Regal is an AI voice agent platform built for B2C contact centers. Bland AI is an enterprise phone agent platform with API-first developer tooling.
Major takeaways
Who should pick Regal over Bland AI? Teams running high-volume B2C contact centers that need native CRM integrations, campaign orchestration, and compliance tooling for regulated industries should evaluate Regal first.
Who should pick Bland AI over Regal? Developers building custom voice products or teams that want model flexibility, transparent per-minute pricing, and API-first control should start with Bland AI.
How do their pricing models differ? Bland AI publishes per-minute rates starting at $0.09 per minute; Regal requires custom quotes and reportedly structures pricing around seat licenses plus usage, though public details are limited.
If you care about X, pick Y
| If you care about | Pick | Why |
|---|---|---|
| Transparent pricing | Bland AI | Publishes per-minute rates on its website; Regal requires a sales call for pricing details. |
| No-code campaign builder | Regal | Has a visual campaign builder for contact-center teams; Bland AI is API-first and requires code. |
| LLM and voice provider choice | Bland AI | Supports bring-your-own LLM and multiple TTS providers; Regal's model stack is not publicly documented. |
| Native CRM integrations | Regal | Built for Salesforce and HubSpot workflows; Bland AI requires custom webhook integration. |
| Developer control and flexibility | Bland AI | API-first platform with extensive function-calling documentation; Regal is packaged for operations teams. |
| Enterprise compliance documentation | Either | Both state SOC 2 Type II; HIPAA and GDPR claims should be verified directly with each vendor. |
How we evaluated these platforms
This comparison draws on vendor documentation from Regal's website, Bland AI's pricing page, and Bland AI's developer docs, as well as review coverage on G2 and TrustRadius. We evaluated build experience, telephony options, pricing transparency, model flexibility, compliance statements, and integration depth.
No test calls were placed and no agents were built for this review. Pricing and feature availability reflect what each vendor published as of October 2026. Public review volume for Regal is limited; themes are drawn from available coverage.
What each platform does
Bland AI
Bland AI is a developer-focused AI phone agent platform that handles inbound and outbound calls via API. Teams build agents by defining prompts, tools, and voice parameters in code, then deploy via REST API or webhook. Bland AI supports multiple LLM providers (OpenAI, Anthropic, bring-your-own) and TTS engines (ElevenLabs, PlayHT, Azure).
The platform is built for developers who want control over the call flow and model stack. Bland AI states it processes millions of calls per month across customer support, appointment booking, and outbound sales use cases, though specific customer counts are not published. Source: Bland AI docs
Regal
Regal is an AI voice agent platform designed for B2C contact centers. It combines outbound dialing, inbound call routing, and SMS in a single platform, with native integrations into Salesforce and HubSpot.
Contact-center teams use Regal's visual campaign builder to orchestrate multi-touch sequences (calls, texts, emails) without code. Regal states it serves brands in financial services, healthcare, home services, and insurance, with customers including LendingTree and Ro reportedly using the platform for lead follow-up and customer engagement. The platform is built for operations teams managing high call volumes rather than developers building custom voice products. Source: Regal website
Feature comparison
| Capability | Regal | Bland AI |
|---|---|---|
| Build model | Visual campaign builder, no-code | API-first, requires code |
| Inbound calls | ✓ | ✓ |
| Outbound calls / campaigns | ✓ Native campaign orchestration | ✓ API-triggered calls |
| Telephony | Bundled numbers (carrier not specified) | Twilio integration, bring-your-own SIP |
| LLM choice | Not documented | ✓ OpenAI, Anthropic, bring-your-own |
| Voice / TTS provider choice | Not documented | ✓ ElevenLabs, PlayHT, Azure, voice cloning |
| Languages | Not documented | 30+ languages per vendor claim |
| Interruption handling | Not documented | ✓ Configurable barge-in settings |
| Warm transfer / human handoff | ✓ | ✓ |
| Function calling / tool use | ✓ CRM lookups, booking | ✓ Custom webhooks, API calls |
| CRM and calendar integrations | ✓ Native Salesforce, HubSpot | Limited; requires custom webhooks |
| Call recording, transcripts, analytics | ✓ | ✓ Transcripts, recordings, logs via API |
| Compliance statements | SOC 2 Type II (per vendor) | SOC 2 Type II (per vendor) |
| Concurrency limits | Not documented | 1,000+ concurrent calls (per vendor) |
Sources: Regal website, Bland AI docs, Bland AI pricing page
Pricing and cost per minute
Bland AI pricing
Bland AI publishes per-minute pricing on its website. The Developer tier starts at $0.09 per minute for the first 1,000 minutes per month, then $0.07 per minute above that threshold. The Growth tier drops to $0.05 per minute for the first 10,000 minutes, then $0.04 per minute beyond. The Enterprise tier has custom rates for teams processing over 100,000 minutes per month.
