Bolna AI vs Open Source Voice Agent: Which to Choose?

Bolna AI vs Open Source voice agent is not a comparison article, it is buyer vs builder question which to choose ?

If you are comparing Bolna AI vs an open source voice agent, you are usually deciding between speed (launch fast) and control (own the stack). It is a tussle between hosted setup, vendor managed ops vs self-hosted, no vendor lock in. This guide is written like a buyer memo, with builder-grade details where it matters. We will focus on total cost of ownership (TCO), not hype.

Why this Comparison Matters (Buyers vs Builders)

You can ship a voice agent in days with a hosted platform, or in weeks with open source, then spend months lowering your per-minute cost and improving reliability.

Who this is for: Business teams vs Developer teams

Business teams usually care about:

• Going live fast
• Predictable costs
• Vendor support and SLAs
• "Good enough" customization

Developer teams usually care about:

• Self-hosting and private networking
• BYOK for LLM/STT/TTS
• Deep workflows and custom actions
• Avoiding lock-in and optimizing cost at scale

Most teams can build a working demo. The real challenge begins after that, fixing issues in real calls, handling edge cases, and reducing costs without slowing down the experience.

What is an open source voice agent?

An open source voice agent is a voice calling system where the core software is available under an open source license, so you can inspect it, modify it, and usually self-host it. In practice, open source voice AI depends on telephony, speech-to-text (STT), text-to-speech (TTS), and the LLM.

What we compare (Bolna AI, Dograh AI, Pipecat, LiveKit, Vocode)

Here is what "open source voice agent" means in this post:

• Bolna AI: Managed voice agent platform (hosted product)
• Dograh AI: Open source voice agent platform (builder + calling), cloud-hosted or self-hostable
• Pipecat: Open source pipeline/orchestration framework for real-time voice agents
• LiveKit: Real-time media infrastructure (WebRTC / streaming audio)
• Vocode: Open source agent framework/connectors (build-your-own agent logic)

Quick Recommendation

If you need...	Pick...	Why
Fast launch with minimal engineering	Bolna AI	Hosted setup, vendor-managed ops, fewer moving parts
Maximum privacy/control and self-hosting	Dograh (self-host) or Pipecat/Vocode + LiveKit	You control infra, retention, keys, and network policies
Indian language focus (test Hindi + code-mix)	Open source stack with BYOK STT or Bolna (Sarvam/Pixa integration)	You can choose the best STT for your accents and noise, Bolna integrates regional STT but publishes no WER
Deep customization (multi-agent workflows, custom actions)	Dograh or Pipecat/Vocode	You can build branching workflows and custom integrations without platform limits
Lowest long-term TCO at high volume	Open source + BYOK vendors	Platform markups usually matter at scale, infra + vendor spend can be optimized

Assumption note: "Lowest TCO" depends heavily on call minutes, concurrency, and whether you can run a lean on-call/DevOps rotation.

Myths to ignore (before you choose)

1. "Open source voice AI is free to run." Open source code can reduce license costs, but you still need to pay for compute, STT/TTS, LLM tokens, telephony, storage, and on-call time.
2. "Self-hosting always guarantees privacy." Self-hosting helps, but privacy also depends on your logging, retention, access control, encryption, and vendor contracts for STT/TTS/LLM.
3. "Lowest latency always means best call outcomes." Latency matters, but outcomes also depend on WER (speech accuracy), correct tool actions, and fallback flows when the model is uncertain.

Quick comparison table (side-by-side)

This table is meant to match what buyers search for: setup effort, self-hosting, integrations, scalability, observability, support, and pricing style.

Bolna AI vs Open Source: Setup, Self-hosting, Integrations, Scalability, Pricing, Best for

