Try OnsaOnsa and Apollo take fundamentally different approaches to B2B sales prospecting. Apollo gives you a 275M+ contact database with filters. Onsa gives you AI agents that research, qualify, and draft outreach automatically from a plain-English ICP description. Apollo is a search engine for leads. Onsa is a research assistant that finds them for you.
If you're comparing Onsa and Apollo, here's what actually matters — from someone who used Apollo for six months before building Onsa.
Apollo is a database. You search it, filter it, and download contacts. The data was collected at some point in the past and sits in their system until you query it.
Onsa is an AI agent. You describe your ideal customer in plain English — "Series B SaaS companies with 50-200 employees selling to healthcare, based in the US" — and AI agents go find matching leads across multiple sources, research each one, score them against your ICP, and draft personalized outreach messages.
The difference matters because it changes what your team spends time on:
With Apollo: Your reps spend time searching, filtering, exporting, researching each lead on LinkedIn, writing messages, and qualifying. Apollo handles step one (finding contacts). Your team handles everything else.
With Onsa: Your reps spend time reviewing AI-qualified leads and having conversations. Onsa handles the research, qualification, scoring, and first-draft messaging.
ICP Definition
Apollo: Build your ICP using filters — industry, company size, job title, location, technologies used. Works well if you know exactly what filters to set. Breaks down for complex or niche ICPs that don't map cleanly to database fields.
Onsa: Describe your ICP in natural language or paste your website URL. Onsa analyzes your product, market, and competitors to build the ICP automatically. You review and adjust. Takes under 2 minutes.
Lead Sourcing
Apollo: Search a database of 275M+ contacts. Filter by company, role, seniority, technology, and more. Export lists of up to 25 contacts per credit on the free plan, more on paid plans.
Onsa: AI agents search across LinkedIn, company databases, public registries, and the open web. Leads come with explanations of why each was selected — not just matching fields, but reasoning about fit.
Real example: One of our customers sells drone management software. Their ICP includes licensed drone operators tracked in FAA registries. Onsa's agents pull from these public registries as seed data, then expand to find companies and employees. Apollo can't do this — it's limited to contacts in its own database.
Data Freshness
Apollo: Static database. Updates happen periodically but data can be stale. In my experience, 10-15% of contacts are nonexistent or severely outdated — profiles that don't exist on LinkedIn anymore, people who changed jobs months ago, accounts with no activity in 12+ months.
Onsa: Data is researched in real-time at the moment you need it. When Onsa's agent checks a lead, it's pulling current information from live sources. No stale database problem.
Lead Qualification
Apollo: Basic scoring based on company and contact attributes. You can set up lead scoring rules, but it's checkbox-based — company size matches, job title matches, done.
Onsa: Uses the Fit + Intent + Timing scoring framework. Every lead gets a 0-100 score across three dimensions: how well the company fits your ICP (0-40), how strong the buying intent signals are (0-40), and how urgent the timing is (0-20). Each score comes with an explanation.
Personalized Outreach
Apollo: Email sequences with template variables. You can personalize with {first_name}, {company_name}, and similar merge fields. Better than nothing, but recipients can tell it's templated.
Onsa: AI-generated messages tailored to each lead's specific company, role, challenges, and recent activity. Not templates with variables — each message references real research about the prospect. Your reps review and edit rather than writing from scratch.
Sales Call Intelligence
Apollo: Not available. Apollo focuses on prospecting, not post-meeting analysis.
Onsa: Analyze recorded sales calls for coaching insights, objection patterns, and deal risk signals. Useful for understanding what's working in conversations and where deals stall.
Pricing
Apollo: Free tier with 60 credits/month. Paid plans start at $49/month (Basic), $79/month (Professional), $119/month (Organization). Per-credit model means costs scale with usage.
Onsa: Trial at fixed price for qualified leads delivered. Then outcome-based pricing (per meeting booked) with minimum monthly commitment. You pay for results, not database access.