All tiers include access to the API, webhooks, and call logs. The per-minute rate covers the Bland AI platform fee, LLM inference, and TTS; telephony (Twilio or bring-your-own carrier) is billed separately at the carrier's rate, typically $0.01–$0.02 per minute for US calls. No platform fee or seat licenses are listed. Source: Bland AI pricing page
| Tier | Per-minute rate (first tier) | Per-minute rate (overage) | Minimum commitment |
|---|---|---|---|
| Developer | $0.09 | $0.07 | None |
| Growth | $0.05 | $0.04 | None |
| Enterprise | Custom | Custom | Custom contract |
Regal pricing
Regal does not publish pricing on its website. Third-party sources estimate that Regal's pricing model includes a platform fee (reportedly in the range of $1,000–$3,000 per month for small teams) plus per-seat licenses and usage-based charges for call minutes and SMS sends. The all-in cost per minute is not publicly documented.
Regal's sales team provides custom quotes based on expected call volume, number of users, and integration requirements. Contract terms and minimums are not disclosed. Teams evaluating Regal should request a detailed pricing breakdown that separates platform fees, seat costs, and per-minute charges. Source: third-party estimates; Regal does not publish pricing
| Component | Estimated range | Source |
|---|---|---|
| Platform fee | $1,000–$3,000/month | Third-party estimates |
| Per-seat license | Not disclosed | Custom quote required |
| Per-minute cost | Not disclosed | Custom quote required |
What a month of calls might cost
To estimate monthly spend on either platform, use this formula:
Monthly cost = (average call length in minutes × calls per month × per-minute rate) + platform fees + telephony costs
For Bland AI, assume:
- Average call length: 3 minutes (illustrative assumption; replace with your actual data)
- Calls per month: 5,000 (illustrative assumption)
- Per-minute rate: $0.05 (Growth tier, first 10,000 minutes)
- Telephony cost: $0.015 per minute (Twilio estimate)
Calculation: (3 × 5,000 × $0.05) + (3 × 5,000 × $0.015) = $750 (Bland AI) + $225 (Twilio) = $975 per month
For Regal, the calculation requires a custom quote. If Regal quotes $0.08 per minute all-in (hypothetical) plus a $2,000 platform fee:
Calculation: (3 × 5,000 × $0.08) + $2,000 = $1,200 (usage) + $2,000 (platform) = $3,200 per month
These are illustrative examples only. Actual costs depend on negotiated rates, call patterns, and contract terms. Request a detailed quote from each vendor with your expected volume before committing.
What users say about these platforms
Bland AI user feedback
Public review coverage for Bland AI is limited at the time of writing. Developer-focused voice platforms often have thin G2 coverage because users are technical teams rather than business buyers.
Available feedback on Reddit and developer forums highlights the platform's API flexibility and transparent pricing as strengths. Some users note that building a production-ready agent requires significant prompt engineering and error-handling logic. Latency and interruption handling are frequently cited as areas where performance depends heavily on the LLM and TTS provider chosen by the developer.
Regal user feedback
Public review volume for Regal is limited; the recurring themes below are drawn from the available G2 / Trustpilot / TrustRadius coverage. Contact-center teams report that Regal's native Salesforce integration and campaign orchestration reduce manual work compared to stitching together separate dialing and CRM tools.
Some users note that the platform's pricing model and contract terms are less transparent than competitors, and that onboarding requires dedicated support from Regal's team. Call quality and agent performance are described as dependent on the quality of the underlying data and the complexity of the use case.
Strengths and gaps
What Regal does well
Native CRM integrations. Regal connects directly to Salesforce and HubSpot, syncing call outcomes, contact updates, and campaign triggers without custom middleware. Contact-center teams report this reduces integration overhead.
Campaign orchestration. The platform's visual builder lets operations teams design multi-touch sequences (calls, SMS, emails) in one interface, which simplifies workflows for high-volume B2C outreach.
Compliance tooling for regulated industries. Regal states it supports TCPA-compliant calling workflows, including consent tracking and do-not-call list management, though teams should verify specific compliance requirements with the vendor.
Where Regal has gaps
Pricing transparency. Regal does not publish pricing, which adds friction to the procurement process and makes it harder to compare all-in costs against competitors.
Developer flexibility. The platform is built for operations teams using the visual builder; developers who want API-level control over call flows and model selection will find Regal's architecture less flexible than API-first alternatives.
Public documentation. Regal's technical documentation is not publicly available, which limits the ability to evaluate model choice, latency characteristics, and telephony options without engaging the sales team.
What Bland AI does well
Transparent pricing. Bland AI publishes per-minute rates on its website, which lets teams estimate costs before a sales call and compare pricing across vendors.
