9 Best Telephony Stack Options for Voice AI in India 2026

TL;DR

A telephony stack is the infrastructure that connects AI agents to real phone networks, handling everything from SIP connectivity and audio streaming to compliance and call recording. For Voice AI in India, the right stack must also manage vernacular languages, code-switching, DLT/DND regulations, and low-latency turn-taking on noisy mobile lines. If you are a BFSI team that wants production-ready multilingual voice automation, a full-stack platform like Awaaz AI is the fastest path. If you want to build your own agent layer, CPaaS providers like Exotel, Plivo, or Twilio give you the telephony foundation, while developer frameworks like Vapi and LiveKit give you orchestration control.

Why the Telephony Stack Decides Whether Voice AI Actually Works

Voice AI does not fail in production because the model is not smart enough. It fails because the telephony stack cannot reliably connect, stream, route, record, and escalate live calls at scale.

This pattern shows up repeatedly. In one published case study, an AI voice company found that its agents were ready but call volume, DID reliability, and customer reachability broke down at the telephony layer. After migrating infrastructure, the system handled 4,000+ calls per minute and supported 20% month-on-month growth with real-time WebSocket streaming source. The bottleneck was not the LLM. It was the phone infrastructure underneath.

India makes this harder. The country is a phone-first market at massive scale, with TRAI publishing fresh telecom subscription data as recently as April 2026 source. Calls happen over narrowband 8 kHz mobile connections, in 22+ scheduled languages, with constant code-switching between Hindi and English (and regional variants). Research on Hindi-English code-switching confirms that ASR systems struggle when speakers mix languages mid-sentence source. Add DLT/DND compliance, RBI collection guardrails, DPDP data obligations, and IRDAI/SEBI rules, and the telephony stack becomes the most consequential infrastructure decision for any Voice AI deployment.

The industry is catching up to this reality. Speechmatics’ 2025 Voice AI report describes a shift from flashy demos toward embedded operational AI that powers core infrastructure source. The question is no longer “can AI talk?” but “can the stack behind it survive real calls?”

This guide compares nine telephony stack options across three buying paths: full-stack Voice AI platforms, cloud telephony and CPaaS, and developer-first agent frameworks. The goal is to help you decide what to build, what to buy, and what breaks if you choose wrong.

What Is a Telephony Stack?

A telephony stack is the set of systems that connects software to phone networks and manages the full lifecycle of a call. For a traditional call center, it routes calls between agents and customers. For Voice AI, it becomes the nervous system of the agent, responsible for far more than dialing.

Here are the layers that matter for AI voice agents:

1. PSTN/SIP connectivity. Phone numbers, SIP trunks, carrier routing, call origination, and call termination.
2. Call control. Dialing, receiving, transferring, conferencing, recording, IVR, and human handoff.
3. Real-time media layer. RTP, WebRTC, or WebSocket streaming, plus codec handling, jitter management, and narrowband audio support.
4. Speech-to-text (ASR/STT). Converting live audio to text, handling noise, detecting language, and managing code-switching.
5. AI reasoning layer. The LLM, prompts, tool use, memory, guardrails, and retrieval that power the agent’s responses.
6. Text-to-speech (TTS). Generating natural-sounding voice responses in the right language and tone.
7. Action layer. CRM updates, loan management system writes, payment link triggers, WhatsApp/SMS follow-ups.
8. Compliance layer. Consent, DND checks, DLT registration, call recording, retention policies, and audit logs.
9. Observability and analytics. p50/p95 latency, answer rates, ASR accuracy, fallback rates, outcome tracking, cost per connected call, and QA review.

A simple dialer starts calls. A Voice AI telephony stack runs conversations. Understanding how AI voice banking works at this layer-by-layer level is what separates teams that demo well from teams that deploy well.

How to Choose a Telephony Stack for Voice AI in India

Before comparing vendors, you need an evaluation framework. Here are the eight dimensions that matter most for Indian deployments.

Call Connectivity and Answer Rate

Can the stack route through Indian carriers reliably? Does it handle DID/CLI presentation issues? What happens during peak traffic? High dial volume means nothing if customers do not answer or drop after a two-second silence.

