Sold by: Bland
Deployed on AWS
Bland is the voice AI platform trusted with the world's highest-stakes phone calls. We're the only voice AI platform that's entirely self-hosted, providing the security, reliability, and predictability your hardest calls demand. Built for regulated industries, Bland handles inbound and outbound calls end-to-end, so you can scale customer support without risking a costly mistake.
4.9
Overview
Bland automates high-stakes phone calls for regulated companies. We're the only voice AI platform that is entirely self-hosted, allowing us to deliver the security, reliability, and predictability that the hardest calls require.
Create agents that handle every kind of conversation. Deploy them on phone, SMS, and web, and connect them to any system. See exactly why your agent made each decision, so you can diagnose and fix things fast. Make your agent better without risking your live calls, by testing every change safely.
Unlike platforms that stitch together third-party speech and language models, Bland owns every layer of the stack. That means no added latency from calls traveling between providers, no outside points of failure, and no exposure to unexpected model changes. Pricing is tied to Bland's own compute cost rather than third-party API fees.
Bland serves enterprises in healthcare, insurance, financial services, logistics, and the public sector, including one of the top three banks in the US. Average implementation time is five weeks. Contact Bland to discuss your use case, implementation requirements, and enterprise pricing.
Highlights
- Fully self-hosted. Bland runs its own STT, LLM, and TTS instead of relying on third-party providers. Calls route to the server closest to the caller, and the platform is load tested at 100,000+ concurrent calls.
- Built for regulated industries. Bland is FedRAMP, HIPAA, SOC 2 Type II, GDPR, and PCI DSS compliant. Call data never leaves Bland's infrastructure or trains outside models.
- White-glove support: Get a dedicated team of engineers building your models from the ground up. No technical experience needed on your end, just hand us your existing call flow.
Details
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Delivery method
Deployed on AWS
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AWS Marketplace now accepts line of credit payments through the PNC Vendor Finance program. This program is available to select AWS customers in the US, excluding NV, NC, ND, TN, & VT.
Pricing
Pricing is based on the duration and terms of your contract with the vendor, and additional usage. You pay upfront or in installments according to your contract terms with the vendor. This entitles you to a specified quantity of use for the contract duration. Usage-based pricing is in effect for overages or additional usage not covered in the contract. These charges are applied on top of the contract price. If you choose not to renew or replace your contract before the contract end date, access to your entitlements will expire.
Additional AWS infrastructure costs may apply. Use the AWS Pricing Calculator to estimate your infrastructure costs.
Dimension | Description | Cost/12 months |
|---|---|---|
Enterprise Platform - Placeholder; Contact Bland for Pricing | The $1 annual amount is a placeholder for listing setup and is not live or active commercial pricing for Bland services. Contact Bland before purchasing to receive a private offer with agreed platform fees, usage rates, and service scope. | $1.00 |
Dimension | Cost/unit |
|---|---|
Per Connected Minute Pricing Placeholder (Contact Bland for Enterprise Pricing) | $0.30 |
Dimensions summary
This listing has two placeholder dimensions, not live commercial pricing. One sets a per-connected-minute rate, where you pay for the time your AI voice agent spends on calls. The other is an enterprise platform fee. The listed amounts are placeholders for setup only. You contact Bland before purchasing to receive a private offer. That offer defines your platform fees, per-minute usage rates, and service scope, sized to your call volume. Telephony is billed separately. Both dimensions work together as a negotiated contract rather than fixed public pricing.
Top-of-mind questions for buyers
The per-minute rate covers the AI processing during live calls: the language model, speech-to-text, and text-to-speech combined into one number. You pay for the time your voice agent spends connected on a call. There are no separate token charges or model-provider pass-through fees.
Telephony is billed separately from the AI per-minute rate. You can connect your own carrier or telephony provider and handle carrier costs directly. Alternatively, you can use Bland's built-in telephony at pass-through cost. Bring-your-own-telephony customers do not pay transfer fees.
Both dimensions bill independently and appear together. The platform fee is a recurring enterprise charge negotiated in your private offer. The per-minute rate accrues based on actual call time. For high call volumes, the per-minute usage typically drives most of the cost. Both amounts are sized to your volume.
www.bland.ai
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Delivery details
Software as a Service (SaaS)
SaaS delivers cloud-based software applications directly to customers over the internet. You can access these applications through a subscription model. You will pay recurring monthly usage fees through your AWS bill, while AWS handles deployment and infrastructure management, ensuring scalability, reliability, and seamless integration with other AWS services.
Resources
Vendor resources
Support
Vendor support
For technical assistance, sign in to your Bland account and submit a request through the support form at https://app.bland.ai/dashboard/support . Product documentation and implementation guides are available at https://docs.bland.ai . Enterprise customers can also work with their assigned Forward Deployed Engineering team and dedicated customer Slack channel for implementation and ongoing support.