Factor	Bolna AI (hosted platform)	Dograh AI (open source platform)	Pipecat (framework)	LiveKit (infra)	Vocode (framework)
Setup effort	Low	Low-Medium	Medium-High	Medium	Medium
Time-to-launch	Fast	Fast (especially for dev teams)	Slower	Depends on stack	Depends on stack
Self-hosting	Typically limited vs OSS	Yes (self-hostable)	Yes	Yes	Yes
BYOK (LLM/STT/TTS)	Often supported via integrations (confirm per deployment)	Yes (design goal)	Yes	N/A (media layer)	Yes
Telephony	Platform-managed connectors	"Any telephony" via integrations	You build/plug telephony	Not telephony itself	You build/plug telephony
Integrations (CRM/helpdesk)	Often native + webhooks	Webhooks + build connectors	Build yourself	N/A	Build yourself
Scalability & concurrency	Vendor-managed	Your infra or Dograh cloud	Your infra	Your infra	Your infra
Observability	Platform dashboards (varies)	OSS + planned observability/evals	You build	Metrics at infra layer	You build
Support/SLA	Vendor support	Community + optional services (varies)	Community	Vendor for cloud, self-managed for OSS	Community
Pricing style	Plan fee + per-minute	OSS license + infra + vendor costs	Infra + vendor costs	Cloud pricing or self-host infra	Infra + vendor costs
Best for	Teams that want speed	Teams that want control + builder speed	Engineers building custom pipelines	Real-time streaming backbone	Agent framework + connectors

Assumptions: “Open source” options still require you to pay for telephony, STT, TTS, and the LLM. Scalability and concurrency depend on the media stack and models you choose.

Concrete numbers to include (what to measure)

When you do a real TCO comparison, collect these metrics:

• Time to first working call (hours/days)
• Concurrent calls supported at target latency
• p95 latency (speech stop - agent begins speaking)
• Barge-in behavior (interruptions feel natural or not)
• Language coverage (Hindi + regional, plus code-mixing)
• WER (word error rate) on your own call samples
• Uptime/SLA target and incident response plan
• Per-minute cost split by STT, TTS, LLM, telephony, platform/infra
• Recording + transcript storage cost per month

Real customer proof checklist (ratings, quotes, sources)

Use proof points you can verify, and do not rely on marketing pages alone:

• Hosted platform: Look for pricing page clarity, status page, and independent reviews.
• Open source: Look for GitHub activity, issue resolution speed, and community discussions.
• Practical builder insight: A real-world thread like Building an AI voice agent for my father's restaurant shows a common pattern: hosted tools are simpler for narrow use cases, frameworks (open source) are for control.
• Another builder summary that matches industry reality: self hosted vs hosted discusses the trade-off - hosted gives convenience, self-hosted gives control.

Pricing and total cost of ownership (TCO)

TCO is the gap between “it works once” and “it works reliably every day at a cost you can justify.”

What is total cost of ownership (TCO) for voice agents?

TCO for voice agents is the combined cost of:

• Platform or licensing
• Telephony minutes and phone numbers
• STT + TTS usage
• LLM tokens
• Infrastructure (compute, networking, storage)
• Monitoring and incident response
• Engineering time (build + maintain)

A cheap per-minute headline often becomes expensive once you add recording, retries, long silences, and real support.

Mini glossary (TCO terms you will see)

• Per-minute pricing: Cost tied directly to call minutes.
• Concurrent calls: How many calls run at the same time.
• p95 latency: 95% of turns should be faster than this number.
• Call recording storage: Ongoing storage cost for audio files and transcripts.
• Total cost of ownership (TCO): The full monthly/annual cost including people time.

Bolna AI pricing (what you pay for and hidden costs)

Bolna publishes tiered pricing with included minutes and per-minute rates:

• Starter: 1,000 minutes, $100, $0.10/min
• Pilot: 10,000 minutes, $500-$1,000, $0.05-$0.10/min
• Growth: 4,000 minutes, $250, $0.063/min

What you are paying for (typical for hosted platforms):

• Managed orchestration
• Default integrations and dashboards
• Simplified telephony setup
• A packaged developer experience

Hidden or commonly missed costs to ask about:

• Call recording and playback storage
• Multiple environments (dev/staging/prod)
• Premium support or faster SLAs
• Extra fees for custom voices, compliance features, or exports
• Vendor markups embedded inside the per-minute number

Practical tip: ask for a line-item breakdown of what the per-minute rate includes (telephony, STT, TTS, LLM, platform margin).

Open-source TCO: license + infra + vendor costs (ASR/TTS/LLM/telephony)

Open source reduces license lock-in, but it shifts responsibility to you.