Latency and Turn-Taking

Ask for p50 and p95 response latency on actual PSTN calls, not browser demos. Test what happens when a caller interrupts the agent, pauses mid-sentence, or changes intent. Pauses kill trust. Practitioners on Reddit report that silence handling, interruption detection, and endpointing are among the most common production failure modes, not just “bad voice quality” source.

Language and ASR Performance

In India, the test is not “does the bot support Hindi?” The test is “does it handle a caller who says: ‘haan, EMI kal kar dunga, but app mein payment option nahi dikh raha’?” AI4Bharat’s IndicVoices dataset covers 12,000 hours of natural speech from 22,563 speakers across 208 districts and 22 languages source, which gives a sense of the diversity involved. Buyers should test vendors on real borrower recordings across regions, not scripted studio demos. For a deeper look at why this matters, see this guide to code-switching in Voice AI.

Compliance

If the stack is used for outbound AI calling (collections, lead qualification, renewals, marketing), the vendor must support DND checks, consent logging, sender identity, and audit trails. TRAI’s Telecom Commercial Communication Customer Preference Regulation (TCCCPR) framework exists specifically to protect customers from unsolicited commercial communications while allowing compliant messaging source. The Digital Personal Data Protection Act adds data-handling obligations around accuracy, security safeguards, breach notification, and erasure source. For BFSI teams handling AI debt collection calls, compliance is not optional; it is existential.

Workflow Integration

A voice bot that cannot update systems is just IVR. The stack should connect to CRM, loan management, collections, payment gateways, and follow-up channels like WhatsApp and SMS.

Observability

Require per-call traces: ASR partials and finals, LLM request time, TTS generation time, tool/API call latency, transfer events, failure reasons, and cost. Without this, debugging production issues is guesswork.

Pricing Predictability

Practitioners on Reddit repeatedly warn that headline per-minute pricing is misleading. The real cost includes telephony, STT, LLM, TTS, platform fees, recording, storage, retries, and rounding. One thread specifically argues that buyers should model blended cost across every component, not anchor on a single advertised number source. For BFSI, cost per successful outcome (cost per promise-to-pay, cost per completed KYC, cost per qualified lead) matters more than cost per raw minute. See this guide to calculating call center cost per minute in India for benchmarks.

Support and SLAs

Ask for escalation paths, outage handling, and carrier failover plans. G2 reviews and Reddit discussions for nearly every platform in this list mention support quality as either a differentiator or a risk source. Do not choose by API docs alone.

At-a-Glance Comparison Table

If you are a regulated Indian BFSI team, start with Awaaz AI. If you are a developer team building the agent layer yourself, compare Exotel, Plivo, Twilio, Telnyx, Vapi, and LiveKit based on control, cost, and compliance responsibility.

The 9 Best Telephony Stack Options for Voice AI Agents

1. Awaaz AI

Best for: Banks, NBFCs, MFIs, small finance banks, fintechs, and BFSI contact centers that need multilingual Voice AI agents with telephony, workflows, analytics, and human handoff, without assembling the stack from parts.

Awaaz AI packages the agent layer and the telephony stack together, which is what distinguishes it from every CPaaS and developer framework on this list. Instead of stitching together SIP trunking, ASR, LLM, TTS, compliance logic, and CRM integrations, BFSI teams get a platform built around finance-first workflows: sourcing, KYC, credit eligibility, collections, and retention.

Pricing:

• Pay-per-use credits charged per minute of customer conversation.
• Four tiers: Starter, Standard, Growth, Scale.
• Public pricing is not listed on the site; demo required.

Key features:

• Voice AI agents across phone, SMS, WhatsApp, and messaging channels.
• In-house telephony stack designed for low-latency, high-accuracy conversations.
• Multilingual support in 8+ languages including Hinglish and vernacular mixes.
• Domain-specific agents for finance, health, commerce, and hospitality.
• CRM/CDP integrations and APIs.
• Human-in-the-loop escalation.
• Analytics and structured call data from millions of conversations.
• Enterprise-grade security positioning.

Proof points (from client materials):

• 3.8M unique customers served in the last year.
• 82% call engagement rate.
• 60% cost reduction.
• 2x conversions.

Tradeoffs:

• Transparent self-serve pricing is not available; requires a sales conversation.
• Technical documentation appears less self-serve than developer-first CPaaS platforms.
• Independent third-party review coverage is limited compared to global CPaaS brands.

