How to Evaluate AI PCB Autorouters: A Practical Guide for Engineers

What Actually Matters in Automated PCB Tools

The market for PCB automation tools has exploded, with vendors making bold promises about AI-powered design. Cut through the noise by focusing on what actually affects your workflow.

Placement is the Prerequisite to Functionality

There is an old adage in PCB design: "Placement is 90% of the work; routing is just connecting the dots."

If component placement is poor, no amount of sophisticated routing—human or AI—can save the board. Long trace loops create inductance, poor grouping ruins signal integrity, and blocked escape paths make routing physically impossible. Good placement isn't just about fitting parts on the board; it is about defining the electrical performance before a single copper track is laid.

Evaluate AI tools on their ability to understand this hierarchy. Does the tool understand that a decoupling capacitor belongs on the specific power pin it protects, not just "somewhere near"? Does it keep crystal oscillators tight to the MCU to minimize noise? A tool that requires you to manually place every component before it starts routing is only doing the easy part of the job.

EDA Integration Quality

Automation that doesn't integrate cleanly creates more work than it eliminates. File format conversions, constraint re-entry, and manual data synchronization add friction to every iteration.

Best integration means working directly with native EDA formats—Altium projects, KiCad files, Cadence databases. The tool should import existing design rules rather than requiring rebuild. After automation runs, you continue refining in your normal EDA environment without export/import friction.

Iteration Speed

PCB design is iterative. First automated attempts rarely produce manufacturing-ready results. Fast iteration matters: tweak constraints, adjust placement, re-run routing, review. If each cycle takes 30 minutes of setup, upload, processing, and download, you'll abandon automation quickly.

The "First Draft" Accelerator

Think of AI automation not as a replacement for a senior engineer, but as a hyper-fast assistant that gets the "boring" 80% done in minutes. Its greatest strength lies in rapid prototyping and generating an immediate initial draft. It allows you to visualize your schematic decisions on the board almost instantly, catching architectural issues early.

However, treat this output as a high-quality draft, not a manufacturing master. While the tool handles the heavy lifting of standard connections and general placement, manual intervention remains essential. You will still need to step in to fine-tune RF paths, adjust mechanical clearances, and apply the final aesthetic polish that ensures long-term reliability.

The value isn't in eliminating work—it is in compressing time. By automating the initial routing pass, you can cut a typical two-week layout cycle down to a few days. The tool gets you to the refinement stage immediately, letting you focus your energy on the critical 10% of the design that actually requires human ingenuity.

The Architecture Question: Cloud vs Desktop

Architecture gets more attention than it deserves. It's one factor among many, not the decision.

When Each Makes Sense

Cloud works for: First-time evaluation (zero setup). Distributed teams needing collaboration. Early prototypes where IP protection isn't critical. Automatic updates without software management.

Desktop works for: Production designs with competitive IP. Regulatory restrictions on data storage. Offline capability or air-gapped environments. High-volume workflows with predictable costs. Deep EDA tool integration.

When Architecture Doesn't Matter

For many teams, automation quality matters more than architecture. A cloud tool with excellent placement and smooth EDA integration beats a poorly integrated desktop tool, even with better data control. A desktop tool requiring extensive manual setup might be less useful than a streamlined cloud alternative, even if desktop aligns with your security preferences.

Test actual tools on your designs before letting architecture philosophy dictate choice. Cloud security concerns might matter less than you expect when automation saves significant time. Or desktop integration might be worth more than collaboration features you won't use.

Use online tools for prototypes and desktop for production if that makes sense. Or use whichever works best for your primary use case. Architecture is less important than whether the tool solves your problems effectively.

Comparing Automation Approaches

PCB automation falls into three categories with different strengths.

Traditional EDA Autorouters

Cadence, Altium, and KiCad offer built-in autorouting with tight platform integration—no file format issues, direct access to design rules, seamless workflow.

The limitation: these autorouters require manual component placement and struggle with modern high-speed designs. They find trace paths without understanding circuit functionality. Results are often poor enough that experienced designers avoid them entirely, preferring manual routing.

Cadence and Altium offer more sophisticated routing in high-end packages. KiCad's autorouting remains basic. If you've never achieved useful results from your EDA's autorouter, that doesn't predict how AI-focused tools will perform.

Online AI Tools

Web-based platforms apply machine learning to PCB automation, promising intelligent placement plus routing with browser access and collaboration features.

Advantage: try tools in minutes without installation. Trade-off: uploading design data to external servers and network dependency for each iteration. Processing speed varies significantly between platforms.