Typical cost buckets:

• Telephony: inbound/outbound minutes, phone numbers, DID management
• STT (ASR): speech recognition cost per audio minute
• TTS: speech synthesis cost per character/second
• LLM: tokens for every user turn + tool call + system prompts
• Compute: CPU/GPU instances (if self-hosting STT/TTS or running media servers)
• Storage: recordings + transcripts + logs
• Monitoring: metrics, logs, traces, alerting
• Engineering/on-call: maintaining reliability, upgrades, and incident response

Two example budgets (structure, not a promise):

• Small volume: fewer minutes, low concurrency - vendor usage dominates
• Medium volume: more minutes, higher concurrency - infra + operational load becomes visible

You can keep costs predictable by:

• Using BYOK to avoid platform markups
• Keeping prompts short and stable
• Reducing retries and long silences
• Sampling recordings for QA instead of storing everything forever

LiveKit pricing and where it fits (infra vs full agent)

LiveKit is not a full voice agent. It is the real-time media layer that can make streaming audio reliable.

Where it helps:

• WebRTC streaming
• Region pinning and better routing
• A solid base for low-latency audio pipelines

Where it does not help:

• Agent logic, prompt/versioning, tool calls, CRM actions
• STT/TTS quality
• Conversation design and evaluation

Latency reference data points from a Pipecat community issue show how different layers affect real-time feel:

• LiveKit: less than 300ms target, <1.5s (notes: streaming, region pin)
• Pipecat: ~1s (user stop to bot), 2-5s reported (notes: LLM bottleneck)
• Vocode: N/A, <800ms target (notes: no specific data)

These are not universal benchmarks. They are field-reported targets/observations that highlight the main bottleneck in many stacks: the LLM and the orchestration pipeline, not just the media server.

Sample cost scenarios (10k mins/month vs 200k mins/month)

These scenarios are meant to help you model TCO. Replace the assumptions with your vendors.

Assumptions (both scenarios):

• Calls are mostly agent-handled, with recording enabled
• STT/TTS/LLM costs are paid either directly (BYOK) or embedded in platform pricing
• Telephony pricing varies by region/provider, so it is listed separately
• Storage assumes you store audio + transcripts for QA

Scenario A: 10,000 minutes / month (pilot)

Line item	Bolna AI (example)	Open source stack (example)
Platform fee + included mins	$500-$1,000 plan range	$0 (OSS license)
Usage per-minute	$0.05-$0.10/min after plan rules	STT + TTS + LLM usage (BYOK)
Telephony	Often separate	Separate
Storage (recordings/logs)	Often add-on	You pay cloud storage
Monitoring/alerts	Included to some degree	You set up your own
Engineering time	Lower	Higher (setup + ops)

Bolna plan data: Bolna pricing

Decision note: at 10k minutes, hosted platforms often win on speed. Open source wins when privacy requirements or customization are strong.

Scenario B: 200,000 minutes / month (scale)

At higher volume, the question becomes: Are you paying a platform margin on every minute?

Line item	Bolna AI (example)	Open source stack (example)
Platform fee	Higher tier or custom	$0 (OSS license)
Per-minute margin	Potentially large at scale	Lower if BYOK optimized
Infra/media	Bundled	You pay (LiveKit/self-host)
On-call	Vendor	Your team
Flexibility	Platform constraints	You can optimize everything

Important: open source can be cheaper at 200k minutes, but only if you have:

• Solid observability
• A tested fallback strategy
• Someone accountable for performance and reliability

Build vs Buy: time-to-launch, effort, and what you must build yourself

You are not choosing between buy vs build. You are choosing what you want to own.

What is an AI calling stack (telephony + STT + LLM + TTS)?

An AI calling stack is the end-to-end system that answers real phone calls:

Audio in > STT > LLM (plus tools/actions) > TTS > Audio out, with logging, storage, and monitoring around it.

Mini glossary (build terms)

• Orchestration: coordinating STT/LLM/TTS, turn-taking, and tool calls.
• Barge-in: letting the user interrupt the agent naturally.
• Retry logic: handling failures without breaking the call.
• Prompt/versioning: managing prompt changes like code releases.
• Evaluation (evals): automated tests for conversation quality.

Time to first working call: Bolna AI vs open source

A realistic timeline block (based on what I have seen in practice):

Day 0 (same day)

• Bolna AI: Create account, configure agent, connect number, test basic script.
• Open source: Pick stack, set up repo, choose vendors, define architecture.

Day 1

• Bolna AI: Working inbound demo, simple webhook action.
• Open source: First end-to-end pipeline working, but fragile.

