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TL;DR: I spent six months pushing Apollo to its limits before realizing that static databases—no matter how big—can’t keep up with the 2026 pace of B2B data decay. I built Onsa to replace “searching” with real-time AI research—here’s the honest breakdown of when to use a database and when to use an agent.
2026 Market Context: As of this year, the shift toward AI-driven sales agents has made “data volume” less valuable than “data signal.” While the experiences shared here began in previous years, the move toward real-time research has only become more critical as AI-filtered inboxes become the standard.
I 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. Regarding the nature of this article: 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Not slightly outdated—completely stale.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
The specific problem of "garbage in, garbage out" for AI pipelines: 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.
(Industry benchmarks from firms like Gartner suggest that roughly 3% of B2B data decays every month, making real-time verification a necessity rather than a luxury.)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?
Transitioning from static data to real-time research: 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.
(This is a matter of public record via the FAA’s Civil Aviation Registry, which provides the ground truth that static databases often lag behind.)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. Regarding Apollo’s technical constraints: 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:
| Use Case | Apollo (Database) | Onsa (Research) |
|---|---|---|
| Primary Goal | Mass volume & broad outreach | High-precision & niche targeting |
| Data Source | Static internal database | Real-time web & registry research |
| Ideal User | Sales Ops / Large SDR teams | Founders / Specialized Sales |
| Setup Time | Instant list exports | Minutes (AI agent research time) |
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.
| Feature | Traditional Sales Intel | Onsa Philosophy |
|---|---|---|
| Query Method | Boolean filters & syntax | Natural language descriptions |
| Data Freshness | Periodic updates (can be stale) | Live verification at time of pull |
| Workflow | Data provider only | End-to-end (Research to Booking) |
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.
Is Onsa just a “wrapper” for existing databases like Apollo?
Not at all—we don’t rely on a pre-built static database. Our AI agents actually navigate the live web, industry registries, and LinkedIn in real-time to find and verify leads based on your specific prompt.
How much slower is the “Research” approach compared to a database?
You’re looking at minutes instead of seconds. While Apollo lets you export 1,000 leads instantly, Onsa takes a bit longer because it’s actually “doing the work” of a human researcher to ensure every lead is a 100% match.
Can I use Onsa and Apollo together?
Definitely, and many teams do. They use Apollo for broad “top of funnel” volume and switch to Onsa when they need to run high-conviction, personalized outbound to a very specific niche.
Does Onsa handle the actual email sending?
Yes, we built it to be end-to-end. We handle the research, the qualification, the message drafting, and the actual outreach so you aren’t stuck stitching five different tools together.
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.