DIY Voice AI: 7 Hidden Costs of Building on Vapi or Twilio

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A DIY voice AI build is an AI phone agent you assemble yourself from separate parts, usually an orchestration layer such as Vapi, a telephony carrier such as Twilio, plus your own speech recognition, language model, voice synthesis and CRM code. The pricing pages make it look cheap. A few cents a minute, a weekend of work, and you have an agent that calls leads.

That first weekend is real. Engineers build working demos on Vapi in an afternoon, and Twilio has been the default programmable voice carrier for over a decade for good reason. The problem is not the build. The problem is month two, when the demo becomes a production calling operation and a set of costs arrive that were never on the pricing page.

This post prices those costs out. It uses a transparent model with the arithmetic shown, it says plainly which teams genuinely should build rather than buy, and it names what the model cannot capture.

Key takeaways

• Per-minute orchestration pricing covers the conversation, not the phone system around it.
• Seven recurring cost centers appear after month one, and five of them are staff time rather than invoices.
• Number registration and carrier vetting cost little in dollars and a lot in calendar weeks.
• Carrier spam labeling is an ongoing maintenance job, not a one-time setup task.
• National Do Not Call Registry access is capped at 23,425 dollars a year for fiscal 2027, and more than fifteen states add their own rules on top.
• Teams with in-house engineers who need the agent inside their own product should build.
• Teams whose constraint is sales capacity rather than engineering capacity usually should not.

Table of contents

• What DIY voice AI is
• Who should build on Vapi or Twilio
• Cost one is telecom engineering that never ends
• Cost two is number registration and carrier vetting
• Cost three is carrier spam flagging
• Cost four is Do Not Call access and state TCPA rules
• Cost five is prompt maintenance
• Cost six is evaluation and call QA
• Cost seven is on-call and incident response
• A cost model for a DIY voice AI build
• What the model leaves out
• Build versus buy compared
• Twelve questions to answer before you build
• How Bigly Sales fits
• DIY voice AI FAQ
• The bottom line

What DIY voice AI is

DIY voice AI is the practice of building your own AI phone agent by wiring together independent vendors rather than buying a finished calling platform. A typical stack has five layers. An orchestration framework manages the call loop and interruption handling. A telephony provider originates and terminates the actual phone call. A speech-to-text engine transcribes the caller. A language model decides what to say. A text-to-speech engine says it.

Vapi is the best known orchestration layer for this pattern. Twilio is the most common telephony layer, and plenty of teams use Twilio for both by writing their own orchestration on top of its media streams. Both are competent products with good documentation, and neither is trying to hide anything. Their pricing pages describe what they sell accurately.

The gap is definitional. Vapi sells orchestration. Twilio sells carrier connectivity. Neither sells the phone operation. In a DIY voice AI project, the phone operation is yours, and that is where the recurring cost lives. If the vocabulary here is unfamiliar, the AI calling glossary defines the terms used throughout this post.

Who should build on Vapi or Twilio

Some teams should absolutely build, and the honest case for it is stronger than most vendor content admits.

Build if the voice agent needs to live inside a product you sell. If you are a SaaS company adding a calling feature that your own customers configure, a managed calling platform is the wrong shape. You need primitives, and Vapi and Twilio sell primitives.

Build if you already employ engineers who understand SIP, call state and carrier behavior. For those teams the telecom work described below is not new overhead, it is the job they already do. Adding a language model to an existing voice stack is a much smaller project than building a voice stack from scratch.

Build if your call flows are genuinely unusual. Warm transfers into a proprietary queue, custom real-time scoring, a barge-in policy no product exposes as a setting. Configuration ceilings are real, and a DIY voice AI build has none.

Build if call volume is very high and very stable. At sustained enterprise volume the per-minute arbitrage between wholesale carrier rates and platform pricing eventually exceeds the cost of the team maintaining it. That crossover point is much higher than most teams estimate, but it exists.

Do not build because the pricing page looked cheaper. That is the reason that fails, and the rest of this post explains why.

Cost one is telecom engineering that never ends

The first hidden cost of DIY voice AI is that voice is a real-time distributed system, and real-time distributed systems need permanent staffing.

A text integration that breaks produces a failed request you retry. A voice integration that breaks produces a person on a phone listening to silence. The failure modes are specific and they recur. Audio latency drifts when a model provider changes regions. Barge-in stops working correctly after a speech recognition upgrade. Calls hang because a SIP BYE was not handled. Answering machine detection starts clipping the first two seconds of every voicemail after a provider tunes its own thresholds.

None of these are anyone’s fault. They are the normal weather of a multi-vendor real-time stack. But each one costs an engineer a day, and they arrive continuously rather than in a burst you can plan around.