Week 1

• Bolna AI: add integrations, refine prompts, basic analytics.
• Open source: stabilize audio streaming, add retries, logging, storage, dashboards.

Week 4

• Bolna AI: production tuning, support process, cost review.
• Open source: production-ready if you invested in observability, evals, and on-call.

Open-source reference stack #1 (Pipecat + LiveKit + STT/TTS + LLM)

This stack is for teams that want a modern streaming pipeline.

High-level architecture:

• Caller audio > Telephony/WebRTC bridge
• LiveKit (real-time media)
• Pipecat (streaming pipeline + turn logic)
• STT (streaming transcription)
• LLM (agent reasoning + tool calls)
• TTS (streaming speech)

Where it shines:

• You can tune latency and barge-in carefully.
• You can swap vendors (BYOK) without rewriting everything.

Where it can hurt:

• You own integration glue, deployment, scaling, and debugging.
• LLM response time becomes a bottleneck (reported in real usage).

Open-source reference stack #2 (Vocode-style agent + telephony + evals)

This stack is for teams that want a clearer agent framework + connectors approach.

Typical architecture:

• Telephony provider (inbound/outbound)
• Vocode framework (agent + connectors)
• STT + LLM + TTS
• Recording + transcripts to storage
• Evals + QA review workflow

If you need outbound calling, add:

• Contact list ingestion
• Dialer logic and rate limits
• Compliance prompts and consent
• CRM sync for dispositions and outcomes

What you must build yourself (telephony, orchestration, retries, monitoring, evals)

Teams underestimate this list. It becomes your real TCO.

Core engineering tasks:

• Phone number procurement and routing rules
• Call flows (IVR-like logic) and handoff to humans
• Barge-in tuning and silence detection
• Latency tuning and region placement
• Prompt management, versioning, rollback
• Tool calling, retries, idempotency, rate limits
• Failure handling (STT down, TTS down, LLM timeout)
• Call recording, storage, retention policies
• Analytics dashboards, QA sampling, evals
• Security reviews and access controls

Dograh's positioning matters here: it aims to reduce this build burden while staying open source, with a builder UI and BYOK approach.

Privacy, compliance, and control (India and global)

Privacy is mostly about your data flow and retention choices, not the marketing page.

Data flow map: where audio, transcripts, and logs go

A simple text diagram you can copy into a security review:

1. Caller audio (in transit) enters your telephony/media edge
2. Audio streams to a media server (hosted or self-hosted)
3. Audio is sent to STT (vendor or self-hosted) > transcript created
4. Transcript + context sent to LLM > decision + tool calls
5. LLM output sent to TTS > agent audio created
6. Agent audio streams back to the caller
7. At rest storage: recordings, transcripts, tool logs, metrics, traces
8. Analytics: dashboards, evaluation datasets, QA review tools

Where data can leak:

• STT/TTS vendor logs
• LLM vendor retention policies
• Over-logging transcripts and tool outputs
• Wide internal access (too many people can replay calls)

How to reduce risk:

• BYOK with strict vendor settings
• Minimal retention by default
• Encrypt recordings at rest
• Strong access controls + audit trails
• PII redaction before storage

What is BYOK (bring your own keys) for voice AI?

BYOK for voice AI means your voice agent platform connects to your own STT/TTS/LLM accounts. This helps you control:

• Billing (no blended markups)
• Vendor contracts and data terms
• Region selection and retention settings

Self-hosting and BYOK (bring your own keys) trade-offs

Topic	Bolna AI (hosted)	Open source / self-host (Dograh/Pipecat/Vocode)
Data residency	Depends on vendor	You can pin regions and control storage
Network isolation	Limited	Strong (VPC, private subnets, private links)
BYOK	Often possible	First-class pattern
Retention	Platform settings	You implement exactly what you want
Security controls	Vendor-defined	You define IAM, encryption, audit, access
Operational burden	Low	Medium-High

If your team is in India and handling sensitive calls, "AI voice agent India" requirements often include: local language performance, consent prompts, and conservative retention.

