Licensed, synthetic or scraped: choosing AI training data
Licensed, synthetic and scraped data do different jobs: licensed data supplies real business context, outcomes and documented rights; synthetic data adds cheap, controllable volume but cannot add ground truth its generator lacks; scraped public data gives breadth but brings contamination, quality and rights risk. AI teams often combine them, using public data for general coverage, licensed data for domain depth and held-out evaluation, and synthetic data to expand around real examples rather than replace them.
Key takeaways
- Licensed data is the most direct route to real internal business context, outcomes and a documented rights position, but supply, cost and lead time limit it.
- Synthetic data scales cheaply and can target cases you specify, yet it cannot supply ground truth that its generator does not have.
- Scraped data gives breadth at low acquisition cost while bringing contamination, quality and unsettled rights risk.
- Keep real data in the training mix and add synthetic data to it rather than replacing it.
- Held-out licensed data that has never been published makes the cleanest evaluation set of the three.
What each source is
Licensed data is data you obtain under a written license from the party that holds the rights to it: a business's operational records, a data vendor's archive, or new recordings made under agreement. The license states what you may do with it.
Synthetic data is data produced by a model, simulator or program rather than recorded from real activity: generated dialogues, rewritten documents, simulated environments, or procedurally generated tasks with automatic checks.
Scraped data is content collected from public websites by crawlers, whether you run the crawl yourself or use a public web corpus. Public access does not mean a license to train.
Comparison at a glance
Each source wins on different dimensions: licensed data on context, outcomes and rights clarity, synthetic data on cost and control, and scraped data on breadth.
| Licensed data | Synthetic data | Scraped public data | |
|---|---|---|---|
| Strongest for | Domain depth, real outcomes, private eval sets | Volume, controlled variation, tasks with automatic checkers | Broad language and world knowledge |
| Internal context and outcomes | Present where the source systems recorded them | Only what the generator invents | Rare outside open-source projects; mostly finished outputs |
| Distribution shift risk | Low within the source's domain; higher if from one source | Follows the generator's assumptions, not observed work | Skewed toward what gets published |
| Eval contamination risk | Low if never published and held out | Can be circular when generator and evaluated model are related | High; benchmarks and solutions circulate online |
| Rights position | Defined by the license; depends on chain of title | Depends on generator terms and any source data used | Unsettled for many uses: copyright, site terms, opt-outs, privacy |
| Personal data | De-identified before delivery to agreed requirements | Usually low; can leak from seed records or the generator | Present and hard to remove completely |
| Cost profile | Highest acquisition cost | Low marginal cost; design and verification dominate | Low acquisition cost; heavy filtering |
| Time to first data | Longest; depends on supply | Shortest once a pipeline exists | Short |
| Typical failure | No matching supply, or one source's quirks | Drift toward the typical; missing tails | Contamination, noise, rights disputes |
Where scraped data falls short
Scraped data falls short when you need a specific capability or a clean measurement, though it remains the cheapest route to the breadth that general-purpose pretraining relies on.
- Contamination. Public benchmarks, their solutions and discussions of them circulate online, so a model trained on crawled text may already have seen your test items. Eval sets built from public sources share the problem.
- Missing internal work. The web holds mostly finished outputs: published articles, answered questions, released code. Open-source projects are a partial exception, but the internal notes, approvals, tool actions and outcomes of everyday business work rarely appear online.
- Distribution shift. What gets published is not what gets done, so a model trained on it meets a different distribution in deployment. Polished final versions, popular topics and some languages are overrepresented compared with everyday business work.
- Quality. Crawls need deduplication and filtering of spam, boilerplate and machine-generated text.
- Rights. Copyright, website terms and privacy law all apply. In the EU, the general text and data mining exception does not cover works whose rightholders have expressly reserved their rights "in an appropriate manner, such as machine-readable means" (Directive (EU) 2019/790, Article 4). The EU AI Act requires providers of general-purpose AI models to put in place a copyright compliance policy that identifies and honors those reservations, and to publish a sufficiently detailed summary of their training content. Under the GDPR, personal data stays personal data after someone publishes it. In the United States, fair use is assessed case by case, and courts are still working out how it applies to training.
Where synthetic data falls short
Synthetic data falls short wherever its generator does, because it cannot contain knowledge the generating model, simulator or program lacks. Within that limit it is fast and controllable: you can generate the scenario mix you want, target cases you can describe, and, for tasks with an automatic checker such as code that must pass tests, filter outputs for correctness at scale.
- No new ground truth. A generated support ticket has no record of what actually resolved it; a generated claim has no adjuster decision behind it. Without an automatic checker, nothing in the pipeline shows whether the output matches how the work is really done.
- Drift toward the typical. Generators reproduce common patterns more readily than rare ones, so long-tail cases must be specified by hand, and you can only specify the cases you already know about.
- Model collapse. Research published in Nature in 2024 found that training successive generations of models indiscriminately on model-generated content made the tails of the original distribution disappear. A separate 2024 preprint found that keeping the original real data and adding synthetic data to it, rather than replacing it, avoided collapse in the settings it tested. The practical reading: use synthetic data as a supplement to real data, not a substitute.
- Circular evaluation. An eval set generated by one model can favor models that share its habits and blind spots.