The dependency count problem

A production DIY voice AI stack typically depends on four to six vendors, each with its own status page, rate limits, breaking-change cadence and support tier. The probability that at least one of them ships something disruptive in a given month approaches one. Somebody has to own that. In small teams, that somebody is the founder.

Cost two is number registration and carrier vetting

Getting phone numbers is easy. Getting phone numbers that carriers trust is a process with a calendar attached.

On the voice side, US carriers authenticate calls using the STIR/SHAKEN framework, and the attestation level your calls carry depends on your relationship with the originating carrier and on whether the calling number is verifiably yours. New numbers on a new account start with no reputation history. Registering in the Robocall Mitigation Database is a filing obligation for voice service providers, and if you are originating your own traffic through your own carrier relationships you need to understand where you sit in that chain.

If your DIY voice AI program also sends text follow-ups, and almost all of them do, you enter A2P 10DLC registration. The direct fees are small. At current published rates a one-time brand registration runs a few dollars, standard brand vetting is in the forty dollar range, campaign registration is roughly fifteen dollars, and monthly campaign fees are commonly around ten dollars. Confirm current pricing with your provider before you budget, because these change.

The cost is not the money. The cost is the wait and the rework. Campaign approval can take days to weeks, use cases get rejected for sample-message wording, and every new message type is a new campaign. A launch date that slips three weeks because a campaign was rejected twice is a real cost, it just does not appear on an invoice.

Cost three is carrier spam flagging

This is the cost that surprises DIY voice AI teams most, because it arrives after everything is working.

Carriers and their analytics partners score calling numbers using behavioral signals. Short average call duration, high dial volume from a new number, low answer rates and consumer complaints all push a number toward a “Spam Likely” or “Scam Likely” label. Once a number is labeled, answer rates on it collapse. The agent is fine. The prompt is fine. Nobody is picking up.

What remediation actually involves

Remediation is not a support ticket. It is a workflow. You identify which numbers are flagged, which requires checking across multiple analytics providers because each carrier uses a different one. You submit correction requests through the relevant registries. You rotate the affected numbers out of rotation and warm new ones in. Then you change the behavior that caused the flag, which usually means reducing dials per number, raising average talk time and cutting your abandoned-call rate.

Why it recurs

Number reputation decays under load. Any high-volume outbound program regenerates the problem continuously, so somebody has to watch answer rates by number every week and act before a number is burned rather than after. Buying more numbers is not a fix. Spreading the same bad behavior across forty numbers produces forty flagged numbers.

Cost four is Do Not Call access and state TCPA rules

Compliance is where a DIY voice AI project stops being an engineering exercise.

Telemarketing calls require scrubbing against the National Do Not Call Registry, and access is paid. The Federal Trade Commission sets the fees annually. For fiscal year 2027, which began October 1, 2026, access to a single area code costs 85 dollars, with a maximum of 23,425 dollars for a single entity accessing all area codes nationwide. The first five area codes are free. The FTC publishes the current schedule in its annual telemarketer fee announcement, and its Telemarketing Sales Rule guidance explains who has to scrub and who is exempt.

Federal registry scrubbing is the easy part. Your own internal suppression list has to be honored across every channel and every business unit, and it has to be honored immediately.

State rules multiply the work

More than fifteen states now run their own telephone solicitation statutes with terms stricter than the federal baseline. Florida’s Telephone Solicitation Act is the widely copied model, with prior express written consent requirements, restricted calling hours, a cap on repeat calls about the same subject and a private right of action. Oklahoma adopted closely similar language. Washington and Maryland have their own versions. Each one changes calling windows, consent wording or frequency caps by state, which means your dialing logic needs per-state rules rather than one national policy.

The one-to-one consent rule that never took effect

Plenty of compliance content still describes the FCC one-to-one consent rule as binding. It is not. The Eleventh Circuit vacated that rule in January 2025 and it never took effect. Prior express written consent under the TCPA remains the operative federal standard for these calls. One-to-one consent is still worth adopting as internal policy, because it reduces litigation exposure on shared and resold leads, but do not build your compliance program around a rule that does not exist.

The genuinely dated item to plan for is the cross-channel revocation requirement, which would treat an opt-out delivered on one channel as an opt-out across all of a caller’s messages. Its effective date has been extended twice and now sits at January 31, 2027. Building suppression that works that way is sensible regardless of what the final rule says. Our page on TCPA compliant AI calling platforms goes deeper on what compliant configuration looks like in practice.

Cost five is prompt maintenance

A prompt is not a configuration file you set once. It is a living script that decays.

It decays for four reasons. Your offer changes, and the agent keeps quoting last quarter’s pricing. Objections evolve, and a rebuttal that worked in March lands badly in September. The underlying model gets updated by its provider, and behavior you tuned around quietly shifts. And prospects find edge cases nobody scripted, because real callers say things no test suite contains.

