The first question every team asks is some version of “couldn’t we just build this?” The honest answer is yes — and for some teams, building is the right call. This post is a frank comparison so you can tell whether you’re one of them.
What “building in-house” actually means
An AI Gateway isn’t one feature. It’s the pile of controls that sit between your application and the LLM provider. Building one means building all of them:
- TLS termination with HSTS and end-to-end encryption (not CDN-terminated)
- JWT/JWKS verification with claim-based service scoping
- Centralized routing policy with per-tenant configuration
- PII scanning across multiple categories, with severity tiers and policy actions
- Provider abstraction (OpenAI / Anthropic / Google / Mistral / DeepSeek) with retry and streaming
- Encrypted raw retention with its own retention and access controls
- Structured usage records and append-only audit logs
- OpenAI-compatible response shape so existing SDKs work
We broke this down in Inside the Gateway. The rough build estimate for an equivalent is 17-31 engineer-weeks, plus ongoing operations. That’s not a number we invented to look good — it’s the sum of the work, hop by hop.
What 17-31 engineer-weeks buys
That estimate is for a gateway that meets a production bar — not a prototype. A prototype is two weeks: a Flask app that forwards OpenAI calls with an API key header. The 17-31 weeks is what it takes to get to something you’d be comfortable running for paying customers.
The breakdown:
- TLS termination with end-to-end encryption: 1-2 eng-weeks
- JWT/JWKS verification with claim-based service scoping: 2-3 eng-weeks
- Centralized routing policy with per-tenant configuration: 3-4 eng-weeks
- PII scanning with 17 categories and policy actions: 4-8 eng-weeks
- Provider abstraction with retry and streaming: 1-2 eng-weeks to build, ongoing to maintain
- Encrypted raw retention with AES-256 at rest (shipped) and application-layer envelope encryption (roadmap): 3-6 eng-weeks plus KMS setup plus legal review
- Structured usage and append-only audit: 2-4 eng-weeks
- OpenAI-compatible response shape and extensions: 1-2 eng-weeks
Notice the spread. PII scanning alone is 4-8 weeks because the categories are hard — Luhn validation for credit cards, check digits for JP mynumber, regexes for API keys that don’t false-positive on base64 blobs. Provider abstraction looks like 1-2 weeks to build but never stops costing — new models ship weekly, prices change monthly, feature flags (tool calls, vision, reasoning content) appear and disappear.
When building in-house is the right call
We tell teams to build when any of these is true:
- Single provider, single service, no compliance. If you call one OpenAI endpoint from one internal tool and nobody asks auditors where prompts live, a direct call is fine. The gateway is overkill.
- You have a platform team with nothing else to do. If the platform team exists and has runway, the work is a good investment. The controls are reusable across every AI feature you’ll ever ship.
- You have requirements SchneeAI doesn’t meet. Specific data residency, specific provider, specific audit log shape. If we don’t fit, build.
The first case is the most common. Most teams shipping their first AI feature don’t need a gateway. They need a working feature.
When SchneeAI is cheaper
SchneeAI is cheaper than building when the alternative is building well. The comparison isn’t “SchneeAI vs a Flask proxy” — it’s “SchneeAI vs the thing you’d actually run in production.”
During the design-partner phase, SchneeAI passes provider cost through at 1× (no markup). The schema is wired so per-model margins can be introduced later without a migration. So the question isn’t “is SchneeAI cheaper than provider cost” — it’s “is SchneeAI cheaper than the engineer-weeks to build equivalent controls.”
For a team of five engineers, 17-31 eng-weeks is 3-6 months of one engineer’s time — or 60-120% of a quarter for the whole team. That’s the entire AI feature roadmap, spent on infrastructure.
The other cost is the one teams underestimate: maintenance. Provider abstraction isn’t build-once. New models ship weekly. Prices change monthly. Feature flags appear. A team that builds their own adapter layer spends a non-trivial fraction of engineering time on a layer that produces no business value. Outsourcing that layer to a dedicated library or service lets the team spend that time on features.
The break-even calculation
For most teams, the break-even is “before the first invoice.” If you’d spend 17 engineer-weeks building the equivalent, SchneeAI at provider-cost passthrough is cheaper on day one — and the gap widens as provider APIs change and your team would otherwise be maintaining the adapter.
The calculation flips when you have a platform team with runway and no higher-priority work. In that case, the controls are reusable and the investment pays off across multiple features. We’ll still be here when you finish building — and if you hit a category we don’t cover, the architecture is OpenAI-compatible so you can mix.
Next steps
- Read the architecture: Inside the Gateway — the eight hops, in detail.
- Estimate your costs: Cost Calculator — model a specific prompt and token count.
- Compare providers: LLM Pricing Benchmark — monthly-refreshed price table.
- Try the integration: API Request Builder — copy-pasteable snippets in four languages.