Compliance checklist (call recording consent, retention, audits)

Requirement	Why it matters	Bolna AI approach	Open source/self-host approach
Recording consent prompt	Legal + trust	Configure scripted prompt	Implement in call flow
Data retention policy	Limits exposure	Platform settings	Your storage lifecycle policies
PII redaction	Protect customers	Vendor feature or custom	Build redaction pipeline
Encryption at rest	Prevent data leaks	Vendor-managed	Use KMS + encrypted buckets/disks
Access controls (RBAC)	Prevent internal misuse	Platform roles	IAM + app-level roles
Audit trail	Compliance + forensics	Platform logs	Central logging + immutable audit logs
Incident response	Reduce downtime and breach impact	Vendor runbooks	Your runbooks + on-call

Performance and Product fit: Languages, Latency, Quality and Integrations

Performance is where voice agents succeed or fail in production.

What is voice agent latency (and why p95 matters)?

Voice agent latency is the delay between the user finishing a sentence and the agent responding. p95 latency matters because a fast average can hide slow, painful moments.

Good targets vary, but real-time conversations usually need:

• Quick barge-in response
• Stable streaming (low jitter)
• Fast STT partials and fast first-token from the LLM

Indian language support and voice quality (what to test)

If you care about Hindi or regional languages, do not trust a checkbox. Test it.

Testing plan (copy/paste):

• 100 real call clips (noisy + clean)
• Hindi + Indian English + Hinglish
• Domain terms (product names, locations, prices)

Measure:

• WER (word error rate) for STT
• Code-mixing handling (Hindi + English in one sentence)
• Noise robustness (street noise, call center noise)
• TTS naturalness and pronunciation of names/brands

Research benchmark to anchor expectations:

• A 2024 paper reports Whisper Large-v3 fine-tuned with prompts achieves 9.24-13.95% WER on Hindi/Gujarati/Marathi/Bengali (Kathbath dataset), with 30-50% improvement over baselines using family prompting and tokenizer changes.

Market expectation (clean speech):

• Commercial providers often claim 92-95% accuracy (implying ~5-8% WER) on clean Hindi/Indian English.
• The same paper notes Google tends to lead for Hindi/Tamil/Telugu accents, and Deepgram for noisy calls.

Bolna language note:

• Bolna integrates Sarvam/Pixa STT for Hindi/regional languages and claims good performance on accents/code-mixing, but it does not publish specific WER statistics.

Latency and streaming quality (barge-in, interruptions, jitter)

Latency is a system property. It is shaped by media, STT streaming, LLM speed, and TTS streaming.

What to measure:

• User stop > bot start speaking (p50 and p95)
• Barge-in time (how fast the agent stops talking)
• Jitter and packet loss effects on audio quality
• Timeout rate (LLM, STT, TTS)

Practical takeaway: if you want consistent sub-second turns, optimize:

• LLM model choice and prompt size
• Tool latency (CRM calls often dominate)
• Streaming TTS that starts speaking early

Integrations (CRM, helpdesk, call center, webhooks)

Integrations decide if your voice agent is a demo or a business system.

Integration need	Bolna AI	Dograh AI	Pure open source frameworks
CRM updates (leads, notes)	Typically native + webhooks	Webhooks + custom actions	Build yourself
Helpdesk ticketing	Platform-dependent	Webhooks	Build yourself
Call center workflows	Platform-dependent	Any telephony + webhooks	Build yourself
Post-call summary	Often available	Buildable via workflows	Build yourself
Custom actions	Limited by platform	Strong (webhooks/your APIs)	Strong (code)

Dograh (capability statement): Dograh supports any telephony/STT/LLM/TTS via integrations and webhooks, which is often the fastest path to BYOK without rebuilding the entire stack.

Observability and analytics (debugging real calls)

Observability is your ongoing cost control lever.

Compare what good looks like:

• Call playback with timestamps
• Transcript + tool call trace per turn
• Prompt/version used for each call
• Latency breakdown (STT vs LLM vs TTS)
• Outcome tracking (disposition codes, conversions)
• Evals dashboard for regression testing

Hosted platforms usually give you dashboards quickly. Open source lets you wire everything into your own stack.

Open Source options deep dive (Dograh, Pipecat, LiveKit, Vocode)

Open source isn’t a single thing. You need to be clear about which layer you’re actually using.

Dograh AI overview (open source voice agent platform)

Dograh is positioned as an open source platform, not just a framework.