Apollo is the right choice if:
— You have a well-defined ICP that maps cleanly to database filters (industry + company size + job title)
— Your team has the time and process to manually research, qualify, and write outreach for each lead
— You need phone numbers and direct emails in bulk
— You're primarily doing high-volume cold email and need a large contact list
— You have a dedicated sales ops person who can build and maintain sequences
— Budget is the primary constraint and you need the most contacts per dollar
Onsa is the right choice if:
— Your ICP is complex or niche (can't be captured with simple database filters)
— Your team spends too much time on research and not enough on selling
— You want AI to handle qualification so reps focus on conversations
— Data freshness matters — you've been burned by stale contact databases
— You need personalized outreach, not templated sequences
— You'd rather pay for qualified leads and meetings than for database credits
Some teams use both. Apollo for bulk contact data in well-defined segments. Onsa for AI-powered research, qualification, and messaging in complex or niche segments.
This works well for teams that sell into multiple verticals — some straightforward enough for database filtering, others too nuanced for checkboxes.
Before building Onsa, I used Apollo as my primary prospecting tool. Here's what I found:
The good: Apollo's database is genuinely massive. For common B2B segments (SaaS companies, marketing agencies, etc.), you'll find contacts. The filtering is powerful once you learn it. The Chrome extension for LinkedIn is useful.
The frustrating: Data quality varies significantly. I'd download a list of 100 prospects and find 10-15 were ghosts — profiles that didn't exist, people who'd changed jobs, or accounts clearly inactive for over a year. When you're feeding that data into an outreach pipeline, those wasted contacts cost you time, sender reputation, and credibility.
The gap that became Onsa: Apollo gives you data, but the work of researching each prospect, figuring out if they're actually a good fit, understanding their specific situation, and writing a relevant message — that's still 100% manual. That gap is what Onsa fills.
As we described in our task automation framework, the highest-leverage automation targets high-volume, repetitive tasks. Lead research and qualification fit that profile perfectly. The relationship building that follows — that stays human.
If you're currently on Apollo and considering Onsa:
1. Export your current ICP criteria. What filters do you use? What signals matter? This becomes the natural-language description you give Onsa.
2. Run a parallel test. Pick one segment. Source 50 leads from Apollo and 50 from Onsa. Compare: time spent per lead, qualification accuracy, response rates, meetings booked.
3. Measure what matters. Don't compare on volume alone. Compare on meetings booked per hour of rep time. That's the metric that reveals whether AI-powered research actually saves your team effort.
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What is the main difference between Onsa and Apollo?
Apollo is a static contact database you search with filters. Onsa is an AI agent that researches, qualifies, and drafts outreach for you automatically. Apollo gives you data; Onsa gives you qualified leads ready for conversation.
Is Onsa more expensive than Apollo?
Different pricing models. Apollo charges per-credit for database access (starting at $49/month). Onsa uses outcome-based pricing — you pay per qualified lead or meeting booked, with a minimum monthly commitment. For teams that value time savings and lead quality, Onsa's cost-per-meeting is typically lower.
Can Onsa replace Apollo completely?
For most teams, yes. Onsa covers the full prospecting workflow: ICP definition, lead sourcing, qualification, and personalized outreach drafts. Some teams keep Apollo for bulk contact data in straightforward segments while using Onsa for complex or niche ICP targeting.
How does Onsa handle data freshness compared to Apollo?
Onsa researches leads in real-time from live sources at the moment you need the data. Apollo uses a static database that updates periodically, which can lead to 10-15% stale contacts (changed jobs, inactive profiles, non-existent accounts).
Does Onsa work for outbound or just inbound?
Both. Onsa sources outbound leads matching your ICP, and also qualifies inbound leads using the Fit + Intent + Timing scoring framework. Inbound qualification scores leads 0-100 and routes them automatically — hot leads to AEs, cold leads to nurture.
What if my ICP is too niche for a database?
This is where Onsa excels. Our agents search across LinkedIn, public registries, company websites, and industry databases — not just one contact database. Example: finding licensed drone operators from FAA registries, or identifying companies using specific open-source tools from GitHub activity.
*I'm Bayram, founder of Onsa. I built Onsa after 6 months of using Apollo and realizing that sales teams need AI agents, not bigger databases. If you want to see how AI-powered prospecting works for your ICP, try it free.*