Try OnsaI should be upfront: I’m the founder of Onsa, a competing product. So yes, I’m biased. But I’m also someone who used Apollo extensively before deciding to build an alternative. This is that story.
If you’re researching Apollo alternatives, you’ll find dozens of listicles comparing features and pricing. This isn’t that. This is what I actually experienced, what broke, and why I ended up building something with a completely different philosophy.
Take it for what it is: one founder’s journey through the sales intelligence landscape.
Back when I started, the goal was simple: find companies that match our ideal customer profile, get contact info, reach out. Standard B2B prospecting.
Apollo seemed like the obvious choice. Big database, reasonable pricing, lots of features. I signed up, learned the filters, started pulling lists.
And it worked. Kind of.
Here’s what I found after running campaigns with Apollo data:
10-15% of contacts were essentially useless.
Not slightly outdated—completely stale. Profiles that didn’t exist on LinkedIn anymore. People who’d changed jobs 6+ months ago. Accounts with tiny connection counts that clearly hadn’t been active in years.
When you’re sending cold emails, a 10-15% garbage rate means:
- Higher bounce rates (hurts deliverability)
- Wasted personalization effort on dead leads
- Your AI pipeline learning from bad data
That last one matters more than people realize. If you’re using AI to generate messages or qualify leads, you’re feeding it garbage. And garbage in, garbage out.
The learning curve was real.
Apollo has a lot of features. That’s good and bad. I spent hours figuring out the right filter combinations, understanding the credit system, learning what the data actually meant.
For a sales ops person who lives in these tools, that’s fine. For a founder trying to move fast, it was friction I didn’t anticipate.
It solved one piece of the puzzle.
The bigger realization: Apollo gives you data. That’s it. You still need to:
- Figure out if the lead actually matches your ICP
- Write personalized messages
- Send and track outreach
- Handle responses
- Book meetings
Apollo is a starting point, not a solution. I found myself stitching together multiple tools—Apollo for data, another tool for sequences, another for LinkedIn, another for enrichment.
Here’s where my thinking changed.
Traditional sales intelligence works like a library. There’s a big database. You search it. You pull records. The data was collected at some point in the past and lives in the database until someone updates it.
The problem: B2B data decays fast. People change jobs. Companies pivot. That contact you pulled might have been accurate when it was added, but that was 8 months ago.
What if instead of searching a static database, you had something that researched leads in real-time? That went out and found information at the moment you needed it, from whatever sources were relevant?
That’s the core idea behind what we built with Onsa.
One of our customers sells drone management software for construction and mining companies. Their ideal customer: companies with licensed commercial drone operators.
With Apollo, you’d search for companies in construction, maybe filter by size, and hope some of them use drones. You’re guessing.
Here’s what our approach looks like:
The FAA maintains a public registry of licensed drone operators. So does the Australian aviation authority. Our AI agents start there—pulling companies that actually have licenses to operate commercial drones.
Then they expand: find the company website, identify employees with titles like “Chief Remote Pilot” or “Drone Operations Manager,” pull their LinkedIn profiles.
The result: a list of leads we know match the ICP, not leads we’re hoping might match.
Apollo can’t do this. It’s limited to its own database. Our agents find information wherever it exists—public registries, company websites, LinkedIn, industry databases.
I’m not going to pretend Apollo is bad. It’s not. There are scenarios where it’s the right choice:
If you need volume fast. Apollo’s database is massive. If you need 10,000 contacts in an industry tomorrow, Apollo delivers. Our approach is more targeted but slower.
If you have sales ops resources. A skilled sales ops person can get a lot out of Apollo. The learning curve pays off if you’re going to use it heavily.
If your ICP is broad. Apollo works well when you’re targeting “marketing managers at SaaS companies.” It’s less useful when your ICP is specific or niche.
If budget is tight. Apollo’s entry price is lower than most alternatives. For early-stage teams running scrappy campaigns, it can work.
The fundamental difference isn’t features—it’s philosophy.
Database vs. Research
Apollo maintains a database you search. We send AI agents to research leads in real-time. Different approaches, different tradeoffs.
Filters vs. Natural Language
Apollo uses boolean filters. You learn the syntax, build complex queries. We let you describe your ICP in plain English: “Series A fintech companies in the US with 20-50 employees who recently hired a Head of Sales.”
Data vs. Qualification
Apollo gives you contact data. We give you qualified leads with context—why they match, what signals we found, what might be relevant for outreach.
One Tool vs. End-to-End
Apollo handles data. We handle the full workflow: research → qualification → message generation → outreach → response handling → meeting booking.
Building Onsa, I’ve learned there are no perfect solutions. Here’s what we trade off:
Speed: Our approach is slower than pulling a list from a database. If you need 5,000 contacts by tomorrow, we’re not the right choice.
Predictability: AI agents sometimes find creative paths you didn’t expect. Usually good, occasionally weird. Static databases are more predictable.
Control: Some teams want to control every filter and parameter. Our natural language approach trades control for simplicity.
Proven track record: Apollo has been around for years. We’re newer. That matters to some buyers.
If you’re comparing Apollo alternatives, here’s my honest take:
Start with your actual workflow. How much of the prospecting process do you want to automate? If you just need data and you’ll handle the rest, Apollo might be fine. If you want more of the workflow handled, look at end-to-end solutions.
Consider your ICP specificity. Broad ICP (“marketing managers”) → database works. Specific ICP (“licensed drone operators in construction”) → research approach works better.
Think about your team. Do you have someone who’ll learn the tool deeply? Or do you need something that works without a learning curve?
Test with real campaigns. Every vendor will show you their best data. Run an actual campaign and measure what matters: response rates, meetings booked, deals closed.
I spent a lot of time with Apollo and similar tools before building Onsa. That experience shaped what we built—not because Apollo is bad, but because I hit specific walls that the traditional approach couldn’t solve.
Maybe those walls won’t matter for your use case. Maybe they will.
The sales intelligence space is evolving fast. Static databases were the best option for a long time. AI-native approaches are emerging. The right choice depends on your specific situation, not on what some comparison listicle tells you.
I’m obviously biased toward what we built. But I tried to give you an honest picture of the tradeoffs. Do your own testing. Talk to real users of whatever tools you’re considering.
And if you want to try the research-first approach, you know where to find us.
I’m Bayram, founder of Onsa. We’re building AI agents for B2B sales prospecting. If you want to talk about any of this—or tell me where I’m wrong—find me on LinkedIn.