Spikefrost vs building AI agents in-house
Should you build your enterprise AI agent platform in-house or buy one? An honest look at the trade-offs in time, security, and total cost — and when each makes sense.
Every team adopting AI agents faces the same fork: assemble a platform in-house, or run on one that exists. Here's an honest comparison.
What "in-house" really means
Writing an agent that calls a few tools is a weekend. Running agents in production for an enterprise is a platform: tenant isolation, identity-based access control down to the tool, short-lived credential handling, full audit logging, durable job state, scaling, and the team to maintain all of it as models and requirements change. The agent logic is a small fraction of that work.
Side by side
| Build in-house | Spikefrost | |
|---|---|---|
| Time to first production agent | Weeks to months | Same day |
| Isolation, RBAC, audit | You design and maintain it | Built in, enforced by the runtime |
| Credential handling | You build secure secret + token flow | Scoped, short-lived, per-call by default |
| Scaling & runtime ops | Your platform team owns it | Managed edge runtime |
| Ongoing maintenance | Continuous | Handled for you |
| Differentiation | The runtime (undifferentiated) | Your agents and your domain logic |
When to build in-house
If agents are your core, differentiating product and you have a platform team prepared to own the runtime for years, building can make sense — you get total control. Be honest about the maintenance commitment.
When to use Spikefrost
For most teams, the runtime is undifferentiated heavy lifting. Spikefrost lets you keep what's yours — the agents and your domain logic — and not rebuild isolation, RBAC, credentials, and audit. You describe the agent and ship the same day, on a runtime where security is the default.
Book a demo, or see the enterprise AI agents guide for the governance model you'd otherwise be building yourself.
Frequently asked questions
Is it cheaper to build AI agents in-house?
Rarely, once you count the whole cost. The agent logic is the easy part; the runtime — isolation, RBAC, credential handling, audit, scaling, and ongoing maintenance — is where in-house projects spend most of their time and budget.
When does building in-house make sense?
When agents are core, differentiating IP and you have a platform team to own the runtime long-term. For most teams, the runtime is undifferentiated heavy lifting that's better bought.