML engineer or data scientist roles

A bachelor’s degree in a quantitative field (CS, math, physics, statistics) helps significantly. Career changers without this background face a steeper climb.

Who Actually Thrives

You’ll love AI/ML if you:

  • Have strong mathematical foundations
  • Enjoy research and experimentation
  • Want to work on problems nobody has solved yet
  • Can handle ambiguity (ML projects often fail before they succeed)

You’ll struggle if you:

  • Dislike math (seriously, there’s no avoiding it)
  • Need quick wins and clear paths
  • Prefer established best practices over experimentation

Honest take: Don’t chase AI/ML just because of the salaries. The mathematical prerequisites are non-negotiable, and the field moves fast enough to induce constant learning pressure. Pursue it if you genuinely find the domain fascinating.

Decision Framework: Choose Your Path

Still uncertain? Walk through this framework.

Choose Cybersecurity If…

  • You want the fastest path to employment (6-12 months)
  • Investigation and analysis appeal more than building
  • You’re comfortable with shift work and on-call early in your career
  • Job security is a top priority (33% growth rate)

Your next step: Get Security+ and start practicing on TryHackMe.

Choose Cloud Engineering If…

  • You have some IT experience already (help desk, sysadmin)
  • High entry-level salary matters to you ($100K+)
  • You enjoy automation and infrastructure
  • You want transferable skills across almost any tech company

Your next step: Start with AWS Cloud Practitioner or Azure Fundamentals.

Choose DevOps If…

  • You’re comfortable with both coding and infrastructure
  • You want to see immediate impact from automation
  • Fast-paced, ship-often culture energizes you
  • You’re okay with on-call responsibilities

Your next step: Learn Linux, then Python and Docker.

Choose Data Analytics If…

  • You’re a career changer without a technical background
  • Communication and business context interest you
  • You prefer analysis over building
  • You want a stable, accessible entry point

Your next step: Learn SQL, master Excel, then complete the Google Data Analytics Certificate.

Choose AI/ML If…

  • You have strong math foundations (or genuinely want to build them)
  • You’re prepared for a longer timeline (18-36 months)
  • Research and experimentation excite you
  • Highest possible salary ceiling is worth the extra preparation

Your next step: Assess your math background honestly. If weak, start there before ML courses.

What About Combinations?

The smartest career moves often combine adjacent skills. Consider these high-value intersections:

Cloud + Security = Cloud Security Engineer Average salary: $166K. Both cloud and security experience plus their intersection.

DevOps + Security = DevSecOps 22% year-over-year job growth. Security-focused automation is increasingly critical.

Data + AI = ML Engineer Natural progression from data analytics into more technical ML work.

Cloud + DevOps = Platform Engineering Our platform engineering guide covers this emerging discipline.

These combinations take longer to achieve but command premium salaries and face less competition.

The IT Foundation Everyone Needs

Regardless of which specialization you choose, certain fundamentals matter everywhere:

Many people skip these fundamentals while chasing advanced certifications. That’s a mistake. Strong basics compound throughout your career.

The Uncomfortable Truth About Career Choice

Here’s what most guides won’t tell you: you’re not making a permanent decision. Most IT professionals change specializations at least once in their careers.

The data analyst who learns Python often moves into data science. The cybersecurity analyst who enjoys automation becomes a DevSecOps engineer. The cloud engineer interested in reliability shifts to SRE.

Your first specialization is a starting point, not a life sentence.

Pick the path that:

  1. You can realistically enter given your current situation
  2. Matches your personality and working style
  3. Offers growth opportunities that interest you

Then start moving. Analysis paralysis kills more tech careers than wrong choices.

FAQ

Which IT field pays the most in 2026?

AI/ML engineering has the highest entry-level salaries ($120K-$150K) but also the highest barriers. Cloud engineering offers the best combination of high salary ($100K-$130K entry) and achievable entry requirements. Cybersecurity offers strong salaries ($70K-$99K entry) with the fastest path to employment.

What’s the easiest IT field to break into?

Data analytics has the lowest technical barriers—SQL, Excel, and visualization tools can get you hired. IT support/help desk remains the most accessible entry point overall, though it’s a generalist role rather than a specialization. For specializations specifically, cybersecurity with Security+ certification offers the fastest route.

Do I need a degree for these IT careers?

Not necessarily. 23% of hiring managers favor certifications over degrees. Cybersecurity, cloud engineering, and DevOps are particularly accessible without degrees if you have certifications and demonstrable skills. AI/ML is the exception—many employers prefer graduate degrees.

How long does it really take to switch into tech?

Realistic timelines: cybersecurity (6-12 months), data analytics (6-12 months), cloud engineering (9-18 months), DevOps (12-24 months), AI/ML (18-36 months). These assume focused part-time study while working, not full-time bootcamps.

Should I start with IT support before specializing?

It depends. Starting in help desk or IT support gives you foundational experience that makes specialization easier—especially for cloud and DevOps roles. However, if you’re targeting cybersecurity or data analytics, direct entry is possible with the right certifications.