Model and voice flexibility. The platform supports multiple LLM providers (OpenAI, Anthropic, bring-your-own) and TTS engines (ElevenLabs, PlayHT, Azure), giving developers control over latency, cost, and voice quality trade-offs.
API-first architecture. Bland AI's REST API and webhook system let developers build custom call flows, integrate with internal tools, and automate call triggers without relying on a visual builder.
Where Bland AI has gaps
No native CRM integrations. Bland AI requires custom webhook integration to sync call data with Salesforce, HubSpot, or other CRMs, which adds engineering work compared to platforms with pre-built connectors.
Code required. Teams without engineering resources cannot build agents on Bland AI; the platform is designed for developers rather than no-code operations teams.
Limited campaign orchestration. Bland AI handles individual call triggers via API but does not include a built-in campaign builder for multi-touch sequences, which means teams must build that logic externally.
Which use cases fit each platform
| Use case | Stronger fit | Why |
|---|---|---|
| Inbound lead qualification and speed-to-lead | Bland AI | API-first architecture lets developers integrate call triggers directly into web forms and CRM workflows; Regal's strength is campaign orchestration rather than real-time inbound routing. |
| Appointment booking and reminders | Either | Both platforms support calendar integrations and outbound calling; choice depends on whether the team prefers a visual builder (Regal) or API control (Bland AI). |
| After-hours reception and call routing | Bland AI | Developers can build custom routing logic and integrate with internal systems; Regal's focus is B2C campaigns rather than general-purpose reception. |
| Outbound sales calling at scale | Regal | Native campaign orchestration and CRM sync simplify high-volume B2C outreach; Bland AI requires external tooling to manage call lists and follow-up sequences. |
| Customer-support deflection | Either | Both platforms handle inbound calls and warm transfers; Regal's native integrations suit contact centers, while Bland AI's API flexibility suits custom support workflows. |
| High-volume contact-center overflow | Regal | Built for contact-center operations with native dialing, routing, and compliance tooling; Bland AI is better suited to developer-built products than packaged contact-center solutions. |
| Developer building a voice product | Bland AI | API-first platform with model flexibility and transparent pricing; Regal is designed for operations teams using the visual builder rather than developers building custom products. |
Pick Regal if you...
Run a B2C contact center and need native Salesforce or HubSpot integration without custom middleware.
Want a visual campaign builder that lets operations teams design multi-touch sequences (calls, SMS, emails) without code.
Operate in a regulated industry (financial services, healthcare, insurance) and need compliance tooling for TCPA, consent tracking, and do-not-call management.
Prefer a packaged solution with dedicated onboarding and support rather than building call flows via API.
Pick Bland AI if you...
Have engineering resources and want API-level control over call flows, model selection, and voice parameters.
Need transparent per-minute pricing to compare costs across vendors before engaging a sales team.
Want flexibility to bring your own LLM (Anthropic, OpenAI, self-hosted) and TTS provider (ElevenLabs, PlayHT, Azure) rather than using a vendor-locked stack.
Are building a custom voice product or integrating AI calling into an existing application rather than running a standalone contact center.
Note: if the calls you're automating are inbound sales conversations that need to sit alongside an outbound AI SDR in one platform, teams in that position typically also evaluate 11x's Julian.
Verdict: which platform is the stronger fit for your team
Regal and Bland AI serve different buyers.
Regal is built for B2C contact-center teams that want a packaged solution with native CRM integrations, campaign orchestration, and compliance tooling. The platform's visual builder lets operations teams design multi-touch sequences without code, and its Salesforce and HubSpot connectors reduce integration overhead. The trade-off is pricing opacity and limited flexibility for teams that want API-level control or model choice. Regal is the stronger fit for contact centers running high-volume outbound campaigns in regulated industries where compliance tooling and native integrations justify the platform fee and custom pricing model.
Bland AI is built for developers who want API-first control, transparent pricing, and model flexibility. The platform's support for multiple LLM and TTS providers lets teams optimize for latency, cost, and voice quality, and its published per-minute rates make cost comparison straightforward. The trade-off is that Bland AI requires code to build agents and does not include native CRM integrations or campaign orchestration tooling. Bland AI is the better starting point for technical teams building custom voice products, integrating AI calling into existing applications, or running inbound qualification workflows where API flexibility and transparent economics matter more than a visual builder.
Before committing to either platform, build the same short test flow on both (a simple inbound qualification script or outbound reminder call), measure the all-in per-minute cost at your expected volume (including telephony, LLM, and TTS), and confirm that each vendor's compliance documentation meets your industry requirements. Request a detailed pricing breakdown from Regal that separates platform fees, seat costs, and per-minute charges, and verify Bland AI's latency and call-quality performance with your chosen LLM and TTS provider before scaling to production.
