AI receptionist pricing comparison
AI receptionist pricing is hard to compare because vendors mix monthly platform fees, per-minute usage, human handoff, setup, and custom implementation. Use this page to estimate the real cost before booking demos.
Best for missed-call text-back, lightweight phone/SMS ops, and narrow workflows. Watch usage fees and limited AI receptionist depth.
Best for SMBs that need receptionist coverage, booking, qualification, and some handoff support.
Best for higher-volume teams, multi-location operations, custom voice agents, human backup, or done-for-you implementation.
Buyer scorecard
Scoring methodology: directional editorial scoring based on visible pricing signal, integration breadth, deployment fit, setup complexity, profile completeness, and buyer tradeoffs. Scores are not paid rankings.
| Vendor | Score | Best-fit signal | Watch-out |
|---|---|---|---|
| Smith.ai | 8/10 | Strong for receptionist and intake workflows. | Pricing requires custom quote |
| Dialpad | 7/10 | AI-powered voice and contact center platform. | Expensive for small operations |
| OpenPhone | 9/10 | Business phone and SMS for teams. | Not full AI receptionist |
| CallRail | 9/10 | Useful for attribution, call tracking, and lead-source visibility. | Focused on call tracking, not full automation |
| Retell AI | 7/10 | Flexible voice AI for custom workflows. | Requires technical team |
| Atlas Agent Suite | 7/10 | Implementation partner for AI automation workflows. | Not a software vendor |
Pricing questions to ask vendors
- Is pricing per user, per minute, per call, per location, or flat monthly?
- Are setup, prompt/workflow design, number porting, integrations, or after-hours coverage extra?
- What happens when call volume spikes?
- Is human handoff included or billed separately?
- Can the vendor show expected cost at your real monthly call volume?
Conversion guidance
If you know your missed-call volume, average job value, close rate, and monthly budget, the fastest path is the matching wizard. It packages your context into a vendor-fit request instead of forcing you through generic demos.