With the growth of artificial intelligence and the rise of 2026, the actual competitive edge will no longer be model architecture, but data quality. The power of Large Language Models (LLMs) depends solely on the datasets that they have been trained on and further refined.

AI startups, enterprise ML teams, and research labs are already on the lookout for trusted data marketplaces to provide reliable AI LLM training data in terms of structured, compliant, and scalable data products.

This article describes the current points of purchase of datasets by AI companies, what to consider in the current AI data marketplace, and why Opendatabay is becoming one of the most popular players in the AI data ecosystem.

https://www.opendatabay.com

How LLM training data landscape is changing in 2026

In the 2026 AI landscape, where the EU AI Act and GDPR hold developers legally accountable for their training data, cutting corners is no longer just a technical misstep; it is a corporate liability. 

While some AI firms resort to scraping the open web or using low-quality synthetic data, this approach invites legal jeopardy from unknown licenses and unresolved IP and rights violations that can surface after deployment. This is why sourcing data from a specialised AI training data marketplaces is the optimal strategy. Unlike internally constructed datasets that are costly and legally fraught, those marketplaces provide fully vetted, rights-cleared corpora where every piece of data comes with a verifiable chain of provenance and legal use. This ensures that your high-quality training data is compliant with international regulations from the ground up, allowing you to accelerate development cycles without the existential risk of a lawsuit or regulatory fine.

How AI and LLM companies acquire data?

There are several sources of data for AI teams.

Own proprietary data

• Product logs, support tickets, documents, CRM data, and internal knowledge bases.
• Usually, the most valuable and defensible source
• Mainly use for internal models training and AI application building

Internal data pipelines & integrations

• ETL from data warehouses (Snowflake, BigQuery, Databricks), application databases, SaaS tools.
• Often combined into “feature stores” or internal training corpora.

Data scraping / web crawling

• Direct scraping or via managed web data platforms, then cleaning/transforming into training sets.
• Increasingly controversial because of copyright, consent, and privacy issues
• Risky as the data could be manipulated or synthetically generated to feed models with malicious or biased information 

Open and public datasets

• Research and community datasets (e.g. The Pile, Common Corpus, EleutherAI projects), Kaggle, Hugging Face Datasets, and academic corpora.
• Great for experimentation, student projects where quality does not matter, and models never deployed to production

Synthetic data generation

• Fully synthetic tabular, text, or image data generated from models or simulators to avoid PII and fill gaps.
• Often built on top of smaller seed datasets or simulated environments.
• Expensive, lengthy process, introduces risk for model collapse

Enterprise data sharing & partnerships

• Direct data‑sharing agreements with other companies, consortia, or sector‑specific alliances.
• Often used in finance, healthcare, and industry collaborations.
• Reddit, Disney, Shutterstock, Publishers like The Guardian, and The Washington Post, which partnered with LLM companies. 

Crowdsourcing and labelling platforms

• Toloka, Scale, Appen, etc. for human‑labelled images, text, audio, and preference data.
• Used heavily for fine‑tuning, RLHF, and evaluation datasets.
• Slow on new data generation, good for a one-time purchase but not suitable for scaling. 

Private data brokers

• Relationship‑driven brokers who source niche datasets (e.g. object images, call recordings, specialised domain corpora).
• Often opaque, manual, and expensive.

Traditional data marketplaces & cloud exchanges

• AWS Data Exchange, Snowflake Marketplace, Datarade and similar B2B data platforms.
• Good for industry, marketing and analytics data; not optimised for LLM licensing and documentation.

AI‑native data marketplaces

• Specialised marketplaces for AI/LLM training and fine‑tuning data (Opendatabay, Scale.AI).
• Focus on AI‑relevant formats and modalities, provenance, and explicit AI training licences.
• Clear on the difference between training, evaluation, fine‑tuning, and commercial deployment licences
• Offer structured listings tailored to model development workflows
  

https://docs.opendatabay.com/

Why Opendatabay Is Positioned for 2026 Growth

Opendatabay is perfectly positioned for 2026 growth since it sits at the intersection of two massive trends: exploding demand for LLM training data and the regulatory push for transparent, licensed, and well‑documented datasets driven by the EU AI Act and similar frameworks. 

While older platforms like AWS Data Exchange and Snowflake Marketplace focus on generic analytics data, and free platforms like Kaggle and Hugging Face struggle with licensing and quality, Opendatabay offers an AI‑native infrastructure, clear training licences, and provenance tracking – that enterprises actually need for production models. 

With its lean, product‑led marketplace that is already connecting many verified data vendors to active AI buyers, Opendatabay is scaling rapidly, and more teams shift from scraping and synthetic data to compliant, marketplace‑sourced training data.