Apr 22, 2025·8 min
How LLMs Choose Databases From Product Needs—and Fail
How LLMs map product needs to database choices, what they miss, and a practical checklist to validate recommendations before you commit to a stack.
How LLMs map product needs to database choices, what they miss, and a practical checklist to validate recommendations before you commit to a stack.
Teams ask LLMs to recommend a database for the same reason they ask them to draft emails or summarize specs: it’s faster than starting from scratch. When you’re staring at a dozen options—PostgreSQL, DynamoDB, MongoDB, Elasticsearch, Redis, ClickHouse, and more—an LLM can quickly produce a shortlist, outline trade-offs, and provide a “good enough” starting point for a team discussion.
Used well, this also forces you to articulate requirements you might otherwise keep vague.
In plain terms, you describe the product (“a marketplace with listings and chat”), the data (“users, orders, messages”), and the constraints (“must scale to 1M users, needs fast search, low ops effort”). The LLM then maps those needs to common architectural patterns:
That mapping can be genuinely useful early on, especially when the alternative is a blank page.
An LLM recommendation is best treated as a hypothesis, not an architecture verdict. It can help you:
But it can’t know your real traffic shape, data growth, team skills, vendor constraints, or operational tolerance without careful inputs—and even then it won’t run production tests.
LLMs tend to fail in predictable ways: leaning on popular rules of thumb, guessing missing details, overlooking transactions and consistency needs, assuming performance without benchmarks, and underestimating cost and operational burden.
The rest of this article breaks down those failure modes and ends with a practical checklist to validate any LLM database advice before you commit.
When you ask an LLM to “recommend a database,” it doesn’t evaluate databases the way an engineer would. It converts your prompt into inferred requirements, matches those to patterns it has seen before, and then produces an answer that reads like a decision.
The inputs aren’t just explicit details you provide (traffic, data size, consistency needs). The model also uses:
Because many prompts are incomplete, the model often fills gaps with implicit assumptions—sometimes correctly, sometimes not.
Most responses land in three layers:
The result can feel like a clear recommendation, but it’s often a structured summary of conventional options.
LLMs generalize from examples; they don’t run your workload, inspect your schema, or benchmark queries. If the training data strongly associates “high scale” with “NoSQL,” you may get that answer even when a well-tuned SQL system would fit.
Confident wording is a style, not a measurement. Unless the model explicitly states assumptions (“I’m assuming mostly append-only writes and eventual consistency is acceptable”), certainty can hide the real uncertainty: missing inputs and untested performance claims.
When people say “pick a database based on product needs,” they often mean far more than “we store users and orders.” A good database choice reflects what the product does, how it must behave under stress, and what your team can realistically operate.
Start with the shape of the product: the core entities, how they relate, and which queries power real workflows.
Do you need ad-hoc filtering and reporting across many attributes? Do you rely on joins across relationships? Are you mostly reading a single record by ID, or scanning time ranges? These details determine whether SQL tables, document models, wide-column patterns, or search indexes fit best.
Databases are chosen as much by constraints as by features:
A system that can tolerate a few seconds of delay is very different from one that must confirm a payment in under 200ms.
Even a “perfect” data model fails if operations don’t fit:
Compliance requirements can narrow choices quickly:
LLMs often infer these needs from vague prompts—so being explicit here is the difference between a helpful recommendation and a confident mistake.
LLMs often map a few stated needs (“real-time,” “scales,” “flexible schema”) to a familiar category label (“use NoSQL,” “use Postgres”). That can be useful for brainstorming, but the reasoning drifts when the model treats database features as if they were the same thing as product requirements.
A feature list (transactions, JSON support, full-text search, sharding) sounds concrete, yet product needs usually describe outcomes: acceptable latency, correctness rules, auditability, team skills, migration constraints, and budget.
An LLM can “check off” features and still miss that the product needs predictable support workflows, a mature ecosystem, or a hosting option your company is allowed to use.
Many recommendations assume that if a database can store a data type, it will serve the product well. The hard part is the relationship between data and queries: how you’ll filter, join, sort, and aggregate—at what volumes and with what update patterns.
Two systems that both “store user events” can behave very differently depending on whether you need:
LLMs may say “Database X is fast,” but performance depends on schema choices, indexes, partitioning, query patterns, and concurrency. Small changes—like adding a composite index or avoiding unbounded scans—can flip the result. Without representative data and queries, “fast” is just a guess.
Even if two databases can technically meet requirements, the better choice may be the one your team can run reliably: backups and restore time, monitoring, on-call load, vendor lock-in, cost predictability, and compliance.
LLMs tend to underweight these realities unless you explicitly provide them.
LLMs often answer database questions by reaching for widely repeated “rules,” like “NoSQL scales better” or “Postgres can do everything.” These shortcuts sound confident, but they flatten the messy reality of products: what you store, how you query it, and what failure looks like when things go wrong.