Verdict: Choose Awaaz AI if you are not trying to become a telecom infrastructure company. For Indian BFSI teams, the hard part is not just placing calls; it is holding low-latency multilingual conversations, enforcing compliance, escalating safely, and turning millions of calls into structured operational data. Awaaz AI is built for that specific problem.

BFSI teams evaluating procurement can review the procurement guide for small finance banks, and security-conscious buyers can request the enterprise security and compliance checklist.

2. Exotel

Best for: Indian enterprises and Voice AI companies that need scalable cloud telephony, voice APIs, real-time streaming, and India carrier support, but are building or integrating the AI agent layer separately.

Exotel is an India-focused cloud telephony platform, not a Voice AI agent. That distinction matters. It provides the telephony infrastructure (numbers, routing, recording, dashboards, APIs, media streaming) that an AI layer sits on top of.

Pricing:

• Dabbler: ₹9,999, 6-month validity, 5,000 credits, 1 phone number, 3 agents.
• Believer: ₹19,999, 11-month validity, 9,500 credits, 2 phone numbers, 6 agents.
• Influencer: ₹49,499, 11-month validity, 39,000 credits, 10 phone numbers, unlimited agents.
• 1 credit = ₹1. 7-day free trial with ₹500 worth of usage credits source.

Key features:

• Voice APIs with real-time WebSocket streaming.
• Live dashboard, call recordings, reports, and analytics.
• Auto dialer, dynamic call flows, call transfer.
• SMS and WhatsApp APIs.
• Missed call services and campaign management.

User sentiment:

• The Fundamento case study demonstrates Exotel handling 4,000+ calls per minute after the AI company migrated from a previous telephony provider source.
• G2 reviews mention easy integrations and call quality as positives, with calling problems, limited customization, and connectivity issues appearing as complaint themes source.

Tradeoffs:

• Not a turnkey Voice AI agent platform. Teams need to manage ASR, TTS, LLM, prompts, compliance logic, and downstream workflows.
• Credit-based pricing may need separate modeling for high-volume AI voice campaigns.
• BFSI-specific workflows (collections guardrails, audit trails, regulatory scripting) are not built in.

Verdict: A strong choice when you already have the agent layer and need Indian telephony infrastructure that understands domestic routing, numbers, and enterprise support.

3. Twilio

Best for: Global product teams and engineering organizations that want mature APIs, broad documentation, programmable voice, SIP trunking, and a contact center option.

Twilio is the default name in CPaaS for good reason: deep documentation, a huge developer community, and a wide product surface. But “default” does not mean “easiest” or “cheapest,” especially for India-specific Voice AI.

Pricing:

• Voice APIs: $0.0085/min to receive, $0.014/min to make a call.
• Elastic SIP Trunking: $0.0045/min origination, $0.007/min termination.
• Conversation Relay (advanced voice AI): $0.07/min.
• Twilio Flex: 5,000 hours free, then $1/active user hour or $150/named user/month source.

Key features:

• Programmable Voice APIs and Elastic SIP Trunking.
• Conversation Relay and Conversation Intelligence.
• Flex contact center.
• SMS, WhatsApp, RCS, email, and verification APIs.
• Serverless functions and Studio flows.

User sentiment:

• G2 shows 4.1/5 from 517 reviews. Users praise documentation, APIs, and ease of setup. Common complaints: expensive pricing, slow customer support, complex configuration source.
• One high-volume user on Reddit complained that Twilio pricing and performance did not scale well, mentioning capacity limits and slower support. Other commenters suggested Telnyx and Azure Communication Services as alternatives source.

Tradeoffs:

• Costs compound across voice, SIP, intelligence, recording, support tiers, and AI add-ons.
• India-specific DLT/DND/BFSI workflows are not solved by default.
• Non-technical operations teams may find it too developer-heavy.

Verdict: Twilio is a strong “build your own stack” option if your team has engineers who can own the full voice workflow and compliance layer. For teams comparing outbound AI calling approaches more broadly, this comparison of AI outbound calling bot platforms offers additional context.

4. Plivo

Best for: Cost-conscious teams that need programmable voice and SIP trunking with clear usage pricing and plan to build or integrate the AI layer separately.

Plivo offers simpler, more affordable telephony APIs than Twilio for many use cases. Its India SIP trunking rates are competitive, and the API surface is straightforward.

