Apollo.io Reviews

1. michelleturner

   · Managing Director at Nuvoro Digital · 11 months ago
   Apollo for automated outreach

   We use Apollo with our Sales and BDR team to manage our cold outreach. The strength of the platform is the sequences and cadences that you can set up. Compared to other tools we have used in the past like Salesloft the UI is much easier to navigate. The main limitation is that the quality of data isn't as vast and often I can find prospects on Linkedin but not in Apollo.

   🏁 Competitors: SalesLoft

   👍 Pros:    Creating email sequences|Ab testing of emails|Good user experience

   👎 Cons:    Data quality is lacking sometimes|Onboardin process was cumbersome

GitHub Reviews

1. Reinhard

   · Boss at CLOUD Meister · over 5 years ago
   perfect 4 open Source

Apollo.io mentions (69)

• Enriching Free Trial Signups: The PLG Data Stack for Turning Inbound Users Into Qualified Pipeline

  Personal email domains destroy this. Clearbit's Enrichment API returns a null company when it hits gmail.com. Apollo routes personal domains straight to a consumer bucket and skips B2B fields entirely. Even PDL's /person/enrich endpoint — the most permissive of the major providers — gives you around 32% hit rate on Gmail addresses versus 74% on corporate domains. I measured this across 6,200 signups for a... - Source: dev.to / 3 months ago

• Clearbit Is Now HubSpot-Only: A 1-to-1 API Migration Map for Teams Getting Locked Out

  A few things worth flagging: PDL beats Clearbit's historical rates for US and Western European companies, but drops to ~52% match rate for Japan and South Korea specifically. Apollo underperforms on raw company matching but returns significantly more contacts per domain in Prospector-style queries than Clearbit's Prospector ever did — the tradeoff is more stale titles in the result set. Hunter.io is fast and cheap... - Source: dev.to / 3 months ago

• Auto-Enriching Your CRM on New Contact Creation: A No-Code Webhook Playbook

  One thing comparison guides consistently get wrong: Clay is not an enrichment API. It's a waterfall orchestration tool that calls People Data Labs, Apollo, Clearbit, and others in sequence for you. It's useful, but it adds 2–8 seconds of latency per row in my runs and costs more per match than going direct. For a CRM webhook flow where you need sub-second enrichment calls, Clay is the wrong layer to hit first. - Source: dev.to / 3 months ago

• How to Build an OSINT-Powered B2B Prospecting Workflow in 2026 (Without Getting Banned)

  Last year I ran the same LinkedIn Sales Navigator export through three enrichment APIs. Apollo matched 61% of the emails. Hunter.io matched 54%. An OSINT-first pipeline I'd built in n8n — pulling from public sources before hitting any paid API — matched 79% and cost roughly $0.003 per contact. The delta wasn't magic. It was sequence. - Source: dev.to / 4 months ago

• LinkedIn Scraping Is Dead: 5 Legal, ToS-Safe Alternatives That Actually Work in 2026

  Despite having its LinkedIn Page removed in 2025, Apollo remains a functional enrichment and outreach platform with 275M+ contacts. The free tier includes 10,000 credits and the $49/month basic plan is the cheapest entry point for a combined enrichment-plus-sequencing workflow. Apollo's data collection methods have attracted LinkedIn's attention, but the product continues to operate. The risk I'd assign it:... - Source: dev.to / 4 months ago

GitHub mentions (2476)

• Como criei um agente de IA para selecionar testes automaticamente em PRs (Parte 1)

  """Clones a single branch of a GitHub repository into a temporary directory for local scanning.""" Import logging Import shutil Import subprocess Import tempfile Logger = logging.getLogger(__name__) Class RepoFetchError(RuntimeError): """Raised when the target repository/branch cannot be cloned.""" Class RepoFetcher: """Shallow-clones a single branch of a repository so its tests can be scanned... - Source: dev.to / about 16 hours ago

• I Treat My Video Descriptions Like a Build Artifact Now. Here's the Pipeline.

  # video-meta.yml Topic: > Walking through how we cut cold-start time on a Lambda-backed GraphQL API from 2.4s to under 400ms, including the two things that didn't work. Target_keyword: lambda cold start optimization Audience: backend devs who already ship serverless, not beginners Model_channels: - the three channels currently ranking for this keyword Links: repo: https://github.com/... slides:... - Source: dev.to / 1 day ago

• I audited a 5.5 GB AI training dataset by downloading 0.8% of it

  Import struct, json, urllib.request REL = "https://github.com/{owner}/{repo}/releases/download/{tag}/" PART = ["...part1.zip.001", "...part2.zip.002", "...part3.zip.003"] SIZE = [1992294400, 1992294400, 1893639808] # from the releases API Def grab(part, start, end, out): # HTTP range fetch req = urllib.request.Request(REL + PART[part], Headers={"Range":... - Source: dev.to / 6 days ago

• Superlogical – Mitchell Hashimoto

  Is published at https://github.com/.keys so an SSH server to which you connect could do a reverse lookup. This is the reason why my ~/.ssh/config has those 2 lines at the end:

          Host *.

  - Source: Hacker News / 14 days ago

• Your Agent's Confidence Score Is Not a Probability

  All of this assumes you can actually inspect what the agent did — the real inputs after resolution, the real tool outputs, the real intermediate steps. That is the other half of the workflow. AgentLens captures the trace: every model and tool step, resolved inputs, raw outputs. agent-eval scores and gates the output; AgentLens gives you the unforgeable, agent-didn't-author trace data for Tier 1+2 to score against... - Source: dev.to / 14 days ago