AWS infrastructure support
AWS Support is a one-on-one, fast-response support channel that is staffed 24x7x365 with experienced and technical support engineers. The service helps customers of all sizes and technical abilities to successfully utilize the products and features provided by Amazon Web Services.
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Customer reviews
Jennifer E.
Bland is a One-Stop-Shop for Voice AI and an Industry Leader in this space.
Reviewed on May 28, 2026
Review provided by G2
What do you like best about the product?
The product is intuitive to use once you understand the mental model for how they have built it. In addition, the engineering team has been responsive with support help and bug fixes as well as feature requests. I should note that we have an Enterprise agreement with Bland. They integrate with Slack, Calendly and other tools, natively. And the custom code nodes are unique to Bland, allowing you to build high performance data transformations as run time.
What do you dislike about the product?
Getting started is hard, but Bland has built Norm, an AI Agent, which allows you to "vibe code" your first pathway. This is super helpful for new builders. But you'll quickly outgrow Norm in its current version. I know they'll keep adding to it, but you'll likely end up using Claude Code to help you build pathways. Bland has made available an MCP server and some Claude Skills, which are dated a bit, but with a bit of work and access to Bland's llm.txt file which is made for agents like Claude Code () you'll be up and running fairly quickly. One thing. If you bring your own Twilio numbers you will save money on telephony charges, but Bland number natively support more capabilities. I'll leave it at that. You can pay a bit more to use Bland's phone numbers and you'll make your life easier. But if you do a lot of dials, its worth it to BYOT.
What problems is the product solving and how is that benefiting you?
We do outbound lead generation calls. This means we first have to get a lead's permission to call them, which we do on our website. Once we have that information we generate a call to them to connect and qualify the lead. If successful, we might transfer the call, book a meeting, etc. So this requires a lot of orchestration, and is a complicated use case. We have several pathways to achieve the results we need. We do not do the tradition inbound calling for appointment rebooking, or technical support. We do a lot of experimentation and have integrated with Optimizely for A/B testing to more rapidly experiment. I know that Bland is looking to enable A/B experimentation but it did not exist a year ago when we started with them and it is still weak right now. In addition their QA capabilities need a bit of work. But they are actively developing this now.
Financial Services
Enterprise-Level Features That Work for Businesses of Any Size
Reviewed on May 13, 2026
Review provided by G2
What do you like best about the product?
Enterprise level features that are usable by businesses of all sizes
What do you dislike about the product?
There are a lot of product features you don't need to sue to get going and productive but sometimes the 'new features" will distract you into trying to implement them.
What problems is the product solving and how is that benefiting you?
Consistent communication across all channels (Voice, SMS, Webchat), scalable and available 24/7, means fewer scheduling dramas for staff and a more consistent, higher-quality communication experience for customers.
Usman J.
From Zero to Voice Escalation in Hours with Bland AI
Reviewed on Apr 28, 2026
Review provided by G2
What do you like best about the product?
What I like best about Bland AI is how quickly it lets you turn real-time events into reliable voice interactions.
In my hackathon project, Sift, I used it to trigger outbound calls when high-severity product feedback was detected, deliver context about the issue, and capture structured responses from free-form speech. That entire loop would normally require stitching together telephony, speech-to-text, and orchestration, but Bland handled it cleanly.
The biggest advantage was speed. I was able to go from zero to a working voice escalation system in a few hours and focus on the learning loop and decision logic instead of infrastructure.
Norm also stood out. It made designing the conversation flow much more natural, which improved how users responded during calls and made the system actually usable, not just a demo.
In my hackathon project, Sift, I used it to trigger outbound calls when high-severity product feedback was detected, deliver context about the issue, and capture structured responses from free-form speech. That entire loop would normally require stitching together telephony, speech-to-text, and orchestration, but Bland handled it cleanly.
The biggest advantage was speed. I was able to go from zero to a working voice escalation system in a few hours and focus on the learning loop and decision logic instead of infrastructure.
Norm also stood out. It made designing the conversation flow much more natural, which improved how users responded during calls and made the system actually usable, not just a demo.
What do you dislike about the product?
One challenge I ran into with Bland AI was controlling and structuring responses from free-form speech during calls.
In Sift, I needed to reliably turn a founder’s natural response into a clear action, like “create an issue” or “ignore this type of signal.” While it worked, getting consistent, structured outputs required careful prompt design and iteration.
I also found that debugging voice interactions is harder than typical API flows. When something goes wrong, it’s less obvious whether the issue is prompt design, transcription, or conversation flow, so tighter feedback loops or tooling for inspecting call-level decisions would help.
Overall, these aren’t blockers, but improving observability and structured response handling would make it even more powerful for building production-grade voice agents.
In Sift, I needed to reliably turn a founder’s natural response into a clear action, like “create an issue” or “ignore this type of signal.” While it worked, getting consistent, structured outputs required careful prompt design and iteration.
