Higher-volume Shopify teams that want agents and AI to act on order data inside the helpdesk.
Small to mid-sized teams that value flexible seat or conversation pricing and a broad support toolkit.
Refund exceptions, complaints, fraud signals and any case where policy needs judgement.
Side-by-side comparison
| Core pricing model | Billable support tickets; AI outcomes billed separately | Per-user plans or conversation-volume plans |
|---|---|---|
| Shopify context | Order history and actions beside the ticket | Customer conversations, order context, chat and proactive cues |
| AI billing | Outcome-based when AI fully resolves the interaction | Included resolutions by seat, then a per-resolution charge |
| Team scaling | Higher plans can suit many agents | Seat plans suit small teams; volume plans allow unlimited staff |
| Best fit | Commerce-heavy support at meaningful volume | Flexible multichannel support with simpler cost choices |
| Main risk | Ticket and automation overages | Seat growth or automation resolution charges |
Use your support workload to evaluate the platform
A helpdesk decision changes how your team handles order questions, refunds, social messages and product advice every day. The right comparison begins with a month of real conversations. Count ticket volume, channels, agent seats, routine questions, escalations and the Shopify actions agents perform after reading a message.
Gorgias and Re:amaze both serve e-commerce support. The difference shows up in pricing mechanics and how far you want the helpdesk to reach into shopping and order operations.
Gorgias is built for commerce actions at scale
Gorgias puts Shopify order history, shipping details and customer data beside the ticket. Agents can take actions such as refunds and cancellations without leaving the helpdesk, subject to the permissions and rules the brand sets. Its AI Agent also uses store data, help-centre content and custom guidance to answer and act across supported channels.
The pricing model deserves careful modelling. Gorgias charges for billable helpdesk tickets, then charges an additional outcome-based fee when AI Agent fully resolves a conversation. That can align cost with support volume, but a brand with seasonal spikes needs to understand included usage and overages before peak season.
- Measure average monthly and peak ticket volume separately.
- List the order actions agents perform and the permissions each action needs.
- Set a hard handover rule for refunds, complaints and policy exceptions.
Re:amaze gives teams more ways to shape cost
Re:amaze combines shared inboxes, live chat, social channels, FAQ, proactive messages, chatbots and workflows. It offers user-based plans with unlimited conversations as well as volume-based plans with unlimited staff. That choice matters for a five-person team with heavy volume and a twenty-person team where only a few people answer customers.
Its AI Agent uses help-centre content, response templates and connected store data. Current plans include a number of resolutions per user seat, with additional resolutions billed separately. A team should compare the likely AI resolution count with the classic workflow and chatbot features it can run without handing every case to a generative agent.
AI resolution rate is one part of support quality
A high automation percentage can hide weak customer experience if the bot closes easy tickets while customers reopen confusing ones. Track repeat contact, escalation quality, refund leakage, customer satisfaction and the time agents spend reconstructing context after a handover.
The useful unit is a correctly resolved customer job. Order-status questions can often run end to end. A damaged product complaint may need empathy, evidence and policy judgement. Product advice can be automated only when catalogue data and guidance are reliable enough to prevent confident mistakes.
Run a two-week workflow proof before migrating the inbox
Export a representative set of conversations and tag them by intent. Build the three most common safe workflows in the candidate platform. Test the answer, data source, Shopify action, escalation packet and audit trail. Ask agents to score whether the handover saves time or creates cleanup work.
HollerLabs builds the process around the helpdesk: data retrieval, response rules, approval boundaries, escalation and reporting. That is useful whether the selected interface is Gorgias or Re:amaze. The platform holds conversations; the operating design decides which work can run safely and what the human receives when it cannot.
Frequently asked questions
Is Gorgias better than Re:amaze for Shopify?
Gorgias is often better for high-volume Shopify support that needs deep order context and commerce actions. Re:amaze can be better for teams that prefer flexible seat or volume pricing and a broad multichannel toolkit.
How do Gorgias and Re:amaze price AI support?
Gorgias uses outcome-based billing when AI fully resolves an interaction, in addition to helpdesk ticket usage. Re:amaze includes a number of AI resolutions by user plan and charges for additional resolutions; its volume plans use a flat per-resolution model.
Can either platform automate refunds?
Both can support commerce workflows, but refund authority should follow explicit value, policy and fraud rules. Unusual or sensitive cases should include a human approval step.
What should we test before switching helpdesks?
Test your top intents, Shopify data accuracy, escalation quality, permissions, reporting and cost at peak volume. A polished sales walkthrough is not evidence that the workflow will hold up.
Sources and freshness
Product details and pricing models were checked on August 27, 2026. Vendors change plans and features, so confirm the live terms before buying or migrating.