What good upkeep looks like

Teams that keep quality high review a sample of real calls every week, tag failures by category, change one thing at a time and re-test against a saved set of hard calls. That is a few hours a week from somebody who understands both the sales motion and the model. It is closer to a copywriting job than an engineering job, which is why it usually goes unassigned in a DIY voice AI build. Engineers do not want to own it and sales managers do not have the tooling.

Cost six is evaluation and call QA

You cannot improve what you cannot see, and raw call recordings are not visibility.

To know whether your DIY voice AI agent is getting better or worse, you need transcripts joined to outcomes, a way to sample calls by outcome rather than at random, a labeled set of failure categories and a regression suite that flags when a prompt change breaks something that used to work. Managed platforms ship most of this. In a DIY build it is an internal product with no external customer, which means it gets deprioritized every single sprint until quality visibly drops.

Cost seven is on-call and incident response

Outbound calling runs on a schedule your customers can see. If the agent stops dialing at 10am on a Tuesday, that is not a background job you catch up on overnight. That is a day of lead response gone, and in speed-sensitive verticals a lead you did not call in the first hour is often a lead somebody else closed.

That means alerting on dial rate, connect rate and error rate rather than only on server health, plus a human who responds during calling hours. For a small team, a rotation of one is a person who cannot take a real vacation.

A cost model for a DIY voice AI build

What follows is a model, not measured results. Every input is an assumption stated in the table, drawn from published list prices at the time of writing and from ordinary fully loaded salary figures. It is here so you can replace the inputs with yours and redo the arithmetic, not so you can quote the total.

The scenario

A mid-market outbound program placing 20,000 dials a month. A 25 percent answer rate gives 5,000 connected calls. Average connected call length of 2.5 minutes gives 12,500 conversation minutes. Unanswered dials still burn telephony time, so the model adds 0.4 minutes of ring time across the 15,000 unanswered dials, for 6,000 extra telephony minutes and 18,500 billable telephony minutes in total.

The arithmetic that matters

Divide 14,010 dollars by 12,500 conversation minutes and the modeled cost is 1.12 dollars per connected minute. Divide it by 5,000 connected calls and it is 2.80 dollars per connected call. The orchestration line that appears on the pricing page, 625 dollars, is about 4 percent of that total.

The other useful split is time. In month one you have not bought Do Not Call access yet, you have no spam problem yet, and your engineer is building rather than maintaining. Month one looks like the 2,384 dollars of variable stack cost, which is 19 cents per connected minute. That is the number that goes into the build-versus-buy spreadsheet. The remaining 11,626 dollars a month shows up later, one line at a time, and by then the decision has been made.

Staff time is 9,063 dollars of the modeled total, or 65 percent. That is the finding. A DIY voice AI program is a payroll line item wearing a per-minute price tag.

What the model leaves out

Three things, and all of them cut in favor of building.

The model ignores volume discounts. At real scale, orchestration, voice synthesis and carrier minutes are all negotiable, and the variable stack compresses. It also ignores that the staffing lines are not linear. The same 0.35 of an engineer can often support three times the dial volume, so cost per minute falls sharply as volume rises.

It ignores strategic value. If the calling capability becomes part of what you sell, the engineering cost is product investment rather than overhead, and comparing it to a subscription is comparing the wrong things.

It also leaves out one item that cuts the other way, because it is impossible to model honestly. Regulatory exposure is not linear. State telephone solicitation statutes carry per-violation statutory damages and private rights of action, which means a configuration mistake in a single state is not a small cost. Managed platforms do not remove that exposure, but they do concentrate the responsibility for calling-window logic and suppression handling in a vendor whose entire business depends on getting it right.

Build versus buy compared

Three realistic paths, compared on what actually differs.

Read that table honestly. The two build columns win on customization and on ownership, and those are real advantages. The buy column wins on everything with the word “owns” in it, and only matters if you did not want to own those things. If you are still evaluating vendors, our roundup of the best AI cold calling software covers the category, and the Bigly Sales versus Vapi comparison covers this specific decision.

Twelve questions to answer before you build

Answer these in writing before committing. If more than four answers are “we will figure that out later”, the DIY voice AI path will cost more than you have budgeted.

• Who is on call during calling hours when dialing stops?
• Who reviews call transcripts weekly, and what is their other job?
• How will you know within an hour that a number has been flagged as spam?
• Which states will you call into, and who maintains the calling-window rules for each?
• Where does your consent record live, and can you produce it for a specific number in under a minute?
• How does an opt-out on one channel suppress the other channels?
• What is your rollback procedure when a prompt change makes conversion worse?
• How many vendor status pages does your stack depend on?
• What happens to answer rate when your best number burns and you have to warm a new one?
• Who owns answering machine detection tuning?
• What is your target cost per connected minute at full load, and what happens if you miss it?
• If the engineer who built this leaves, how long until someone else can safely change it?