Pricing (India SIP trunking):

• Local number outbound: ₹0.60/min.
• Local number inbound: ₹0.60/min.
• Toll-free inbound: ₹1.30/min.
• Local number rental: ₹250/month.
• Free signup with trial credits; volume discounts available through sales source.

Key features:

• SIP trunking and Voice APIs.
• Local and toll-free number support.
• Secure trunking.
• Global routing.
• Developer-friendly API documentation.

User sentiment:

• G2 shows 4.5/5 from 746 reviews. Users praise ease of use, pricing, and API quality. Complaint themes include inconsistent customer support, unintuitive UX, and some messaging issues source.

Tradeoffs:

• Not a turnkey Voice AI agent solution.
• Compliance, AI orchestration, ASR/TTS/LLM, analytics, and workflows are the buyer’s responsibility.
• BFSI teams need to verify DLT/DND, recording, retention, and audit capabilities for their specific use case.

Verdict: Worth shortlisting when the telephony requirement is clear, the team is cost-sensitive, and the AI agent stack will be assembled separately.

5. Telnyx

Best for: Developer teams that want granular, component-level control over voice, SIP, media streaming, recording, and STT/TTS choices with transparent pricing.

Telnyx frames Voice AI as an infrastructure problem and prices it that way: every component (media streaming, STT, TTS, recording, transfers) has its own line item. This is appealing for teams that want to understand exactly what they are paying for.

Pricing:

• Voice API outbound/inbound: $0.002/min + SIP trunking fee.
• Call recording: $0.002/min.
• Call transfer: $0.10/invocation.
• Media streaming over WebSockets: $0.0035/min.
• Telnyx STT: $0.015/min. Google/Azure STT: $0.017/min source.

Key features:

• Voice API and Elastic SIP trunking.
• WebSocket media streaming.
• Call recording, call transfer, SIP interface.
• Multiple STT/TTS provider options.
• Noise suppression and decrypted forking.
• 24/7 support on volume contracts.

User sentiment:

• G2 reviews mention well-documented APIs, broad product surface, and in-house engineering support source.
• Reddit feedback is mixed. Some users report that moving from Twilio to Telnyx reduced costs and gave more control. Older threads mention support delays and porting issues source.

Tradeoffs:

• Not India-specific by default. Buyers need to validate Indian number availability, DLT/DND workflows, and data residency.
• Agent orchestration, consent management, BFSI compliance logic, and workflows remain the buyer’s responsibility.
• Support quality varies by contract tier and region.

Verdict: A strong option when engineering control, WebSocket media streaming, and transparent component-level pricing matter more than turnkey India BFSI workflows.

6. Vonage

Best for: Enterprises already using Vonage communications APIs, or teams that want a managed, low-code AI Studio path across voice, SMS, WhatsApp, and web chat.

Vonage offers both raw communications APIs and a higher-level AI Studio for building conversational agents. The AI Studio provides a GUI builder, NLU, templates, and multi-channel deployment, making it more accessible to non-developer teams than pure CPaaS options.

Pricing:

• AI Studio Standard plan: $0/month (low-traffic self-deployed agents).
• Advanced plan: $1,100/month (larger-scale conversational agents).
• Fully Managed: custom pricing.
• NLU: $0.0073/request. Knowledge AI: $0.0073/request.
• Regular Vonage API charges apply for voice, ASR, TTS, SMS, and WhatsApp on top source.

Key features:

• AI Studio low-code builder with templates.
• Voice, SMS, WhatsApp, and web chat channels.
• NLU and Knowledge AI.
• Insights dashboard.
• Webhooks and custom code support.
• Professional services and fully managed option.

User sentiment:

• G2 shows 4.2/5 from 404 reviews. Users praise ease of use, clear documentation, and reliability. Complaint themes include complex advanced configuration, pricing for smaller teams, and support delays source.
• Reddit discussions include serious support complaints around account and fraud issues, suggesting buyers should validate support SLAs before committing source.

Tradeoffs:

• Costs are layered: AI Studio fees + API charges + ASR/TTS + professional services.
• Advanced configurations may require technical support.
• Not positioned for India-specific BFSI workflows by default.

Verdict: Strongest when the enterprise already has a Vonage footprint or wants a more managed, low-code agent-building approach. Indian BFSI teams should still validate local compliance and language performance.

