Kimchi vs. LazyApply: Which AI Job Search Tool Actually Gets You Hired?

AI job search tools all promise the same thing: less time applying, more interviews. But "AI job search tool" covers two very different approaches, and LazyApply and Kimchi sit on opposite ends of that spectrum. Here's what actually separates them.

What LazyApply does

LazyApply is a browser extension built for volume. It auto-fills and submits applications across platforms like LinkedIn, Indeed, Greenhouse, Dice, and ZipRecruiter, and its plans are priced around how many applications you can fire off, up to 1,500 a day on its highest tier. The pitch is simple: apply to everything that could plausibly fit, and let the odds work in your favor.

The tradeoff shows up in how people actually experience it. LazyApply sits around 2 stars on Trustpilot, and a common complaint is that it applies to roles that don't match the person's background or level at all. One widely shared story involved a user submitting 14,000 applications and getting back hundreds of rejections for jobs they were never a real fit for in the first place. Volume without targeting doesn't convert, it just generates noise, for you and for the hiring managers on the other end.

What Kimchi does differently

Kimchi, built by Second Ladder, starts from the opposite assumption: the bottleneck in a senior job search isn't application volume, it's precision. Instead of blasting every remotely relevant posting, Kimchi is built to match you to roles that actually fit your background, level, and target comp, and to position each application so it reads like it was written specifically for that role, because it was tailored to it.

That matters more the more senior you are. A Director or VP applying to 500 roles a month doesn't look diligent to a hiring manager, it looks like a mismatch. A smaller number of precisely targeted, well-positioned applications consistently outperforms a flood of generic ones, especially past the Manager level.

Volume vs. precision: why more applications isn't the win

The math that matters in a job search isn't applications sent, it's interviews earned per application sent. Online applications convert at roughly 2 to 4 percent for senior candidates even when they're well targeted. Applying to roles you're not actually a fit for pushes that number toward zero, and it costs you something else too: ATS systems and recruiters both learn to filter out accounts that look automated or indiscriminate. A tool optimized purely for volume can quietly work against you.

Which one fits which person

LazyApply can make sense if you're early career, casting a wide net for entry-level or high-volume roles where any reasonable match is worth a shot, and you're comfortable with a lower hit rate in exchange for sheer scale. Kimchi is built for the other end of the market: Directors, VPs, Heads of, and senior individual contributors in strategy, product, and operations, where the fastest path to an offer is fewer, better applications, not more of them.

What to actually look for in an AI job search tool

Whichever tool you use, a few questions cut through the marketing. Does it match you to roles based on your actual background, or does it apply to anything with overlapping keywords? Does it tailor your resume and materials per application, or send the same generic version everywhere? Can you see and control what's being submitted on your behalf, or is it a black box? And does the pricing reward volume, or reward outcomes? The answers tell you more about how a tool will perform than any feature list.

Bottom line

Both tools automate the mechanics of applying. The difference is what they optimize for. LazyApply optimizes for how many applications go out. Kimchi optimizes for how many of those applications turn into real conversations. If you're running a senior search where every application needs to land, precision beats volume every time.

Kimchi is Second Ladder's AI job search tool, built specifically for senior professionals who need their applications to actually convert. See how Kimchi works.