How Bigly Sales fits

Bigly Sales is a managed AI calling platform. That means the seven cost centers above are our operating expense rather than yours. Numbers, carrier relationships, spam remediation, registry scrubbing, calling-window rules by state and prompt tuning are all included in the service rather than assembled by your team.

The honest caveat is the one in the table. A managed platform has configuration limits that a DIY voice AI build does not. If you need the agent embedded inside a product you sell to your own customers, or your call flow needs behavior no product exposes as a setting, you will hit a ceiling with us and you would not hit it with Vapi. We would rather say that now than three months into an implementation. Our pricing page shows what the managed side costs so you can put it next to your own version of the model above.

DIY voice AI FAQ

Is DIY voice AI cheaper than a managed platform?

On per-minute cost alone, usually yes. On total cost of ownership, usually no, because the per-minute rate excludes the phone operation around the call. In the model in this post, orchestration fees are about 4 percent of the modeled monthly total and staff time is about 65 percent. Whether building is cheaper for you depends almost entirely on whether the required staff time is incremental or work your team already does.

How long does it take to get a DIY voice AI agent into production?

A working demo takes days. Production quality takes two to four months on an orchestration layer and three to six months building directly on a carrier. The gap is not the conversation logic. It is answering machine detection tuning, retry policy, spam remediation, consent plumbing, CRM write-back and the calling-window rules for each state you dial into.

Are Vapi and Twilio good products?

Yes. Vapi is a well built orchestration layer with strong documentation, and Twilio has been the reference programmable voice provider for over a decade. Neither one is doing anything misleading. They sell components, and they describe those components accurately. The mistake is comparing a component price to a service price and concluding one is cheaper.

Why do my AI calls get marked Spam Likely?

Carrier analytics score numbers on behavior. Short average call duration, high dial volume from new numbers, low answer rates and consumer complaints all push a number toward a spam label. New numbers with no reputation history are especially vulnerable. Fixing it means correcting the underlying behavior, submitting remediation requests to the analytics providers, and rotating numbers rather than simply buying more of them.

Do I need 10DLC registration for AI voice calls?

Not for voice itself. A2P 10DLC governs text messaging to US mobile numbers. You need it as soon as your calling program sends text follow-ups, confirmations or missed-call texts, which almost all of them do. Budget for the calendar time rather than the fees, since campaign approvals can take days to weeks and rejected use cases have to be resubmitted.

How much does National Do Not Call Registry access cost?

The Federal Trade Commission sets fees each fiscal year. For fiscal 2027, starting October 1, 2026, a single area code costs 85 dollars and the maximum for one entity accessing all area codes is 23,425 dollars. The first five area codes are free, and some exempt organizations get the full list at no charge. A national outbound program should budget against the cap.

Is the FCC one-to-one consent rule in effect?

No. The Eleventh Circuit vacated it in January 2025 and it never took effect. Prior express written consent under the TCPA remains the operative federal standard. Many teams still adopt one-to-one consent as internal policy because it reduces litigation risk on shared and resold leads, but it is a choice rather than a requirement. The dated item to plan for is the cross-channel revocation requirement, currently set for January 31, 2027.

What is the biggest hidden cost of DIY voice AI?

Staff time, by a wide margin. In the model above, the three partial roles needed to keep an outbound program healthy come to 9,063 dollars a month against 2,384 dollars of vendor charges. The second biggest is the one with no invoice at all, which is the launch delay caused by number registration, carrier vetting and campaign approvals.

Can I start on Vapi and move to a managed platform later?

Yes, and it is a reasonable sequence. A DIY voice AI pilot is a cheap way to learn what your call flow actually needs. The migration cost is mostly in your consent records, suppression lists and CRM integration rather than in the prompts. Keep those three things portable from the start and switching stays inexpensive.

When does building genuinely beat buying?

When the agent has to live inside a product you sell, when you already employ engineers who understand call state and carrier behavior, when your call flow needs behavior no platform exposes as a setting, or when volume is high enough and stable enough that carrier arbitrage exceeds the maintenance payroll. Outside those four cases, the operational load usually outweighs the per-minute saving.

The bottom line

The per-minute price of a DIY voice AI stack is accurate and it is also nearly irrelevant. The recurring cost is telecom engineering, registration calendars, spam remediation, registry access, state-by-state calling rules, prompt upkeep and somebody being awake during calling hours. In the model above those items are roughly 96 percent of the monthly total.

None of that is an argument against Vapi or Twilio. They are good tools sold honestly, and teams with engineers who want the agent inside their own product should use them. It is an argument for pricing the whole operation before you commit, using your own inputs in the model above rather than the number on a pricing page.