Bullhorn ATS vs Spott

70% of placements made come from candidates that already live inside your existing database, rather than from newly sourced candidates. As a recruitment agency matures, efficiently and quickly filtering the existing candidate pool becomes increasingly important.

Bullhorn heavily relies on Boolean queries to narrow down your search. In practice, this usually means your searches quickly explode and start to look like this:

("Software Engineer" OR "Software Developer" OR "ML Engineer" OR "AI Engineer") AND ("Machine Learning" OR "Artificial Intelligence" OR "Deep Learning" OR "NLP" OR "Computer Vision") AND ("Python" OR "TensorFlow" OR "PyTorch" OR "Scikit-learn" OR "Keras") AND ("Natural Language Processing" OR "Reinforcement Learning" OR "Neural Networks") AND ("Bachelor's Degree" OR "Master's Degree" OR "PhD") AND ("5+ years" OR "senior" OR "lead" OR "principal")

Even simple variations in search terms (like typing a middle name instead of a first name) can completely derail the search process. Loading the search screen typically takes 5 to 6 seconds, with the same delay each time you adjust the filters.

All that effort, with no guarantee you didn’t miss your gold medalist along the way. With Spott, a simple search like "senior AI/ML engineers with experience in Python, TensorFlow or similar tools" delivers similar results. We automatically vectorize your entire candidate database - meaning our models understand semantics. They recognize that a developer and a software engineer are the same, without the need for complex ‘OR’ filters.

Bullhorn’s 2024 acquisition of Textkernel shows they recognize this growing need. By integrating Textkernel’s AI-powered matching and search capabilities, Bullhorn aims to improve this very bottleneck, but the overall experience is still held back by data limitations and legacy architecture.

Creating a cadence of outreach messages is becoming the preferred way to manage larger prospect pipelines, replacing the tedious and time-consuming task of sending each message manually.

Bullhorn has chosen not to include sourcing cadence features in their platform, which means users need to purchase this functionality from third-party providers, often at a high cost. Spott, on the other hand, lets you set up integrated outreach cadences across platforms, similar to tools like Sourcewhale or Dripify. For example, you can send an initial LinkedIn connection request, follow up with a personalized message, send a reminder, and schedule a final check-in, all directly from within Spott.

Although the Bullhorn interface feels a bit outdated, it is still functional. That said, older software often comes with other limitations. Agencies commonly face high customization costs to adapt the core platform to their needs, such as integrating a CV parser with their website or adding new custom fields.

Spott addresses these pain points directly. We allow users to create their own custom attributes or labels, so you have the flexibility to customize everything to your own workflow without additional back and forth.

You can reach out to clients via email, WhatsApp, or LinkedIn directly from Spott, without switching between platforms. You can also export your pipeline as a status update straight from Spott, eliminating the need to copy and paste names and numbers between your ATS and email.

Bullhorn is well respected for its Analytics suite. It acquired Cube19, a UK player specializing in growth analytics for the global recruitment industry.

Customers are generally positive about Bullhorn’s dashboards, live metrics, and the wide range of insights across sourcing, placements and job postings. As agencies scale, analytics become increasingly important for mid- and upper-level management, and Bullhorn has successfully positioned itself to meet that need.

Spott adds another layer on top. Ask, “What’s my average time-to-fill?” or “How many placements last month?” to get instant answers or dashboards without configuring dashboards manually. No digging through hundreds of attributes - just plain English queries. This lets you focus on creating insights, not on the process to get there.

Customers report that Bullhorn's automation tools are offered as bolt-on solutions at an additional $39 per month, so they do not natively integrate with the core platform.

Take messaging as an example. While some email campaigns can be automated, not all emails and calendars are automatically synced within the ATS. This requires double-checking, which often cancels out the time saved by automation.

We’ve yet to touch on one key issue: data quality. In Bullhorn, you need to manually log all your calls, notes, and tag all your candidates - a time-consuming process prone to human error. At Spott, we offer our own note taker, automatically summarizing notes and suggesting updates straight into our ATS.

Additionally, tagging of candidates can happen automatically with AI, so there's no need to do that on a rolling basis anymore. By eliminating these manual tasks, we've created a system that maintains higher data quality with significantly less effort, allowing recruiters to focus on what really matters - building relationships with candidates and clients.

Bullhorn is built for enterprise-level customers, and its pricing reflects that. The basic plan starts at $99 per user per month, but lacks essential features like email add-ons and LinkedIn integration. Upgrading to the ‘Corporate’ plan at $199 per user per month still leaves out key functionality such as customizable dashboards, lead tracking, and API access. As a result, costs rise quickly if you need a fully equipped recruitment platform.

Users on Trustpilot consistently report that Bullhorn’s base pricing is misleading. When they finally reach customer support, they are often met with sales reps pushing upgrades instead of solutions. This forces customers to pay significantly more than the already expensive, advertised starting price.

Bullhorn is the gold standard among legacy recruitment software: a recruitment platform designed for large enterprises who have dedicated training resources, a need for many features and generous budgets. But will they be able to keep up with the next wave of AI automation?