7. SignalWire

Best for: Technical teams that want a telecom-native platform with Voice AI runtime, media control, and a single-invoice model instead of stitching vendor bills together.

SignalWire differentiates by bundling telecom and Voice AI runtime into one platform with one invoice. For teams tired of tracking separate bills for telephony, STT, TTS, LLM, and orchestration, the bundled approach is appealing.

Pricing:

• AI Agent Runtime: $0.16/min, with bundled pricing and a single invoice source.

Key features:

• AI processing and agent runtime.
• Full media control and real-time API triggers.
• Routing and memory/state management.
• Observability and error-handling emphasis.
• Bundled pricing model.

User sentiment:

• Practitioners on Reddit note that for production-stable Voice AI, architecture decisions (latency, media control, observability) matter more than whether the platform uses a single speech-to-speech model or a composed pipeline source. SignalWire’s emphasis on full media control and observability aligns with this perspective.

Tradeoffs:

• Less India/BFSI-specific public positioning compared to Awaaz AI or Exotel.
• Buyers need to validate India number availability, DLT/DND, data residency, and language support.
• Public third-party review depth is thinner than Twilio, Plivo, or Vonage.

Verdict: An interesting option for builders who want telecom and Voice AI closer together, especially when predictable bundled billing matters. India-specific validation is required before committing.

8. Vapi

Best for: Engineering teams building custom voice agents who want model/provider choice, agent orchestration, and the flexibility to bring their own telephony and AI providers.

Vapi is a voice-agent orchestration layer, not a telecom carrier. It sits between your telephony provider and your AI models, handling agent logic, turn detection, and provider routing. This makes it powerful for developers, and complex for everyone else.

Pricing:

• Vapi charges $0.05/min for calls, prorated to the second.
• Transcriber, model, voice, and telephony costs are charged at cost (pass-through).
• Bring-your-own provider keys supported.
• Phone numbers: $2/month.
• New accounts receive $10 free credits source.

Key features:

• Assistant/agent orchestration.
• Provider choice for STT, LLM, TTS, and telephony.
• BYO API keys.
• Enterprise plan with higher concurrency, hands-on support, shared Slack, and included minutes.

User sentiment:

• Reddit discussions praise Vapi as a strong starting point for builders. One commenter describes it as “really cool” for custom voice bots but notes it requires a developer to piece together source.
• Other threads complain about Vapi/Retell costs, latency spikes, and difficulties scaling high-volume campaigns profitably source.

Tradeoffs:

• True cost is not $0.05/min; it includes telephony, STT, TTS, and LLM on top.
• India compliance (DLT/DND, BFSI audit) must be built by the buyer.
• Non-technical operations teams will struggle.

Verdict: A powerful builder tool, not a shortcut around telephony or compliance responsibility. Best when engineering control matters and the buyer accepts provider-level cost management.

9. LiveKit

Best for: Engineering teams building real-time voice or video agent products with WebRTC/SIP, custom models, deployment control, and detailed observability.

LiveKit is real-time communications infrastructure with an AI agent deployment layer. It is not designed for “run an EMI reminder campaign next Tuesday.” It is designed for teams building voice agent products that need media-level control, model choice, and production metrics.

Pricing:

• Build plan: $0/month (1,000 agent minutes, 5 concurrent sessions).
• Ship plan: $50/month (5,000 minutes, 20 concurrent sessions, then $0.01/min).
• Scale plan: $500/month (50,000 minutes, up to 600 concurrent sessions, then $0.01/min).
• Inference billed separately, e.g. Deepgram Nova-2 Phone Call at $0.0058/min, Cartesia Sonic TTS at $0.0300/min source.

Key features:

• AI voice and video agent hosting and deployment.
• SIP/telephony support and WebRTC.
• Deployment metrics, cold-start prevention (higher tiers), and instant rollback.
• Noise suppression and voice isolation.
• Built-in end-of-turn detection and interruption handling.
• LLM/STT/TTS inference access.

User sentiment:

• Practitioner threads on Reddit cite latency, VAD/endpointing, and observability as the real production blockers for voice agents. LiveKit’s explicit pricing and features around agent deployment metrics, interruption handling, and inference make it relevant to engineering-led teams tackling these problems source.