- Rights still apply. Some model providers' terms restrict using outputs to build competing models. Synthetic data generated from licensed or personal records can remain subject to the source license and to privacy law, especially if it reproduces source records closely.
Where licensed data falls short
Licensed data falls short on supply, speed, cost and breadth of sources, even though it carries the context and outcomes the other two lack.
- Supply is not guaranteed. You can license only what a business has recorded and agrees to license.
- Slower and costlier to acquire. Sourcing, rights review, de-identification and negotiation come before the first record arrives.
- It reflects its sources. One company's records carry its policies, tools, customers and tagging habits. Several sources generalize better than one.
- It is messy. Real records have missing outcomes, inconsistent labels and stale fields, and de-identification removes some signal.
- Rights depend on chain of title. A license is only as good as the licensor's right to grant it, which is why due diligence matters.
Cost and time trade-offs
Scraped data is cheapest to acquire, synthetic data is cheapest per extra record once a pipeline exists, and licensed data costs the most and takes the longest to arrive.
- Scraped: low acquisition cost, but real engineering for crawling, deduplication, filtering and personal-data removal, plus legal exposure that is hard to price.
- Synthetic: low marginal cost per record once a pipeline exists. The cost sits in task design, generation compute and verification, and iteration is fast.
- Licensed: the highest acquisition cost and the longest lead time, front-loaded into sourcing, diligence, preparation and negotiation, with refreshes as an ongoing cost. In return you get records that cannot be generated or crawled, under terms you can point to.
Which source for which job
Use public data for breadth, licensed data for real outcomes and clean evaluation, and synthetic data for volume on checkable tasks and for rare scenarios.
| If you need | Start with |
|---|---|
| Broad language and general knowledge | Filtered public data and licensed corpora |
| Domain behavior with real outcomes | Licensed operational records |
| An uncontaminated eval set | Held-out licensed data or newly written items |
| Volume on a task with an automatic checker | Synthetic data |
| Rare or unsafe scenarios | Synthetic data, validated against real cases |
| Data nobody has recorded yet | A new collection program |
How teams combine them
Many teams use all three. Common patterns:
- Public data for breadth, licensed data for depth. General knowledge comes from filtered public data; domain behavior comes from licensed records of the work itself.
- Licensed seeds, synthetic expansion. Real records seed variations, harder cases and counterfactuals. Confirm first that the license allows synthetic derivatives; see AI data license terms explained.
- Held-out licensed data for evaluation. Real records that were never published and never trained on give a contamination-free test of whether a model does the work the way skilled people did; see private evaluation sets.
- Real data as the anchor. Keep real data in the training mix and add synthetic data to it, in line with the model-collapse research above.
- Real distributions as the check. Compare generated data with licensed records on length, structure, category mix and outcome rates, and fix the generator where they diverge.
Where SourceX fits
SourceX sources licensed data: operational archives and workflow datasets from established businesses, plus new recordings of hands-on work through a separate collection path. Public scraped content is secondary to that work, and SourceX does not train models. Its supply fits where licensed data is strongest: domain depth, real outcomes and held-out evaluation. Availability depends on which businesses hold matching data and agree to license it. Send a data request to describe what you need.
Related dataset types
- Enterprise workflow and task execution histories
Linked task trajectories from request to outcome, across every tool the work touched
- Human feedback and QA-scored work
Work items with scores, verdicts and corrections from the people who reviewed them
- Software engineering histories (issues, PRs, reviews)
Issues linked to commits, pull requests, code review, CI runs, deploys and incidents
- Contact center call recordings and transcripts
Recorded service and support calls with diarized transcripts, dispositions and QA scores
Questions
Can synthetic data replace real data for training AI models?
Rarely on its own. Synthetic data works well for volume, controlled variation and tasks with an automatic checker, such as code that must pass tests. It cannot supply facts its generator does not have, such as how a real case was resolved, and research on model collapse suggests that replacing real data with model-generated data across generations degrades models. A common pattern is to use synthetic data to extend a base of real data.
Is it legal to train AI models on scraped web data?
It depends on the jurisdiction, the content and the use, and the law is still developing. Copyright, website terms and privacy law all apply. In the EU, the general text and data mining exception does not cover works whose rightholders have reserved their rights, for example in machine-readable form. In the United States, fair use is assessed case by case and courts are still applying it to training. This is general information, not legal advice.
What is model collapse?
Model collapse is the loss of quality and diversity that can occur when models are trained on data generated by earlier models, generation after generation. Rare patterns in the original data disappear first, so outputs drift toward the typical. Research has found the effect when each generation trains mainly on model output, and other research found that keeping the original real data and adding synthetic data to it avoided collapse in the settings tested.
Why use licensed data for evaluation sets?
Because licensed records that were never published cannot have leaked into a model's training data from the web, so scores reflect capability rather than memorization. Real records also carry known outcomes, such as how a case was resolved or whether a change was reverted, which give graders a reference answer. Keep the eval set out of every training run and restrict who can access it.
Can I generate synthetic data from licensed data?
Only if the license allows it. Synthetic data created from licensed records is usually a derivative of them, so the license should say whether you may create it, what it may be used for and whether it survives the end of the term. If the source contains personal data, check that generated records do not reproduce real people's details.
Ready to source data?
Send the domain, modality, volume, format, timeline and permitted use you need. SourceX will match it against partner businesses.
Updated 3 October 2026.