FAQ
Is Regal cheaper than Bland AI?
It depends on call volume, contract terms, and the all-in per-minute rate Regal quotes. Bland AI publishes per-minute rates starting at $0.09 per minute (Developer tier) and dropping to $0.05 per minute (Growth tier), plus telephony costs of roughly $0.01–$0.02 per minute. Regal does not publish pricing; third-party estimates suggest a platform fee of $1,000–$3,000 per month plus per-seat licenses and usage charges, though the per-minute rate is not disclosed. Teams processing fewer than 10,000 minutes per month may find Bland AI's transparent pricing more cost-effective, while high-volume contact centers negotiating enterprise contracts with Regal should compare the all-in cost per minute directly.
Which is easier to build with if I can't code?
Regal is easier for non-technical teams. The platform's visual campaign builder lets operations teams design call flows, set triggers, and configure multi-touch sequences without writing code. Bland AI is API-first and requires developers to define prompts, tools, and call logic in code. Teams without engineering resources should evaluate Regal or other no-code voice platforms rather than Bland AI.
Can I bring my own phone numbers or carrier to each?
Bland AI supports bring-your-own SIP trunking and integrates with Twilio for telephony, which lets teams use existing phone numbers or negotiate carrier rates directly. Regal bundles telephony with the platform; the carrier and number-porting process are not publicly documented. Teams with existing phone infrastructure or carrier contracts should confirm porting and SIP support with Regal's sales team before committing.
Which handles outbound calling campaigns better?
Regal is built for outbound campaign orchestration. The platform's visual builder lets contact-center teams design multi-touch sequences (calls, SMS, emails), set triggers based on CRM data, and manage call lists without code. Bland AI handles individual outbound calls via API but does not include a built-in campaign builder; teams must build call-list management, retry logic, and multi-touch sequencing externally. For high-volume B2C outbound campaigns, Regal's native tooling is the stronger fit. For API-triggered outbound calls integrated into custom workflows, Bland AI has more flexibility.
Are Regal and Bland AI HIPAA compliant?
Both vendors state they are SOC 2 Type II certified. HIPAA compliance depends on whether the vendor will sign a Business Associate Agreement (BAA) and how the platform is deployed. Regal's website mentions healthcare customers and compliance tooling, but specific HIPAA documentation is not publicly available. Bland AI's compliance page does not list HIPAA certification. Teams in healthcare should request BAA documentation and a detailed compliance review from each vendor before processing protected health information (PHI) on either platform.
Can either transfer a call to a human?
Yes. Both platforms support warm transfers to human agents. Regal's contact-center tooling includes native call-routing and transfer workflows. Bland AI supports warm transfers via API, letting developers route calls to external phone numbers or SIP endpoints based on custom logic. The quality of the transfer experience (hold music, context passed to the human agent, transfer latency) depends on how each platform is configured and integrated with the contact center's existing phone system.
What do users complain about most?
Public review coverage for both platforms is limited. For Regal, available feedback notes that pricing transparency is lower than competitors and that onboarding requires dedicated support from the vendor's team. Some contact-center teams report that call quality and agent performance depend heavily on the quality of the underlying CRM data and the complexity of the use case. For Bland AI, developer feedback highlights that building a production-ready agent requires significant prompt engineering and error-handling logic, and that latency and interruption handling vary depending on the LLM and TTS provider chosen. Teams evaluating either platform should request customer references and test the platform with their specific use case before committing.
Which platform has better voice quality?
Voice quality depends on the TTS provider and telephony setup. Bland AI supports multiple TTS engines (ElevenLabs, PlayHT, Azure), which lets developers choose the provider that best fits their latency and naturalness requirements. Regal's TTS provider is not publicly documented. Both platforms use standard telephony protocols (SIP, Twilio), so call clarity also depends on network conditions and carrier quality. Teams should test both platforms with their expected call scenarios to compare voice quality directly.
Can I use these platforms outside the US?
Bland AI states it supports 30+ languages and integrates with Twilio, which has global telephony coverage. Regal's language support and international telephony options are not publicly documented. Teams operating outside the US should confirm language support, local phone-number availability, and carrier coverage with each vendor before committing.
How does 11x's Julian compare to Regal and Bland AI?
Julian is 11x's AI phone agent for inbound speed-to-lead and qualification, sold as part of the 11x platform alongside Alice (11x's outbound AI SDR). Julian is a packaged sales agent rather than a general-purpose voice-building platform, so teams building custom voice products or running contact-center operations typically compare it less directly to Regal or Bland AI. Julian is the stronger fit for revenue teams that want inbound voice and outbound email in one system; Regal and Bland AI are better starting points for contact-center teams or developers building standalone voice products. For more on Julian's positioning, see 11x's Julian page.
