Build vs buy: Your guide to scalable synthetic data via LLMs

The "LLM wrapper" dilemma

When it comes to generating synthetic data for software testing and development, a new question is surfacing in engineering planning meetings: "Why can't we just prompt an LLM to generate our synthetic data?"

It is a fair question. LLMs are incredible at understanding context and generating text. For a developer needing a simple JSON blob of ten fake users, building a quick Python script around the OpenAI API seems like an obvious, cost-effective choice.

However, at Tonic.ai, we’ve found that while LLMs are excellent semantic engines, they are poor database administrators. Think of it this way: an LLM is a brain, but a production-grade data platform is the body. A brain without a body cannot interact with the world, enforce constraints, or move data where it needs to go.

In this guide, we will break down the technical realities of building your own LLM-based data generator versus buying a purpose-built solution like Tonic Fabricate. At the end, you’ll find a checklist to help you consolidate your thoughts and choose the solution that’s best for you.

Signs that you should buy

Here are some signals that your data needs have outpaced what a raw LLM prompt can deliver.

1. You need deterministic validity, not just "vibes."

LLMs are probabilistic token predictors. They are prone to "hallucinating" data that looks plausible but is structurally invalid. If you ask an LLM for a credit card number, it might invent a string of 16 digits that fails a Luhn check. If you need data that respects strict mathematical logic—valid national IDs, real geolocation coordinates, or complex financial instruments—you need a solution that combines generative AI with deterministic code execution. Tonic Fabricate uses a hybrid generation engine that delegates creative text to the LLM but executes code in a secure Javascript sandbox for mathematically rigid fields.

2. You are managing complex relational integrity.

Generating a single table is easy. Generating 50 tables where the user_id in the orders table matches the id in the users table, while respecting foreign key constraints and business logic distributions, is exponentially harder. Raw LLMs often lose the "thread" of these relationships across large contexts. A purpose-built solution, on the other hand, provides you with a self-healing architecture—an iterative workflow that inspects schemas, identifies constraint violations during data insertion, and patches the generation code automatically.

3. You need a "body" for the "brain."

An LLM generates text, but it doesn't manage state. If you build in-house, you must also build the infrastructure to:

• connect to external databases (Oracle, SQL Server, Databricks);
• quickly revert unwanted changes made by overzealous AI;
• execute code written by the agent in a secure, scalable way; and,
• export to specific formats (PDF, CSV, Parquet).

Fabricate provides this "body" out of the box, allowing the LLM to act as a decision-maker rather than a standalone script.

4. You require enterprise-grade governance.

If this data is being used for new product development or sales demos, who has access to the generation schemas? How are you auditing what was created? Internal scripts rarely get investment in Role-Based Access Control (RBAC) or SSO. Leveraging a platform solution ensures that your synthetic data workflow is secure, auditable, and collaborative across teams.

5. You need "anti-assumption" logic.

When you build a wrapper around an LLM, you often get generic data because the model guesses your intent. Fabricate utilizes engineered anti-assumption protocols. It is explicitly forbidden from guessing; it queries your actual table schema, analyzes existing distributions (if allowed), and asks clarifying questions before writing a single line of code. This ensures the data reflects your business logic, not the training data of the base model.

Risks involved in buying

Buying software always introduces external dependencies. Here is what to watch for.

Signs that you should build

There are specific scenarios where a DIY approach using raw LLMs is sufficient.

1. You need unstructured, free-text data only.

If your only requirement is generating a smaller number of realistic reviews, blog posts, or email bodies, and you don't care about the underlying database structure or relational integrity, a direct call to GPT-4 is often sufficient. The "hallucinations" of an LLM are actually a feature here, providing creativity.

2. You are generating simple, flat JSON blobs.

If you need a list of 100 fake user profiles for a frontend mockup and there are no complex backend constraints (like unique constraints or cross-table dependencies), a simple script is a viable path.

3. Zero tolerance for external tools.

In extremely air-gapped environments where absolutely no third-party software can be introduced, building a strictly internal tool on top of a local LLM may be your only option.

