We’ve helped hundreds of customers implement voice AI, many who came to us after trying to build it themselves or deploying other solutions. This guide breaks down what a real-world voice AI implementation actually entails, the tradeoffs between building in-house, buying a managed service, or using a developer-first platform, and where the hidden complexity tends to surface.
What this doc covers:
- The voice AI landscape and why infrastructure ownership is the single most important differentiator
- What a voice AI stack actually requires beyond the model layer
- Building on open source
- Using a managed service
- Using a developer platform
- An honest side-by-side comparison
The Voice AI Landscape
The most important question to ask any voice AI vendor is: do you actually own and host the voice AI infrastructure, or are you reselling someone else's?
This is the fundamental dividing line in the market right now, and it matters far more than most buyers realize.
What a self-hosted stack actually means. A voice AI call has three model layers: transcription (speech-to-text), inference (the language model), and TTS (text-to-speech). On top of that sits an orchestration server that manages the conversation flow, handles telephony, and coordinates everything in real time. A vendor that self-hosts all of this owns the full pipeline. When a customer buys from that vendor, they can get their own dedicated orchestration server and dedicated GPUs for the model infrastructure. The customer literally owns their own voice AI. When they want to promote changes, roll back a model version, or test updates against a subset of call volume, they have direct control over that.
What a reseller looks like. Most voice AI vendors today don't own the model infrastructure. They provide an orchestration layer on top of third-party model providers. They're resellers. When you buy from them, your calls are running through someone else's transcription, someone else's language model, and someone else's TTS. You don't have a direct relationship with those providers, and you have no control over them.
Why this matters, concretely. When you're building on top of third-party model infrastructure you don't control:
- The provider can push model updates whenever they want. If a model change degrades your agent's behavior mid-production, you can't roll it back. You're just dealing with it.
- The provider's terms of service can change. Their data handling policies can change. Their moderation policies can change. You have no say in any of that, and your vendor doesn't either.
- If the provider has a data breach, your data was in that pipeline.
- If the provider changes pricing, your vendor's cost structure changes, and so does yours.
- You can't get dedicated infrastructure. You're on a shared, multi-tenant stack that you have zero visibility into.
For highly-regulated companies, this should be disqualifying. They should optimize for self-hosted voice AI where the full infrastructure stack is owned and operated by the vendor you have a direct contract with, where you get dedicated infrastructure, and where model changes happen on your terms.
Bland self-hosts the entire pipeline: transcription, inference, TTS, and orchestration. We trained our own TTS models. We run on bare metal. When a customer buys Bland, they get their own dedicated orchestration server, and we can provision dedicated GPUs for their model infrastructure. They control when changes get promoted. No third-party dependencies, no surprise model updates, no terms-of-service risk from providers they've never talked to.
With that context, here are the three paths.
What a Voice AI Stack Actually Requires
The core pipeline is straightforward: speech-to-text, language model inference, and text-to-speech. You already know what those are.
What's less obvious is everything around that pipeline that a production contact center deployment actually needs: telephony integration (SIP trunking into existing phone systems), call routing and transfer logic, post-call analytics, observability and monitoring, regression testing and QA tooling, guardrails you can define on top of calls, compliance and data handling controls, and an interface for non-engineers to manage and iterate on agent behavior.
The model layer gets all the attention, but the surrounding infrastructure is where most of the ongoing work lives.
Building on Open Source
Using an open-source framework like LiveKit, your team assembles the stack from components. LiveKit handles the real-time audio transport layer and provides some baseline tooling for connecting models to voice streams. You bring your own models and build everything above the transport layer.
Where this makes sense:
If your team is building a product, a novel, vertical voice AI solution that doesn't exist yet, building from components gives you maximum control. If you have a team of voice AI engineers who want to own and evolve a deeply custom system, open source is the right call.
Where it gets hard:
Self-hosting transcription, inference, and TTS models means your team needs to:
- Procure and manage GPU infrastructure, including autoscaling policies (peak hours vs. off-hours), hardware procurement cycles, and capacity planning. At smaller call volumes, the unit economics of dedicated GPUs are punishing.
- Become experts in model serving. Configuring model inference for low-latency voice, where every 100ms matters, is a specific discipline. It's not the same as deploying a chatbot.
- Solve TTS quality. Open-source TTS has improved, but it's still noticeably behind the best proprietary options. Bland had to train its own TTS models to get quality to an acceptable level for production contact centers. If you're building, you're either accepting lower quality or investing heavily here.
- Build the platform layer from scratch. LiveKit gives you audio transport. It does not give you regression testing, post-call analytics, simulation tooling, warm transfer logic, or any of the other operational infrastructure a contact center needs.
- Develop voice AI prompting expertise. Prompting a voice agent is meaningfully different from prompting a text-based LLM. The output has to be functionally correct and sound natural when spoken aloud.
- Maintain it indefinitely. The build isn't a one-time cost. Models need to be updated, infrastructure needs to be patched, and the system needs continuous improvement as call patterns change.
Overall, building makes sense if you're creating a new voice AI product. If you're modernizing a contact center, you're taking on the ongoing cost and complexity of maintaining a voice AI platform as a side project to your actual mission.
Using Managed Services
Many vendors operate on outcomes-based pricing and lean heavily on their own professional services teams to build out your implementation. You're not getting a self-serve platform. You're getting a team that builds for you, and you pay per resolution or per outcome.
