Build vs buy and DIY
Build vs buy an AI SDR: what DIY really costs
By Viktorio Shopov, co-founder of Sellinger. Reviewed by Asen Levov. Published . Last updated . 9 minute read.
The software bill for a home built AI SDR is small, often a few hundred dollars a month. The real cost is the hours to build it, the hours to keep it working, the risk to the accounts you send from, and the compliance work. Build only if the process is unusual and an engineer owns it.
What is a DIY AI SDR made of?
An AI SDR does five jobs: it finds people, writes to them, sends the messages, reads the replies, and books the meeting. A home built one is a stack of parts that each do one job.
- Data. A list of people and companies, with emails or profile links, and a way to keep it current.
- Orchestration. A workflow tool or code that runs the sequence on a schedule.
- A model. To write the first message and the follow ups, and to read replies.
- Sending. Mailboxes for email, and a connection to LinkedIn or WhatsApp accounts.
- State and controls. A record of who was contacted, what was said, who opted out, and a way to stop.
Parts 1 to 4 are what people picture when they say "I will build it." Part 5 is where most of the work hides. We cover the failures in What DIY outbound breaks first. The head to head page is Build vs buy an AI SDR.
What does the tool bill look like? (An estimate)
This table is an estimate. It prices the software components from public pages we read on 19 October 2026. It does not include your time, a data provider, or taxes. Every row names its source, and the assumptions are stated under the table so you can replace them with your own.
| Component | Public price we read | Our assumption | Estimated monthly cost |
|---|---|---|---|
| Workflow tool | n8n Pro, 50 euros a month on annual billing, 10K executions. The Community Edition is free but self hosted (n8n pricing) | One workspace | 50 euros |
| Model calls | Claude Sonnet 5.5 at $2 per million input tokens and $10 per million output tokens (Claude API pricing) | 1,000 conversations a month, 8 calls each, 3,000 input and 300 output tokens per call | About $72 (24M input = $48, 2.4M output = $24) |
| Account connection API | Unipile, from $55 a month for up to 10 connected accounts, no usage fees (Unipile pricing) | 5 connected accounts | $55 |
| Mailboxes | Google Workspace Business Starter, 6.80 euros per user per month list price (Google Workspace pricing) | 3 sending mailboxes | About 20 euros |
| Data (list and verification) | Depends on the provider | Not priced here | Your own quote |
| Engineering time | Your own rate | Not priced here | Hours times your rate |
The assumptions are ours, not measured. Token counts depend on your prompts and on how many threads reach several replies. A conversation with more turns costs more, and a cheaper model costs less.
Read the table for what it shows. The tool bill lands in the low hundreds a month for a small setup. That is below Sellinger's list price for one LinkedIn agent plus an email plan ($198 and from $39, see the pricing page). We say so plainly, because a build vs buy article that hides it is not worth reading. The tools are cheap. The question is what is not in the table.
What is not in the table?
Build time
You have to build the sequence, the reply handling, the approval step and the opt out handling, then test them on real accounts. Our planning assumption, not a measured figure, is a few weeks of an engineer's time for a version that sends and logs, and longer for approvals and safe reply handling. Multiply your own estimate by your own hourly cost. If your engineer is also your product person, count what they would have built instead.
Maintenance
Channel connections change. Models change and your prompts drift. A mailbox gets flagged and the workflow keeps sending into it. Someone has to watch for these. This is a recurring monthly cost that lasts as long as the system does, and it is the line most estimates leave out.
LinkedIn account handling
If LinkedIn is a channel, you are automating a personal account. LinkedIn's Help Centre says it does not permit the use of third party software that scrapes, alters the look of or automates activity on its site, and that members who use these tools risk accounts being restricted or shut down (LinkedIn Help: Prohibited software and extensions). That applies to a home built tool and a commercial one equally. No tool, ours included, can promise that LinkedIn will not restrict an account. The difference is who sets the pacing and who watches the limits. Sellinger sets per account daily and weekly invitation limits and a quiet window; defaults are 20 invitations a day and 150 a week. In a DIY build you design and monitor those yourself. Read Is LinkedIn automation safe? first.
Data
A list is only as good as its freshness. Buying a list, verifying emails, and keeping it current is a recurring line. Sellinger includes a database of 700M+ contacts and charges from $0.01 per lookup, which you can compare with your provider's price. We do not quote a DIY data price because it depends on the provider you choose.
Email deliverability and compliance
Google's sender guidelines for bulk senders (more than 5,000 messages a day to Gmail accounts) require SPF, DKIM and DMARC, one click unsubscribe, and a spam rate kept below 0.30% (Google email sender guidelines). You may be far below that volume, but the habits are the same. In the United States, the FTC's CAN-SPAM guide requires opt outs to be honoured within 10 business days and states penalties of up to $53,088 per violating email (FTC guide). You need a suppression list that every send checks.
How do build and buy compare on the work itself?
| Question | Build | Buy (Sellinger) |
|---|---|---|
| Time to a first message | Weeks | Usually live within a day |
| Who maintains channel connections | You | The vendor |
| Approval before sending | You build it | Approval mode per campaign |
| Opt out handling | You build and test it | Replies such as STOP go on a suppression list that is checked before every send |
| Per account limits | You design and watch them | Set per account; the agent cannot exceed them |
| Cost shape | Low tools bill, high hours | Subscription, few hours |
| Control over every detail | Complete | Within what the product exposes |
The last row is the real reason to build. If you need control that no product offers, build. If you do not, you are paying hours for the privilege of re-solving problems others already solved.
When should you build?
- [ ] Your process is unusual, and you can describe it in a page.
- [ ] An engineer will own it for a year and has time reserved for it.
- [ ] Volume is small, so a mistake costs little.
- [ ] You can afford for it to break for a week.
- [ ] You are comfortable being the one who answers if a message goes wrong.
If you tick all five, build, and build the part that is yours. A middle path also works: buy the sending, state and controls, and build your own scoring or reporting on top through the REST API and the MCP connector.
How do you compare it for your own numbers?
Take the table above, replace the assumptions with yours, add your hours at your rate, and set it next to the subscription price. The ROI calculator projects meetings and pipeline value for the benefit side, and the build vs buy comparison lays the two side by side. If after that the build still wins, you will know why.
Common questions
Is building an AI SDR cheaper than buying one?
How long does it take to build one?
What is the biggest hidden cost?
Can I build on LinkedIn myself?
When does building make sense?
How Sellinger helps
Read how it works on See Sellinger pricing, or see a real campaign in Read how we use it ourselves.