Risks involved in building

If you decide to wrap an LLM yourself, be aware of the "hidden iceberg" of development costs.

1. The "scaffolding" burden. You aren't just writing a prompt; you are building a platform. You will need to handle token limits, context window management, error handling for malformed JSON outputs, and retry logic.
2. Security vulnerabilities and arbitrary code. Building an agent that autonomously writes code and connects to databases is a significant security risk. Without a robust, isolated sandbox, you are effectively allowing an AI to execute arbitrary code on your infrastructure. A "hallucinated" command or a buggy script could accidentally drop a table, expose credentials, or open a backdoor to your internal network. Securing this pipeline is non-trivial; it requires rigorous engineering to ensure the agent can only touch what you explicitly allow—an architecture that off-the-shelf solutions will have already built and battle-tested.
3. Lack of domain expertise. Data synthesis requires deep knowledge of data type constraints, distributions, and edge cases. Without years of experience (like the specific algorithms for generating format-preserving replacements), your internal tool will likely generate data that breaks your application in unexpected ways.
4. Maintenance of the "brain." Foundational models change rapidly. Maintaining your internal prompts and scaffolding to keep up with model deprecations and API changes requires dedicated engineering hours that could be spent on your core product.

Build vs buy checklist

Use this checklist to determine the best path for your synthetic data strategy.

• 1. Relational complexity

  • Buy if your data involves multiple tables, foreign keys, and strict referential integrity.
  • Build if you only need flat, non-relational data lists.

Tonic.ai's guidance

Buy

Relational integrity is where LLMs struggle most; Fabricate’s architecture guarantees consistency across databases.

• 2. Data fidelity and logic

  • Buy if you need mathematically valid data (IDs, financial data, coordinates) mixed with realistic text.
  • Build if "looks real enough" is acceptable and strict validation isn't required.

Tonic.ai's guidance

Buy

Fabricate’s hybrid engine ensures you don't have to choose between creativity and accuracy.

• 3. Connectivity and workflow

  • Buy if you need to push data to an API, Oracle, or SQL Server automatically.
  • Build if are comfortable manually copying/pasting JSON outputs or writing your own database connectors.

Tonic.ai's guidance

Buy

The "body" (connectors/integrations) is just as important as the "Brain" (LLM).

• 4. Scalability and speed

  • Buy if you need to generate millions of rows or spin up new datasets for sales demos and model training in minutes.
  • Build if you have plenty of developer time to troubleshoot and maintain internal scripts.

Tonic.ai's guidance

Buy

Your developers’ time is expensive. Don’t waste it building internal tools that already exist in a more robust form.

• 5. Scope & Frequency

  • Buy if you need a repeatable data pipeline for CI/CD, ongoing model training, or a team of developers who constantly need fresh data.
  • Build if this is a one-time "throwaway" request for a hackathon, a single spreadsheet, or a quick Monday morning mockup that will never be used again.

Tonic.ai's guidance

It depends

If you only need a small amount of simple data once, investing in a platform is overkill. A quick script or direct LLM prompt may be the right tool for a one-off job, or using Fabricate’s free tier can suffice.

How Tonic.ai can help

Tonic Fabricate is not just a UI wrapper; it is an agentic AI platform built on years of data synthesis experience. It bridges the gap between the creative potential of LLMs and the rigorous requirements of database engineering.

With Fabricate, you can:

• Generate complex data via chat: Describe your business logic, and let our Data Agent build the schema and data for you.
• Ensure deterministic quality: Leverage proprietary generators for IDs, numbers, and logic that LLMs cannot handle alone.
• Scale your workflow: Export to any major database, visualize data relationships, and manage access via RBAC.
• Power new use cases: From training AI models on edge-case scenarios to creating perfect, reliable datasets for sales demos.

Stop debugging prompts and start building your product with rapid, realistic, synthetic data.

Would you like to see the Fabricate Data Agent in action? Book a demo with our team to see how we handle your specific schema.