A common pattern is assuming that if you mention growth, high traffic, or “big data,” the safest pick is a NoSQL database. The problem is that “scale” is rarely the first unsolved problem. Many apps hit limits because of:
In those cases, switching databases doesn’t fix the root cause—it just changes the tools.
Rules of thumb also gloss over requirements that heavily influence database fit. An LLM might recommend a document store while overlooking that you need:
Those needs don’t automatically rule out NoSQL, but they raise the bar: you may need careful schema design, extra application logic, or different trade-offs than the LLM implied.
When a recommendation is built on a slogan instead of your actual access patterns, the risk isn’t just a suboptimal choice—it’s costly re-platforming later. Migrating data, rewriting queries, and retraining the team tends to happen exactly when you can least afford downtime.
Treat “rules” as prompts for questions, not answers. Ask what you’re scaling (reads, writes, analytics), what must be correct, and what queries you can’t avoid.
LLMs are good at turning a short description into a confident database pick—but they can’t invent the missing constraints that actually determine whether a choice works. When the inputs are vague, the recommendation becomes a guess dressed up as an answer.
Words like “real-time,” “high traffic,” “scalable,” or “enterprise-grade” don’t map cleanly to a specific database. “Real-time” might mean “updates within 5 seconds” for a dashboard—or “sub-50ms end-to-end” for trading alerts. “High traffic” could be 200 requests per second or 200,000.
Without hard numbers, an LLM may default to popular heuristics (e.g., “NoSQL for scale,” “Postgres for everything”) even when the true needs point elsewhere.
If you don’t provide these, the model will silently assume them:
The most damaging omissions are often query-shaped:
A database that excels at key-value access can struggle when product suddenly needs flexible filtering and reliable reporting.
Treat “database selection” as a two-step interaction: first collect constraints, then recommend. A good prompt (or internal checklist) should require numbers and example queries before naming any engine.
A common LLM mistake is recommending a database “category” (SQL, document, graph, wide-column) without validating whether the product’s data actually fits that model. The result is choosing a store that sounds right for the workload, but fights the structure of the information you need to represent.
LLMs often gloss over relationship depth and cardinality: one-to-many vs many-to-many, nested ownership, shared entities, and how often users traverse across them.
A document database might feel natural for “user profiles,” but if your product constantly answers cross-entity queries—“all projects where any member’s role changed in the last 7 days,” or “top 20 tags across all teams filtered by compliance status”—you’re no longer just fetching a document; you’re joining concepts.
When those joins are frequent, you either:
Duplication isn’t free. It increases write amplification, makes updates harder to keep consistent, complicates audits, and can create subtle bugs (“which copy is the source of truth?”). LLMs sometimes recommend denormalization as if it’s a one-time modeling choice, not an ongoing operational burden.
Before accepting an LLM recommendation, force a quick reality test:
If the model and queries don’t align, the recommendation is noise—even if it sounds confident.
LLMs often treat “consistency” as a preference rather than a product constraint. That leads to recommendations that look reasonable on paper (“use a scalable NoSQL store”) but fall apart when real user actions require atomic, multi-step updates.
Many product flows are not a single write—they’re several writes that must either all happen or none happen.
Payments is the classic example: create a charge, mark an invoice paid, decrement account balance, and append an audit record. If any step fails after the first succeeds, you’ve created a mismatch that users and finance will notice.
Inventory is similar: reserve stock, create an order, and update availability. Without transactions, you can oversell during spikes or partial failures.
LLMs sometimes equate eventual consistency with “the UI can refresh later.” But the question is whether the business action can tolerate divergence.
Booking conflicts show why this matters: two users try to book the same time slot. If the system accepts both and “resolves later,” you’re not improving UX—you’re creating customer support issues and refunds.
Even with a database that supports transactions, the surrounding workflow needs clear semantics:
When an LLM ignores these, it may recommend architectures that require expert-level distributed systems work just to reach “normal” product correctness.
LLMs often recommend a “fast” database as if speed were an intrinsic property of the engine. In practice, performance is an interaction between your workload, schema, query shapes, indexes, hardware, and operational settings.
If you don’t specify what needs to be fast—p99 latency for single-row reads, batch analytics, ingestion throughput, or time-to-first-byte—an LLM may default to popular choices.
Two products can both say “low latency” and still have opposite access patterns: one is key-value lookups; the other is search + filtering + sorting across many fields.
Performance advice also drifts when models ignore:
An LLM may assume caches will save you, but caches only help predictable access patterns. Queries that scan large ranges, sort by non-indexed fields, or use ad-hoc filters can miss cache and stress disk/CPU.
Small changes in query shape (e.g., OFFSET pagination vs keyset pagination) can flip performance outcomes.
Instead of trusting generic “X is faster than Y,” run a lightweight, product-shaped test:
Benchmarks won’t predict everything, but they quickly reveal whether an LLM’s performance assumptions match reality.
LLMs often optimize for fit on paper—data model, query patterns, scalability buzzwords—while glossing over what makes a database survivable in production: operations, failure recovery, and the real bill you’ll pay month after month.