Tradeoffs:

• Engineering-heavy. Not suitable for operations-led deployments.
• India-specific telephony, DLT/DND, and BFSI compliance must be validated or built from scratch.
• Inference, telephony, and support costs need careful modeling.

Verdict: The right tool if your team is building real-time voice agent products and wants deep control over media, deployment, and model choices. Not the right tool if you need to launch a BFSI campaign next month.

Build vs Buy: Which Route Should You Choose?

The nine options above fall into three buying paths. The right one depends on your team, timeline, and tolerance for infrastructure responsibility.

Choose a Full-Stack Voice AI Platform When:

• You are in banking, NBFC, MFI, insurance, or regulated services.
• You need multilingual Indian conversations that handle code-switching naturally.
• You need human handoff, audit trails, and compliance workflows.
• You care about business outcomes (collections, KYC, sourcing, eligibility, retention), not infrastructure assembly.
• You do not want to spend months stitching together telephony, ASR, LLM, TTS, compliance, and CRM integrations.

Awaaz AI fits here. For teams focused on Voice AI in banking, this path offers the fastest route from pilot to production.

Choose CPaaS / Cloud Telephony When:

• You already have voice engineers and telecom expertise.
• You want to own routing, numbers, call flows, dialer logic, and AI provider choices.
• You can build compliance workflows internally.
• You are optimizing infrastructure cost and flexibility, not time-to-value.

Exotel, Twilio, Plivo, Telnyx, Vonage, and SignalWire fit here. Just remember: CPaaS does not automatically solve Indian language understanding, BFSI workflow design, or regulatory scripting.

Choose Developer-First Agent Frameworks When:

• You are building a voice AI product, not just running a campaign.
• You need orchestration flexibility and model choice.
• Your engineers can manage telephony, observability, data pipelines, compliance, and failure modes.
• The use case is experimental, developer-led, or global.

Vapi and LiveKit fit here. They are powerful, but operationally heavy. Teams exploring automated outbound calling solutions should weigh whether the control is worth the assembly cost.

Hidden Costs That Break Voice AI ROI

The advertised per-minute rate for any telephony stack is almost never the actual cost. Here is what a real Voice AI call bill includes:

• Telephony minutes (inbound + outbound).
• SIP trunk fees (per-minute or per-channel).
• Platform/orchestration fee (Vapi charges $0.05/min on top of provider costs source; SignalWire bundles at $0.16/min source).
• STT/ASR (Telnyx charges $0.015/min for its STT source).
• LLM tokens (varies wildly by model and prompt length).
• TTS (character or minute based).
• Call recording and storage.
• Analytics and dashboards.
• Phone number rental (Vapi: $2/month; Plivo India local: ₹250/month).
• Support tier fees.
• Professional services and implementation.
• Retries, failed calls, and rounding increments.
• DLT onboarding or compliance configuration.
• Human escalation cost (agent time when handoff occurs).

Vonage’s AI Studio, for example, applies regular API charges for voice, ASR, TTS, SMS, and WhatsApp on top of AI Studio plan costs source. Every platform has some version of this layering.

The formula:

True cost per minute = telephony + platform/orchestration + STT + LLM + TTS + recording + storage + analytics + retry overhead + support fees

For BFSI teams, cost per successful outcome matters more than cost per raw minute. Track cost per promise-to-pay, cost per completed KYC, cost per qualified lead.

Demo Checklist: What to Ask Before You Commit

Most vendor demos are controlled. Five calls in a quiet room with scripted prompts prove nothing. Here is what to demand:

1. Noisy mobile call test. Run the demo over an 8 kHz PSTN/mobile connection with background traffic noise, not studio audio.
2. Code-switching test. Inject Hinglish or regional-language mixing mid-sentence and see if the agent keeps up.
3. Interruption test. Have the caller interrupt the agent twice and change intent. Watch how it recovers.
4. Compliance test. Attempt a call outside allowed campaign windows. It should be blocked automatically.
5. Handoff test. Ask for a human mid-call. Verify that the transcript, intent, sentiment, and last action transfer cleanly.
6. Concurrency test. Show performance at expected peak simultaneous call volume, not just single-call latency.
7. Cost trace test. Ask for a full per-call cost breakdown: telephony, STT, LLM, TTS, platform, recording, and retries.
8. Audit test. Show consent logging, transcript, recording, disposition, and downstream CRM update for a single call.
9. DLT/DND workflow test. Demonstrate how DND checks, consent verification, and sender identity are handled.
10. Carrier failover test. What happens if one carrier route degrades? Is there automatic failover?
11. Data ownership question. Who owns recordings, transcripts, prompts, and extracted data?
12. Retention and erasure. What are the retention periods and erasure workflows under DPDP?
13. Concurrency limits. What is the hard ceiling, and what happens beyond it?
14. References. Can the vendor provide references in your specific industry?
15. Pilot timeline. How fast can you launch one controlled pilot?

