UnderPeaks, the team behind the headless CMS UnderPeaks Core, says supporting five databases through one adapter took roughly 4–5 months, against about 2–3 weeks for a single database. That is the author’s own approximate estimate, not an independently timed benchmark. The same retrospective, published on DEV Community on Sep 29, 2026, explains what the extra months bought and where they went. This piece walks through its claims, attributes them to the author, and turns them into a decision guide.
What UnderPeaks built
According to the article, UnderPeaks Core is a headless CMS that generates Flutter and Next.js apps from a shared data model. It supports Supabase, PostgreSQL, MySQL, MongoDB and Firebase through a single DBAdapter interface. Routes never import a database driver. They call whichever adapter is configured, and each adapter implements the same operations: create, read, update, delete and authentication helpers.
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Why the author says it was worth doing
The stated motivation is database choice over time. The author names three kinds of reader:
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- people who “already run a database and don’t want to migrate to use a CMS”;
- people who “build for clients with different infrastructure”;
- people who “want the option to change their mind later without a rewrite.”
The author calls the portability “insurance” and concedes that many projects will never switch. The value is the option, not a guaranteed migration.
What it cost: the author’s estimate
| Scope | Approximate build time | Basis |
|---|---|---|
| One database | 2–3 weeks | UnderPeaks author’s estimate, 2026 |
| Five databases | 4–5 months | UnderPeaks author’s estimate, 2026 |
The article gives no measurement method and no comparison projects, so treat this as one team’s experience. It should not be read as an industry norm for multi-database adapters.
The author’s explanation is that typing out five adapters was not the expensive part: “The cost isn’t writing five adapters. It’s that every feature now has five edge cases.” Differences in authentication, file storage, pagination and filtering each needed handling per backend, and a bug fix had to be checked against all five.
The edge cases the author describes
Primary keys
SQL tables commonly use id, but a model can specify another key such as product_id. Firebase document IDs may not be stored as fields in the document. So the interface passes the key explicitly for operations like update instead of assuming a name.
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The author reports that parallel queries are acceptable with Firebase or Supabase’s HTTP client, but with pooled PostgreSQL or MySQL connections they can exhaust the pool or interleave. The SQL adapters therefore read sequentially. This is the project’s account of its own implementation, not a universal rule for every client or workload.
Response shapes
One code path returned { data } and another returned { records }. Generated applications that expected one shape received undefined. It is a small inconsistency, but a common interface only works if every adapter returns exactly the same structure.
Schema
SQL engines need physical tables and columns, while MongoDB and Firebase accept less constrained input. The adapter layer creates tables where required and enforces structure where the engine will not.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Portable adapter or single engine?
The author acknowledges that a database-specific tool can use engine features directly, move faster with fewer edge cases, and tune performance more deeply. A portability layer must target the common denominator or add backend-specific handling. The article says a Postgres-native tool is a reasonable choice when you know the project will stay on Postgres.
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| Question | Leans portable if… | Leans single-engine if… |
|---|---|---|
| How many engines must you support now? | More than one | Exactly one |
| Do clients bring their own infrastructure? | Yes, and it varies | No, you choose the stack |
| How likely is a future database change? | Plausible | Unlikely |
| How much engine-specific functionality do you need? | Mostly common CRUD and auth | Heavy use of native features |
| Can you sustain testing across backends? | Yes, on every change | Not realistically |
The practical lesson is to budget for the ongoing compatibility work, not just the initial build. If the “insurance” will probably never be claimed and you lack the capacity to test every backend, a single-engine design is the more honest choice.
Source and limits
Everything above comes from one article by UnderPeaks, “Why we built a five-database adapter layer (and what it cost in build time),” on DEV Community. It was read through a search excerpt rather than the full page, and no independent corroboration of the figures or implementation claims was found.
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