What it is:

• A platform to design, test, and deploy voice agents
• A drag-and-drop workflow builder
• "Build in plain English" editing for fast iteration
• Inbound and outbound calling
• Multi-agent workflows (useful for reducing hallucination by structuring decisions)
• BYOK-friendly design (telephony, STT, LLM, TTS)
• An AI-to-AI testing suite ("looptalk") that is still work-in-progress

Best for :

• Developers and small teams who want control from open source but still want an easy, ready-made platform
• Teams that want to move to self-hosting later without rebuilding everything

Dograh AI GitHub, Community, and Roadmap (proof points)

If you are evaluating open source, verify traction and maintenance.

Checklist (add these links in your internal evaluation doc):

• Dograh GitHub repo link (search "Dograh AI GitHub" in your review packet)
• Contributor guide and license clarity
• Issue velocity and last commit recency
• Public roadmap items (looptalk improvements, evals, observability)

Also, when comparing "bolna ai github" presence versus open source projects, treat that as a category signal:

• Hosted platforms often have less core code open.
• Open source platforms/frameworks live or die by GitHub activity.

Pipecat vs Vocode vs LiveKit: what each one is best at

• LiveKit: best at real-time media streaming and building a reliable audio layer.
• Pipecat: best at streaming pipeline orchestration (how audio/text flows through STT > LLM > TTS).
• Vocode: best at agent framework + connectors patterns.

When you combine them:

• LiveKit handles media transport and streaming quality.
• Pipecat coordinates real-time turn-taking and vendor calls.
• Vocode-style components can manage agent logic and integrations.

If you want a platform experience with open source control, Dograh aims to package much of this into a more product-like workflow.

Decision guide: choose Bolna or choose open source (checklists)

If you answer "YES" to 3+ items in a checklist, that option will usually fit better.

Choose Bolna AI if... (fast launch, small team, managed ops)

Pick Bolna if you need:

• A working voice agent fast with minimal engineering
• Managed operations and fewer infrastructure decisions
• A vendor to lean on for reliability and support
• Acceptable platform constraints on workflows
• You are not ready to own on-call for voice infra

This is usually the right fit for business teams piloting quickly.

Choose open source voice agent if... (privacy, control, lower long-term TCO)

Pick open source if you need:

• Self-hosting or strict data control
• BYOK for STT/TTS/LLM with your vendor contracts
• Deep customization and unique workflows
• Freedom from lock-in and better long-term cost control
• The ability to integrate anything via webhooks and custom services

Recommended open source paths:

• Dograh (platform + open source + BYOK)
• Or Pipecat/Vocode + LiveKit + BYOK STT/TTS/LLM (framework-heavy, maximum control)

Team and budget fit guide (startup vs mid-market vs enterprise)

Team type	Best starting choice	Why
Startup (0-2 engineers on it)	Bolna or Dograh cloud	You need speed and low ops burden
Mid-market (dedicated dev + ops)	Dograh self-host or Pipecat stack	Control, compliance, and cost tuning start to matter
Enterprise (strict policies)	Self-host (Dograh or custom stack)	Data control, audit, and integration depth become non-negotiable

Maintenance reality check:

• Hosted: vendor on-call, but you still own conversation quality.
• Self-host: you own uptime, latency, vendor failures, and security posture.

Why this category is moving fast (and why TCO matters more than ever)

Voice agents are being adopted because they can cut costs and increase throughput. Integration and quality still block many teams.

A 2025 benchmarks roundup citing Gartner, McKinsey, and Deloitte reports:

• 80% of enterprises plan AI chatbots/voice bots by 2025
• Buyers target 30-45% operational cost cuts and improved CSAT
• Yet only 37.5% currently use chatbots, often due to integration challenges

This is why TCO is the right lens: production success depends on cost control, integration depth, and reliable operations.

Prerequisites (so you do not get surprised)

Before you pick Bolna vs open source, make sure you have:

• A clear target use case (support triage, appointment booking, collections, lead qualification)
• A decision on inbound vs outbound
• Consent and retention requirements written down
• A shortlist of STT/TTS/LLM vendors (or a platform that fits)
• An owner for QA and conversation design (not only engineering)

If you want the open source route, add:

• A basic on-call plan
• Monitoring and logging standards
• A deployment plan (cloud region, scaling, secrets management)

If your roadmap includes high volume or sensitive data, start on open source (Dograh or a custom stack). Migrating off a hosted per-minute model after you have hundreds of thousands of minutes is painful, and the migration itself becomes a hidden TCO line item. If you need self-hosting/BYOK, shortlist Dograh or a Pipecat/Vocode+LiveKit stack.

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