Evaluate based on automation quality rather than architecture. Some produce intelligent placement and routing; others struggle beyond simple designs despite AI marketing. Browser access works well for evaluation and collaboration but may limit production workflows depending on EDA integration.

Desktop AI Tools

Desktop tools like AutoCuro focus on local processing while applying AI to placement and routing. Architecture provides offline capability, direct EDA file access, and complete data control.

AutoCuro integrates with Altium, Cadence, and KiCad using native file formats. It handles placement and routing together, typically achieving 75-85% routing completion in under 10 minutes for embedded hardware. Remaining refinement requires approximately 3 hours—significant savings versus full manual design, though not elimination of engineering work.

Desktop tools generally iterate faster than cloud alternatives through local processing. However, they require more initial setup and manual updates for new features.

Approach	Best For	Key Limitations
Traditional EDA	Simple boards with manual placement done; tight budgets	Limited placement intelligence; mediocre routing; struggles with high-speed
Online AI	Evaluation; distributed teams; prototypes; learning	Requires upload; network dependent; integration friction; variable quality
Desktop AI	Production; IP-sensitive; high-volume; offline; deep integration	Installation required; manual updates; higher initial cost

Decision Framework

Choose based on your specific requirements, not general recommendations.

By Project Type

Rapid prototypes: Speed beats perfect IP control. Online tools with minimal setup help iterate quickly. Production versions can use controlled workflows later.

Production designs: Quality and IP protection justify careful selection. Desktop tools typically integrate better with production workflows and provide more data control.

High complexity (8+ layers, 500+ components): Automation value increases with complexity, but so do algorithm requirements. Test on your actual complexity level—performance on 4-layer references doesn't predict 12-layer behavior.

IP-sensitive: If competitive advantage comes from the layout itself, prioritize data control. Desktop tools eliminate IP risk categories that cloud tools only mitigate through policy.

By Team Structure

Solo or co-located teams: Collaboration features provide minimal value. Choose based on automation quality and EDA integration.

Distributed teams: Cloud platforms' built-in collaboration can streamline reviews and iteration. However, version control systems provide similar benefits with desktop tools if used effectively.

Contract services: Managing multiple clients with varied security needs favors flexibility. Consider different tools for different client sensitivity levels.

By EDA Platform

Altium Designer: Look for tools working directly with Altium projects that import/export complete design rules. File compatibility matters more than architecture. See how we automate altium ai pcb routing.

KiCad: Open file formats make integration easier for both cloud and desktop. Evaluate on automation quality rather than integration concerns. See how we automate kicad pcb routing.

Cadence Allegro: Integration typically requires desktop tools handling Allegro's database structure. Cloud tools often support through exports that may lose design information.

Multiple platforms: Choose automation with broad format support rather than optimizing for one tool.

What to Test Before Committing

Vendor demos reveal little about tool performance on your actual work. Meaningful evaluation requires testing with your designs under realistic conditions.

Run YOUR Designs, Not Vendor Examples

Automation tools are optimized for their demos. Select 2-3 completed projects representing your typical work—including one that was difficult to layout manually.

If IP concerns prevent uploading production designs, create sanitized versions with similar complexity but generic values. Or evaluate on non-sensitive projects first.

Watch edge cases and failures, not just successes. Does it handle your board shapes? Your component libraries? Do error messages help you understand what to adjust?

Measure Actual Time Savings

Track complete workflows: file preparation and constraint setup, automated processing time, upload/download for cloud tools, manual refinement to reach production quality, time fixing automation mistakes.

Compare against normal manual design time. Some tools have fast algorithms but slow workflows; others automate effectively enough that setup becomes worthwhile. End-to-end time to production-ready layout is what matters.

Check Refinement Requirements

After automation completes, examine: How many traces need rerouting for length matching? Are decoupling placements close enough or need adjustment? Do power planes require cleanup? Are differential pairs routed properly? Can you refine efficiently in your EDA tool?

For embedded hardware, expect 2-4 hours refining automated layouts. If refinement consistently takes longer, either automation quality isn't good enough or design complexity exceeds tool capabilities. Under an hour means automation delivers clear value.

Making the Choice

Multiple automation approaches can deliver useful results. The challenge is matching specific tools to your specific needs.

Prioritize automation quality and EDA integration over architectural debates. A tool producing intelligent placement, routing effectively, and fitting your workflow provides more value than one with preferred architecture but poor integration or mediocre results.

Test before committing. Run actual designs through candidate tools and measure real time savings under realistic conditions.

Be willing to use different tools for different projects. One tool doesn't have to handle every use case effectively.

Maintain realistic expectations. Good automation saves significant time on repetitive tasks while requiring engineer judgment on critical decisions. Tools acknowledging this reality deliver lasting value.