Veto vs DIY: Build vs Buy for AI Agent Authorization
This is the real comparison. Most teams don't evaluate Veto against Alter or ContextFort -- they evaluate it against building their own. Here's an honest breakdown of what building agent authorization actually looks like, what it costs, and when it makes sense.
Quick verdict
Build if authorization is your core product, you have dedicated security engineers, and you have 3-6 months of runway before you need production coverage. Use Veto if you need authorization in production this week, not this quarter. Most teams underestimate the scope by 3-5x because the initial if/else checks are easy -- it's the audit logging, approval workflows, policy versioning, framework integrations, and ongoing maintenance that eat months.
What "build your own" actually means
Every team that builds their own starts the same way: a few if/else checks before tool calls. It works for a demo. Then requirements accumulate.
Basic checks
If/else guards before tool calls. Hardcoded thresholds. Works for 2-3 tools.
Policy abstraction
Hardcoded checks don't scale. You build a rule engine, a config format, an evaluator. Now you're maintaining a DSL.
Approval workflows
Product wants human-in-the-loop. You need async state management, notification routing, timeout handling, and a UI for approvers.
Audit and compliance
Compliance wants tamper-proof logs. You need structured logging, retention policies, export formats, and query capabilities.
Framework integrations
You're using LangChain, but another team uses OpenAI directly, and someone wants MCP support. Each integration is a separate wrapper.
Maintenance
Framework APIs change. New tools get added. Policies need updating. Security patches. On-call rotation. 20-40 hours/month.
Feature comparison
| Capability | Veto | DIY |
|---|---|---|
| Declarative YAML policy engine | ||
| Tool-call interception | ||
| Human-in-the-loop approvals | ||
| Audit trail with retention | ||
| LLM-backed semantic checks | ||
| LangChain / LangGraph integration | ||
| OpenAI / Anthropic integration | ||
| MCP gateway compatibility | ||
| Policy versioning and rollback | ||
| Dashboard and analytics | ||
| Open source SDK | ||
| Self-hosted option | ||
| Automatic security patches | ||
| Full control over internals | ||
| Zero external dependencies |
DIY can technically implement any feature. The question is whether your team will, and how long it will take. Most DIY implementations cover tool-call interception and basic checks. Few build approval workflows, policy versioning, or framework integrations.
Time to production
Veto
Total: Half a day to production
DIY
Total: 10-19 weeks for full coverage
Real cost comparison
Engineering time is the cost people forget. A senior engineer at $200K/year costs roughly $100/hour fully loaded (salary + benefits + equipment + overhead). That's the number below.
| Cost factor | Veto | DIY |
|---|---|---|
| Initial build | $0 | $40,000 - $76,000 |
| Monthly subscription | $0 - $149/mo | $0 |
| Monthly maintenance (hours) | 0 | 20-40 hrs |
| Monthly maintenance ($) | $0 | $2,000 - $4,000 |
| Security incident response | Handled by Veto | Your on-call rotation |
| Opportunity cost | None | 10-19 weeks of eng time |
| Year 1 total | $0 - $1,788 | $64,000 - $124,000 |
DIY estimate: 10-19 weeks at $100/hr for initial build, plus 20-40 hrs/month ongoing. Does not include opportunity cost of features those engineers could have shipped instead.
What DIY teams typically miss
The initial implementation is the easy part. Here's what catches teams 3-6 months in:
Policy evaluation edge cases
What happens when two rules conflict? When a rule references a field that doesn't exist? When the policy file has a syntax error? Production policy engines need conflict resolution, graceful degradation, and validation -- not just happy-path evaluation.
Async approval state management
Human-in-the-loop approvals are distributed systems problems. What if the approver doesn't respond? What if two approvers respond simultaneously? What if the agent times out? You need timeouts, escalation paths, and idempotent state transitions.
Audit log integrity
Compliance teams don't just want logs -- they want tamper-evident, queryable, exportable logs with configurable retention. Building this on top of your existing logging is a separate infrastructure project.
Framework version churn
LangChain, OpenAI, and Anthropic ship breaking changes regularly. Each framework integration needs ongoing maintenance. When LangGraph changes its tool interface, someone on your team needs to update the wrapper.
When to choose each
Use Veto when
- -You need production authorization this week
- -Your team doesn't have dedicated security engineers
- -You use multiple agent frameworks
- -You need approval workflows and audit trails
- -Your engineering time is better spent on product
- -You want the safety net of open source (fork if needed)
Build DIY when
- -Authorization is your core product differentiator
- -You have dedicated security engineers with runway
- -Requirements are genuinely unique (most aren't)
- -Regulatory requirements mandate zero external dependencies
- -You can justify $60K-$120K+ in annual engineering cost
- -You have 3-6 months before you need production coverage
Frequently asked questions
Can I start with DIY and migrate to Veto later?
Is Veto's SDK really open source?
What's the real catch with "free" DIY?
What if I only need basic checks -- no approvals or audit?
How do I justify this to my team?
Ship authorization this afternoon. Not this quarter.