A database recommendation isn’t complete unless it answers basic questions: How do you take consistent backups? How fast can you restore? What’s the disaster recovery plan across regions?
LLM advice frequently skips these details, or assumes they’re “built in” without checking the fine print.
Migration is another blind spot. Switching databases later can be expensive and risky (schema changes, dual writes, backfills, query rewrites). If your product is likely to evolve, “easy to start” isn’t enough—you need a realistic migration path.
Teams don’t just need a database—they need to operate it.
If the recommendation ignores slow query logs, metrics, dashboards, tracing hooks, and alerting, you may not notice issues until users complain. Operational tooling varies widely between managed offerings and self-hosted setups, and between vendors.
LLMs tend to underestimate cost by focusing on instance size and forgetting multipliers:
A “best” database that your team can’t confidently run is rarely best. Recommendations should align with team skills, support expectations, and compliance needs—otherwise operational risk becomes the dominant cost.
LLMs sometimes try to “solve everything at once” by proposing a stack like: Postgres for transactions, Redis for caching, Elasticsearch for search, Kafka + ClickHouse for analytics, plus a graph database “just in case.” This can sound impressive, but it’s frequently a premature design that creates more work than value—especially early in a product.
Multi-database designs feel like a safe hedge: each tool is “best” at one thing. The hidden cost is that every additional datastore adds deployment, monitoring, backups, migrations, access control, incident response, and a new set of failure modes.
Teams then spend time maintaining plumbing instead of shipping product features.
A second (or third) database is usually justified when there’s a clear, measured need that the primary database can’t meet without unacceptable pain, for example:
If you can’t name the specific query, latency target, cost constraint, or operational risk driving the split, it’s probably premature.
Once data lives in multiple places, you face hard questions: Which store is the source of truth? How do you keep records consistent during retries, partial failures, and backfills?
Duplicated data also means duplicated bugs—stale search results, mismatched user counts, and “it depends which dashboard you look at” meetings.
Start with one general-purpose database that fits your core transactions and reporting. Add a purpose-built store only after you can (1) show the current system failing against a requirement and (2) define an ownership model for sync, consistency, and recovery.
Keep the escape hatch, not the complexity.
LLMs can be helpful for generating a first draft database recommendation, but you should treat it like a hypothesis. Use the checklist below to validate (or reject) the suggestion before you commit engineering time.
Turn the prompt into explicit requirements. If you can’t write it clearly, the model likely guessed.
Draft the real entities and relationships (even a sketch). Then list your top queries and access patterns.
Translate “it should be fast and reliable” into measurable tests.
Use realistic data shapes and query mixes, not toy examples. Load a representative dataset, run queries under load, and measure.
If the LLM proposed multiple databases, test the simplest single-database option first, then prove why splitting is necessary.
If you want to speed up this step, a practical approach is to prototype the product slice that drives the database choice (a couple of core entities + the key endpoints + the most important queries). Platforms like Koder.ai can help here: you can describe the workflow in chat, generate a working web/backend app (commonly React + Go + PostgreSQL), and iterate quickly while you refine schema, indexes, and query shape. Features like planning mode, snapshots, and rollback are especially useful when you’re experimenting with data models and migrations.
Write a short rationale: why this database fits the workload, what trade-offs you’re accepting, and what metrics would force a re-evaluation later (e.g., sustained write growth, new query types, multi-region requirements, cost thresholds).
Treat it as a hypothesis and a way to accelerate brainstorming. Use it to surface trade-offs, missing requirements, and a first-pass shortlist—then validate with your team, real constraints, and a quick proof-of-concept.
Because your prompt is usually missing hard constraints. The model will often:
Ask it to list assumptions explicitly before it names any database.
Provide numbers and examples, not adjectives:
If you can’t specify these, the recommendation is mostly guesswork.
Use it to generate a requirements checklist and candidate options, then force a schema-and-query reality check:
“Scale” isn’t a database type; it’s what you’re scaling.
Many apps hit limits due to:
A well-designed relational system can scale far before a database switch is the right fix.
They’re often under-specified in recommendations.
If your product needs multi-step updates that must succeed or fail together (payments, inventory, bookings), you need clear support for:
If an LLM doesn’t ask about these, push back before adopting its suggestion.
Because data relationships drive query complexity.
If you frequently need cross-entity queries (filters, joins, aggregations across many attributes), a document model may force you to:
That increases write amplification, inconsistency risk, and operational complexity.
Performance depends on your workload, schema, indexes, and concurrency—not the brand name.
Run a small, product-shaped test:
Because each extra datastore multiplies operational surface area:
Start with one general-purpose database for the core workload. Add a second store only after you can point to a measured requirement the first one can’t meet.
Ask for a cost model that includes the real multipliers:
Also require an operations plan: backup/restore steps, RPO/RTO targets, and how you’ll detect slow queries and capacity issues.