Apollo vs Decagon AI

Apollo and Decagon AI are both business tracked by AIDiveForge. Below is a side-by-side comparison of pricing, capabilities, platforms, and ownership — sourced from each tool's live website and verified before publishing.

Apollo

Apollo combines a contact and company database, AI-assisted search filters, multi-touch email and call sequences, waterfall enrichment, and pipeline analytics under one login. The database scale and intent signal coverage are what most teams cite first — the vendor states over 600,000 companies use the platform. Where cracks show: data freshness at the edge of niche verticals produces bounce rates that force supplemental enrichment runs, and the sequencing builder, while functional, hits its ceiling when teams need branching logic more granular than basic A/B splits. Teams running high-volume outbound at enterprise scale often find they still need a dedicated deliverability layer sitting in front of Apollo's sending infrastructure.

Decagon AI

Decagon deploys AI agents that handle customer support end-to-end: identity verification, order lookups, refunds, subscription changes, and routing to the right team — without a human touching most of it. Workflows are defined in natural language through Agent Operating Procedures, so CX operations teams can update agent behavior without filing an engineering ticket. The platform unifies voice, chat, and email under one intelligence layer, which means the customer's context follows them across channels. Customer stories on the vendor site cite 80% deflection rates and 95% cost reductions — but those are headline outcomes from enterprise deployments with significant onboarding investment. Teams with in-house AI engineering appetite or sub-enterprise ticket volume will find the contract size hard to justify.

Attribute	Apollo	Decagon AI
Pricing	Paid	Paid
Price	$49-$119/mo	—
Free trial	14 days	No
Open source	No	No
Has API	Yes	Yes
Self-hosted option	No	No
Platforms	Web	Cloud (SaaS)
Released	—	2023
Pros	Waterfall enrichment across multiple data sources, so contact records fill in where a single-provider lookup would return blank fields — reducing the manual gap-filling that kills list quality before a sequence even starts. Intent signal filters inside the prospecting search, so reps can surface accounts showing active buying behavior rather than cold-filtering by firmographic data alone. Native dialer, email sequencer, and CRM sync under one login, which means teams avoid the integration debt of three separate tools passing data between each other through Zapier hacks. AI-assisted sequence copy and prospecting research, so SDRs spend less time on first-draft messaging and more time on replies — the vendor reports a 75% meeting increase for at least one customer reducing manual work. API and Chrome extension access, so RevOps teams can pull enrichment data into their own pipelines or trigger Apollo workflows from external systems without rebuilding the data layer.	Natural language Agent Operating Procedures let CX and operations teams update agent workflows without engineering involvement, so behavior changes ship in hours instead of sprint cycles. A single intelligence layer spans voice, chat, and email, which means customer context persists across channels and you avoid the broken handoff where an agent starts the conversation over on a different channel. Built-in A/B testing and QA simulation at scale let teams validate changes against live traffic before fully deploying, so a mis-configured workflow doesn't surface first in production at peak volume. The agent executes transactions — refunds, subscription changes, account recovery — not just lookups, so deflection rates reflect actual resolution rather than customers who gave up and called back. Usage-based pricing tied to conversations or resolutions aligns vendor incentives with actual outcomes, so you are not paying a flat fee for an agent that routes everything to a human.
Cons	Contact data quality degrades noticeably in niche verticals and smaller international markets — bounce rates climb, and teams running outbound into those segments end up purchasing a secondary enrichment source anyway, which defeats the single-stack argument. Sequence branching logic is limited to basic splits; teams that need to route prospects down different paths based on what the previous email returned (opened, clicked, replied with a specific intent) hit the ceiling fast and move to a dedicated sales engagement platform like Outreach or Salesloft. High-volume senders find Apollo's sending infrastructure alone does not protect deliverability at scale — inbox placement degrades without a separate warm-up or deliverability monitoring layer bolted on, adding a tool and cost that partly erodes the stack-consolidation pitch. Advanced workflow automation and certain enrichment credit volumes are paid-only features, so teams that start on the free tier and build processes around it face a forced architecture review when they hit the credit wall mid-campaign.	No self-serve trial and no free tier means you cannot validate fit before entering a procurement cycle — teams that need a proof of concept before budget approval are forced to negotiate access through a sales process, which typically adds weeks before any agent runs a single conversation. Self-hosting is not on offer, which is a hard stop for financial services or healthcare teams with data residency requirements that prohibit sending customer data to a third-party cloud — those teams move to a self-hostable competitor or build on an open-source agent framework instead. Contract structures in the six-figure annual range make Decagon economically indefensible for support operations below a certain ticket volume threshold — teams that are scaling toward enterprise but are not yet there exit for a mid-market tool with per-seat or lower-commitment pricing. Because the platform is fully managed and closed, teams with internal AI engineering capacity who want to own the model selection, retrieval architecture, or fine-tuning pipeline hit a wall — Decagon operates the agent for you, and if that is not what you want, the product is working against your team rather than with it.

Bottom line

Apollo and Decagon AI are closely matched on pricing model, openness, and API availability — pick by feature set and platform support in the table above.

Frequently asked questions

What is the difference between Apollo and Decagon AI?

Apollo is Paid, while Decagon AI is Paid. Compare pricing, free trial, API, platforms, and pros/cons in the table above on AIDiveForge.

Is Apollo better than Decagon AI?

It depends on your workflow. Use the side-by-side attributes (pricing, open source, API, self-hosted, platforms) to decide. AIDiveForge does not rank a universal winner — we publish verified facts so you can choose.