Build vs. Buy: Should You Build Your Own AI Production Agent?
A detailed build vs. buy analysis for AI production agents covering real engineering costs, where internal builds get stuck, and when each approach makes sense.
The agent itself is 10% of the work. Integrations, knowledge capture, memory systems, and operational reliability are the other 90%. Here is the real cost of building versus buying.
Your team is drowning in toil. Alerts are up. MTTR is bad. The instinct is to build a solution. You have smart engineers, access to LLMs, and MCP makes tools look easy. How hard can it be?
Harder than you think. The agent is maybe 10% of the job. The other 90% is the boring stuff: integrations, memory systems, guardrails, and keeping it online. This post breaks down the real cost so you don't walk into a trap.
The Real Cost of Building
Here is what a production-quality agent actually needs. Not a hackathon project. A system you trust at 2 AM when everything is on fire.
| Component | Build Effort | Ongoing Maintenance |
|---|---|---|
| Core agent + LLM orchestration | 2-4 weeks | Prompt tuning until you cry, swapping models, fixing regressions |
| Integrations (observability, SCM, CI/CD, comms) | 4-8 weeks | API breaking changes, auth rotation, integrating tools you forgot you owned |
| Knowledge / memory system | 4-12 weeks | Data quality, de-duping, making sure it doesn't hallucinate docs from 2019 |
| Action engine with guardrails | 4-8 weeks | Security reviews, approval workflows, ensuring you don't accidentally delete prod |
| Operational reliability (uptime, monitoring, on-call) | 2-4 weeks | Congratulations, you are now on-call for your on-call tool |
| Tribal knowledge ingestion | 2-4 weeks | Scraping Slack, docs, and incident channels forever |
Total build effort is 18-40 weeks for 2-3 senior engineers. That is 4-10 months before it investigates its first real incident. And the maintenance never stops. You need to keep up with the latest model improvements, agent architecture patterns, memory technologies, and tool calling standards. The AI landscape moves fast enough that what you built three months ago is already outdated. For a full pricing breakdown, see our cost guide.
Where Internal Builds Get Stuck
Everyone who tries to build this hits the same walls. Here are the four things that usually kill the project:
The integration iceberg
Getting a Datadog API call working in a demo takes an afternoon. Getting it working reliably across 50 configs, handling auth rotation, versioning, and rate limits takes months. This is the grunt work that kills internal builds.
RAG hits a wall
Most teams start by dumping Slack messages into a vector database. This works for simple Q&A. It falls apart on real investigations where the agent needs to correlate events across time, understand dependencies, and actually think.
Action without trust
Reading logs is safe. Letting a bot rollback a deploy requires trust. You need approval workflows, audit logs, and blast radius controls. Without them, your tool never makes it past read-only mode.
You are now on-call for your on-call tool
If the agent crashes during an incident, you now have two problems. Operational reliability for a production tool is a full-time job. Internal tools almost never get the budget for that.
Build vs. Buy: Side by Side
| Dimension | Build | Buy |
|---|---|---|
| Time to first value | 1 week to prototype, 3 months to tune it into something useful, then 6 months maintaining it while LLM advancements force an architectural rewrite. | Hours to integrate. Full investigation happens within 24 hours. No maintenance burden. |
| Integration breadth | DIY every integration. Fix them when APIs change. | 50+ native integrations. Vendor's problem, not yours. |
| Knowledge system | Dump docs in a vector DB. Hit accuracy walls immediately. | Purpose-built engines that actually understand context. |
| Remediation | Scary without guardrails. Needs major security work. | Pre-built engine with approvals and audit trails. |
| Ongoing maintenance | 1-2 engineers keeping this alive plus chasing model upgrades, new agent architectures, and memory tech. | Vendor handles the plumbing, upgrades, and uptime. You get improvements without the R&D cost. |
| Cost | $400-800K/year (2-3 senior engineers + infra) | $100-200/engineer/month |
When Building Makes Sense
Sometimes building is the right move. Be honest about whether these apply to you:
- Your operational workflow is incredibly unique.
- Security forbids third-party access and the vendor won't do on-prem.
- You have a DevEX team that wants to maintain this long-term.
- You are seeing this as a project for the team to learn how to build AI agents.
If all four are true, go ahead and build. If even one is false, the math says buy.
When Buying Makes Sense
For most teams, the decision is simple:
- You need this working in weeks, not next year.
- Your platform team is already drowning and can't take on a build project.
- You want it to actually fix things, not just investigate them.
- You need on-prem but don't want to build the server rack yourself.
The Hybrid Trap
Some teams try to build a "lite" version first. This sounds safe but usually fails. You get a tool that's good for demos but flakes out during real incidents. Engineers stop using it because they can't trust it.
If you build, commit resources and budget. If you buy, pick a vendor that delivers value in week one so you can validate it quickly. Our buyer's guide walks through exactly how to evaluate vendors and run a POC.
Frequently Asked Questions
How long does it take to build this myself?
About a week to get a prototype running. 3 months to tune it into something useful. Then you're maintaining it forever while LLM advancements force architectural rewrites. The LLM prompt is 1% of the work. The rest is an ever-changing agent architecture, integrations, memory, guardrails, and keeping up with a landscape that moves every quarter.
Can I just use Claude and MCP?
It makes for a great demo. Scaling it to production is the hard part. Even big, well-funded companies tried this and ended up buying a purpose-built tool. The gap between "look at this cool script" and a reliable system is massive.
What if I already built something?
Be honest with yourself. How much time does it take to maintain? Does it actually work during a fire? If the answers are "a lot" and "barely," you should probably switch to a vendor and let your engineers do real work.
Does the vendor see my prod data?
Depends on the model. Cloud vendors process data on their side. On-prem vendors deploy inside your VPC, so data never leaves. If compliance is watching, make sure "on-prem" is real and not just a roadmap slide. Make sure to ask for Zero Data Retention (ZDR) agreements as well.
What if the vendor dies?
Pick vendors with real revenue and customers. Work with a team that is in it for the long-haul. Your observability data stays in your tools anyway. The only thing you lose is the agent layer, not your infrastructure.
Stop Building. Start Fixing.
TierZero Production Agents integrate in an hour. You get 50+ native integrations, a real Context Engine, and an action engine that won't destroy production. See value this week.