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Posted on • Originally published at firstpasslab.com

CML vs GNS3 vs INE: How Network Engineers Should Build Labs in 2026

If you're building network labs in 2026, the real question is not "which tool is best?" It's which tool matches the kind of lab work you actually need to do.

Some engineers want a legal, repeatable Cisco-heavy environment for deep protocol work. Some want guided labs they can launch in a minute. Others just want the cheapest path to testing routing, switching, VPN, and firewall ideas at home.

The three tools that come up most often are Cisco Modeling Labs (CML), INE's cloud labs, and GNS3. They overlap, but they are not interchangeable.

My short take:

  • CML is the best foundation for serious Cisco-centric lab work
  • INE is best when you want structured, ready-to-run scenarios
  • GNS3 is still excellent when budget and flexibility matter most

Quick Comparison

Feature CML INE GNS3
Cost ~$200/year ~$20 to $50/month Free
Official Cisco images Yes, included Yes, in provider labs No
Local lab control Yes No, cloud only Yes
Custom topologies Excellent Limited compared to local builds Excellent
Modern Cisco platform support Strong Strong in prebuilt labs Mixed, depends on images and host resources
Best for Cisco-focused engineers, repeatable deep labs Structured practice Budget builds, mixed-vendor labs, experimentation

1. CML is the strongest base for Cisco-heavy labs

CML wins when you want a lab that feels like an engineering sandbox, not just a training portal.

The biggest advantage is simple: you get legitimate Cisco images out of the box. That removes the messiest part of home labbing, especially if you want to work with IOS-XE, IOS-XR, NX-OS, or ASAv without hunting around for images and licenses.

That matters a lot if your lab goals include:

  • routing protocol behavior
  • BGP policy testing
  • MPLS or segment routing experiments
  • EVPN and VXLAN topologies
  • firewall and services integration
  • repeatable topology snapshots

CML is also one of the few options that makes it realistic to build larger topologies and keep iterating on them. You can save a base design, clone it, break it on purpose, and rebuild quickly.

Why engineers like it

  • Legal Cisco image access
  • Good support for modern Cisco virtual platforms
  • API access for automation and repeatable topology deployment
  • Better fit for protocol deep dives than point-and-click training labs

Where it hurts

  • It wants real hardware, especially RAM
  • Apple Silicon support is still awkward because x86 emulation costs performance
  • It is a blank canvas, which is great for engineers but less friendly for beginners

Here's the kind of simple config loop CML is great for when you're testing and retesting behavior:

! R1
router ospf 1
 router-id 1.1.1.1
 network 10.0.12.0 0.0.0.255 area 0
 network 1.1.1.1 0.0.0.0 area 0
!
interface GigabitEthernet2
 ip address 10.0.12.1 255.255.255.0
 ip ospf network point-to-point
 no shutdown
Enter fullscreen mode Exit fullscreen mode
! R2
router ospf 1
 router-id 2.2.2.2
 network 10.0.12.0 0.0.0.255 area 0
 network 2.2.2.2 0.0.0.0 area 0
!
interface GigabitEthernet2
 ip address 10.0.12.2 255.255.255.0
 ip ospf network point-to-point
 no shutdown
Enter fullscreen mode Exit fullscreen mode

That looks trivial, but once you scale it into 12 to 20 nodes and start layering redistribution, overlays, services, and failure testing, the choice of platform starts to matter a lot.

2. INE is best when you want fast, structured reps

INE solves a different problem.

Its biggest strength is not topology freedom. It's friction reduction. If you want a lab launched for you, with a workbook or a guided task waiting, INE is much faster than building everything yourself.

That makes it useful when you want:

  • guided labs instead of blank topologies
  • repeatable drills with less setup overhead
  • cloud access from a lighter laptop
  • a structured path through topics

For a lot of engineers, that structure is worth paying for. It removes the "I spent 45 minutes wiring a lab and 20 minutes actually learning" problem.

Why engineers like it

  • Prebuilt scenarios
  • Fast start time
  • Good for focused drills and guided progression
  • No need to run a heavy local lab server

Where it hurts

  • Monthly cost adds up fast
  • Cloud dependency means no internet, no lab
  • You are working inside someone else's topology design most of the time
  • It is not the best place for open-ended architecture experiments

My practical read: INE is a great complement to a local lab, not always a replacement for one.

3. GNS3 is still the best zero-dollar power tool

GNS3 remains useful because it is flexible, familiar, and free.

If you are early in your journey, building mixed-vendor labs, or testing ideas that do not depend on bundled Cisco images, GNS3 still does a lot of work for the price of zero.

It is especially good for:

  • budget home labs
  • combining routers, Linux VMs, containers, and appliances
  • mixed-vendor experiments
  • learning how virtual network topologies actually fit together

Why engineers like it

  • Free and mature
  • Huge community footprint
  • Flexible enough for lots of weird lab ideas
  • Strong choice when your environment is not Cisco-only

Where it hurts

  • No official Cisco image bundle
  • Modern Cisco virtual platforms can be more painful to run well
  • Stability and performance depend more heavily on how you assemble the environment
  • Support is community-driven

GNS3 is still very good. It is just not the cleanest answer when your goal is repeatable Cisco production-style behavior with minimal licensing ambiguity.

What should you choose?

Here's the practical version.

Choose CML if:

  • you work primarily in Cisco environments
  • you want protocol and topology freedom
  • you care about legal image access
  • you want to automate lab deployment through an API
  • you plan to keep and evolve labs over time

Choose INE if:

  • you learn best from guided scenarios
  • you want to spend more time practicing and less time building
  • you do not want to maintain local lab infrastructure
  • you mainly need focused reps, not open-ended design work

Choose GNS3 if:

  • budget matters most
  • you want maximum flexibility
  • you are building mixed-vendor or hybrid labs
  • you are comfortable owning more of the setup and troubleshooting yourself

My recommendation

For most serious Cisco-focused engineers, the strongest setup is:

  1. CML as the main lab platform
  2. INE when you want guided drills
  3. GNS3 when you need cheap flexibility or mixed-vendor experimentation

If you can only pick one, I would pick CML for deep Cisco lab work and GNS3 for budget-first lab work.

Cost reality

Platform 18-month cost Notes
CML Personal ~$300 Cheapest legal Cisco-first path over time
INE ~$360 to $900 Depends on plan and duration
GNS3 $0 Software is free, hardware and images are the real variables

That cost discussion matters less if the platform saves you dozens of hours or helps you run more realistic labs. Cheap tools are not actually cheap if they slow down every session.

Practical setup advice

A few things matter more than the logo on the platform:

  1. Use a dedicated lab box if you can. CPU and RAM solve more problems than brand debates.
  2. Save base topologies. Rebuilding the same underlay every time is wasted energy.
  3. Practice failure, not just configuration. Break adjacencies, corrupt policy, fail links, then recover.
  4. Keep a known-good config library. Good labs come from fast iteration, not retyping from scratch.
  5. Time your sessions. Even outside certification study, time pressure reveals weak spots in your workflow.

Bottom line

If you want the cleanest and most engineer-friendly Cisco lab platform in 2026, CML is the strongest default.

If you want fast guided reps, INE is excellent.

If you want the most flexible zero-cost lab tool, GNS3 still absolutely belongs in the conversation.

The right answer is less about hype and more about how you work: blank-canvas experimentation, guided repetition, or budget-first flexibility.


Disclosure: This Dev.to post was adapted with AI assistance from the original FirstPassLab article. The canonical version is published on firstpasslab.com.

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