Where this makes sense:
If you're a large enterprise that wants to hand off the entire problem, doesn't need deep technical control, and is comfortable with a vendor's team owning the implementation, this model can work.
Where it breaks down:
They don't own the stack. They are wrapping third-party voice models. They're resellers. All of the risks outlined in the landscape section apply here directly: no control over model updates, no ability to roll back, no dedicated infrastructure, and exposure to third-party terms of service and data handling changes.
They're chat-first businesses. Their platforms were architected for chat, and voice is a newer addition. This matters because voice is a harder problem than chat. Latency requirements are tighter, conversational dynamics are more complex, and the quality bar is higher. A platform designed for chat and extended to voice will have different architectural tradeoffs than one built for voice from day one.
Limited developer control. They operate more like a single-prompt agent. You can't design complex, multi-stage conversations, and they don't offer API endpoints for core operations like dispatching outbound calls or pulling call data into your own systems of record. Some may have more of a graph-based interface, but none are built for technical teams that wants to iterate directly on the implementation.
Cost structure. Because these vendors are marking up third-party AI providers and bundling in professional services, the unit economics are significantly higher.
Using a Developer Platform
Bland is a voice-first, developer-first platform. You can sign up and start building for free. Your team owns the implementation directly, with support from our deployment engineers when you want it.
We offer the platform layer for building and designing conversational agents, full observability into every call, guardrails you can define on top of calls, extensive APIs for both inbound and outbound, and your own dedicated voice AI infrastructure. Here's what that looks like in practice.
Dedicated infrastructure per customer. Each customer gets their own dedicated orchestration server, and we can provision dedicated GPUs for model infrastructure. You control when changes get promoted. You can spin up separate containers for testing new versions of the stack, routing a portion of call volume to staging before promoting to production. Model updates happen on your terms.
Fully self-hosted stack. Transcription, inference, TTS, orchestration: all owned and hosted by Bland. We trained our own TTS models. We run our own LLM inference, including for post-call analysis. No third-party dependencies. We run on bare metal and control our own unit economics, which translates to competitive pricing.
VPC and on-prem deployment. We support deploying the full stack within your network boundary.
SIP integration. We connect directly into existing telephony infrastructure via SIP. Your team keeps its own phone systems and telephony rails.
Robust APIs. Everything in the platform can be controlled programmatically. Dispatch outbound calls, pull call data into your own systems of record, manage agents, configure pathways: all available via API.
Conversational Pathways. This is how you build agents on Bland. Pathways let you design conversations as a graph of nodes, where each node represents a phase of the conversation. Within a node, the LLM drives fluid, open-ended conversation. Between nodes, loop conditions define the conditional logic for when the agent moves from one stage to the next. You can execute custom code, make real-time API requests to interact with systems of record, and extract variables throughout the flow. There's also a global prompt that applies across all nodes, so you get holistic behavioral instructions alongside node-specific depth without blowing up your context window.
Observability and Citations. Every interaction is fully observable. You can see which nodes were hit, what decisions the agent made, and why. Post-call analysis cites exact timestamps and source material, so a human can go back and listen to the specific moment in the call to verify any extracted data point.
Guardrails. You can define guardrails on top of calls to ensure agents stay within policy boundaries, handle sensitive topics appropriately, and escalate when needed.
The long tail of production. This is where we differentiate. Getting a voice AI demo working is relatively easy. Getting it into production, and keeping it there reliably, requires handling a long tail of things: warm transfers, live translation, real-time code execution, SMS, long-term memory, appointment scheduling, latency monitoring, and more. We've built for that long tail because it's what our largest customers actually need.
Forward Deployment Engineering (FDE) team. Our deployment team specializes in voice AI agent design and prompting. They support implementation from day one, but this is supplemental to your team's work, not a replacement for it. You own the implementation.
Speed of Iteration: How You Actually Build on Bland
Speed of build-out and iteration is often the deciding factor for technical teams that move quickly, so it's worth describing the actual workflow.
You design your conversation in Pathways, with each phase of the call getting its own node, prompts, and logic. You test individual nodes in isolation using node-level testing. You run simulated calls to validate end-to-end behavior. You deploy, and then you use pathway logs and citations to understand exactly what's happening in production. When you need to make changes, Console (our Cursor-style text interface for pathways) lets you iterate on specific nodes without navigating through the entire graph manually. And regression testing ensures your changes don't break other parts of the agent.
Your team moves at its own pace. You're not waiting on a vendor's professional services team to make changes. You're not debugging a black box. You have full observability, direct control, and the testing infrastructure to iterate confidently.
Comparison
Conclusion
If you're building a voice AI product as a core competency, open-source frameworks give you full control, but are expensive and slow.
If you want to fully outsource the problem, a managed service can work, but you're giving up control, paying a premium, depending on someone else's team to move at your pace, and building on top of third-party model infrastructure you have no relationship with and no control over.
If the goal is to move fast, own the implementation, and have a self-hosted, dedicated voice AI stack under the hood, a developer-first voice platform is the right category. Bland is worth evaluating because we own the full infrastructure stack top to bottom, we're voice-first, we support the deployment models highly-regulated companies need, and we've built for the long tail of things that actually matter in production.
If you’re interested in learning more about how we work with highly-regulated companies, you can book a meeting with us here.