Final Recommendation

The best telephony stack depends on how much responsibility you want to own.

If you are building a voice AI product from scratch, Vapi or LiveKit give you the orchestration and infrastructure control you need. If you need raw Indian telephony, evaluate Exotel or Plivo. If you want global programmable communications infrastructure, compare Twilio, Telnyx, Vonage, and SignalWire.

But if you are an Indian BFSI team trying to automate real customer conversations in vernacular languages, with low latency, analytics, human escalation, and compliance-sensitive workflows, a full-stack Voice AI platform is the safer starting point. Awaaz AI packages the agent layer and telephony stack around the finance-first workflows that matter: collections, KYC, credit eligibility, sourcing, and retention, across 8+ Indian languages including Hinglish.

The telephony stack is where Voice AI stops being a demo and starts being a product. Choose accordingly.

Frequently Asked Questions

What is a telephony stack?
A telephony stack is the set of systems that connects software to phone networks and manages the full lifecycle of a call. It includes PSTN/SIP connectivity, call control, media streaming, and (for Voice AI) the speech, reasoning, action, compliance, and analytics layers that sit on top.

What is the difference between CPaaS and a Voice AI platform?
CPaaS (Communications Platform as a Service) provides telephony infrastructure: numbers, SIP trunks, voice APIs, and media streaming. A full-stack Voice AI platform adds the agent layer on top: ASR, LLM, TTS, workflow logic, compliance, analytics, and human handoff. CPaaS gives you building blocks. A Voice AI platform gives you a working agent.

What is the best telephony stack for AI voice agents in India?
It depends on your team and use case. For Indian BFSI teams that need multilingual voice agents with compliance and workflows built in, Awaaz AI is the strongest starting point. For teams building their own AI layer, Exotel and Plivo provide strong India telephony infrastructure. For global developer teams, Twilio, Telnyx, Vapi, and LiveKit offer different levels of control.

How much does a Voice AI telephony stack cost?
Costs vary widely. CPaaS telephony alone can range from ₹0.60/min (Plivo India SIP) to $0.014/min (Twilio outbound). When you add STT, LLM, TTS, platform fees, recording, and analytics, blended costs often reach $0.10 to $0.20+ per minute. Full-stack platforms like Awaaz AI charge per minute of conversation with tiered plans. Always model the full blended cost, not just the headline rate.

Should I build or buy a Voice AI telephony stack?
Build if you have voice engineers, telecom expertise, and the time to manage compliance, observability, and multi-vendor billing. Buy a full-stack platform if you care about time-to-value, need BFSI compliance out of the box, and want to focus on business outcomes rather than infrastructure assembly.

What compliance checks matter for AI calling in India?
Key frameworks include TRAI’s TCCCPR for DND/DLT and unsolicited commercial communication, the Digital Personal Data Protection Act for data handling and erasure, and sector-specific rules from RBI, IRDAI, and SEBI for financial services communication. Any telephony stack used for outbound AI calling must support consent management, call-window enforcement, recording/retention policies, and audit trails.

Why does latency matter in AI phone calls?
Human conversation expects responses within a few hundred milliseconds. When Voice AI takes 1 to 2 seconds to respond, callers lose trust, talk over the agent, or hang up. Latency in a Voice AI telephony stack compounds across ASR processing, LLM inference, TTS generation, and network transit. Ask vendors for p50 and p95 latency on actual PSTN calls, not browser-based demos.

How do I test a voice agent before production?
Do not rely on scripted demos. Run calls over real mobile connections with background noise. Test code-switching, interruptions, intent changes, and handoff to a human. Verify compliance workflows block calls outside allowed windows. Ask for per-call cost traces and concurrency benchmarks at expected peak volumes.