I also found that debugging voice interactions is harder than typical API flows. When something goes wrong, it’s less obvious whether the issue is prompt design, transcription, or conversation flow, so tighter feedback loops or tooling for inspecting call-level decisions would help.
Overall, these aren’t blockers, but improving observability and structured response handling would make it even more powerful for building production-grade voice agents.
What problems is the product solving and how is that benefiting you?
Bland AI is solving the problem of turning voice into a reliable, programmable interface rather than a fragmented stack of tools.
Traditionally, building anything voice-driven means stitching together telephony, speech-to-text, text-to-speech, and conversation logic, with a lot of edge cases and failure points. That makes it slow to build and hard to maintain, so most teams avoid using voice for core product workflows.
Bland abstracts that complexity into a single system where you can trigger calls, control the interaction, and capture usable outputs.
In my project, Sift, this directly enabled a voice-first escalation loop. Instead of just sending alerts, the system calls the user when something critical happens, explains the context, and captures their decision in real time.
The benefit is leverage. I was able to build a full feedback-to-action loop in a few hours that would normally take days or weeks, and it made the product fundamentally more proactive instead of passive.
Traditionally, building anything voice-driven means stitching together telephony, speech-to-text, text-to-speech, and conversation logic, with a lot of edge cases and failure points. That makes it slow to build and hard to maintain, so most teams avoid using voice for core product workflows.
Bland abstracts that complexity into a single system where you can trigger calls, control the interaction, and capture usable outputs.
In my project, Sift, this directly enabled a voice-first escalation loop. Instead of just sending alerts, the system calls the user when something critical happens, explains the context, and captures their decision in real time.
The benefit is leverage. I was able to build a full feedback-to-action loop in a few hours that would normally take days or weeks, and it made the product fundamentally more proactive instead of passive.
Cameron O.
Bland AI: Flexible API, Exceptional Speed
Reviewed on Apr 23, 2026
Review provided by G2
What do you like best about the product?
I love using Bland AI for both my personal and client work, especially helping clients get voice AI running smoothly because Bland AI offers high performance and low latency. Having tested many voice platforms, I think Bland AI is the fastest in terms of latency and the most comprehensive. I love that I can initiate calls within Bland directly from the API without needing to use the visual node pathway builder. The API is my favorite aspect of Bland AI because it's really flexible, allowing me to use it for so many different things like sending custom voicemails, creating custom pathways, and configurations. This flexibility makes it so much easier to build custom systems for clients who don't want to have to learn a new platform themselves. I appreciate the ability to programmatically set up and get clients started seamlessly. Bland AI's programmability of the API allows me to integrate it with virtually any systems, especially helpful for legacy telephony. It gives me total freedom and creativity in my work.
What do you dislike about the product?
I would say that there's definitely some things that have been blockers in the past. The thing that was a major blocker for me is SIP trunking not being available on the standard plans, only on enterprise - but this has sense been opened up. There was a point where v2 voices were kind of singing on calls instead of speaking, like they were doing a little bit of voice hallucination - this too was fixed quite quickly. The team is super responsive. Any complaints that I've had about Bland in terms of things not working correctly, they've been fixed or mitigated very quickly. They have a great team over there.
What problems is the product solving and how is that benefiting you?
I use Bland AI for personal and client work to quickly set up high performance, low latency voice AI. The flexible API allows for custom configurations and integrating with existing systems, making management seamless and keeping latency low.
Robert V.
A super solid voice AI stack that actually keeps up
Reviewed on Apr 19, 2026
Review provided by G2
What do you like best about the product?
I really appreciate how Bland AI simplifies so many things and takes a lot of work off our shoulders. The customer support is great and the team is super open to directly jump on a quick call and resolve issues super fast. They are competent and always available whenever something important comes up. Additionally, I value that they constantly ship new features that genuinely improve the product and bring more value, rather than just for the sake of shipping. The onboarding is incredibly easy, and the support is just great. We have a Slack channel with people taking care of us, answering questions, and jumping on calls. It was incredible to have the CEO or CTO available, something not common with many companies. The guys from Bland and not only incredibly good at what they are doing but also willing to share their knowledge to help you solve some of your issues. If you'd like feel free to call me under the following number if you have any doubts / questions: (415) 503-7468
What do you dislike about the product?
Not much at all to be fully honest. Maybe only that they're so good that it's hard to leave Bland behind :) One thing that comes to my mind is that it's quite hard to understand what happens under the hood. You get strong results but it's not always fully clear how outputs are generated, which makes fine-tuning more iterative than it could be. However, Bland is already a few steps ahead and shipping automated testing tools, which make your voice AI bulletproof.
What problems is the product solving and how is that benefiting you?
I use Bland AI to handle deployment and build voice agents, simplifying complex order flows and speech-to-text processes. It connects easily with phone providers like Twilio, taking work off our shoulders and providing an understandable infrastructure.