Bria vs Gretel vs Mostly AI Synthetic Data Platforms (2026): The Honest Comparison

These platforms address different parts of the synthetic data market. Understanding the format split is the first decision.

**Bria** (https://bria.ai/) specializes in visual synthetic data — images and increasingly video — built on legally-cleared source data (a key differentiator versus general-purpose image models trained on web-scraped data of uncertain provenance). Bria's product is aimed at teams fine-tuning computer vision models, training image generation models with rights-cleared training sets, or generating product imagery for e-commerce at scale. The differentiator is the licensing posture: every output is commercially usable without exposure to the copyright lawsuits that have hit general-purpose image model outputs in 2024-2026.

**Gretel AI** (https://gretel.ai/) covers text and tabular synthetic data generation with privacy engineering as a first-class feature. Its products include Gretel GPT (LLM-trained synthetic text generation), Gretel Transform (tabular synthetic data with privacy controls), and a privacy risk reporting layer that quantifies re-identification risk on every generated dataset. The fine-tuning-relevant product is the text synthesis: you provide a small real dataset, Gretel generates 10-100x more synthetic examples in the same distribution, and you fine-tune downstream on the combined dataset.

**Mostly AI** (https://mostly.ai/) is the deepest on tabular synthetic data with the most rigorous differential-privacy story. Its core product synthesizes tabular and relational datasets with mathematically-provable privacy guarantees (differential privacy with explicit epsilon control). This is the right pick for highly-regulated industries — healthcare, finance, insurance — where the legal requirements around training data are strict and the math of differential privacy is the only acceptable answer. The Mostly AI QA report quantifies fidelity (how closely synthetic mirrors real) versus privacy (epsilon and re-identification risk) trade-offs.

Pricing structures differ across the three platforms because the underlying generation cost differs.

**Bria** prices per image generated, with rates from $0.30 to $3.00 per image depending on model and resolution. For a fine-tuning use case requiring 5,000 synthetic product images at the mid-tier rate ($1.50/image), the total is approximately $7,500. This is expensive but reflects the cost of running diffusion models on GPU infrastructure plus the licensing premium of clean source data.

**Gretel AI** prices text synthesis at approximately $1.20 per 1K generated examples and tabular synthesis at approximately $0.40 per 1K rows. For a typical LLM fine-tuning use case (generating 10,000 synthetic prompt-response pairs to augment a 1,000-example real dataset), cost is approximately $12. For a 1M-row tabular generation job (privacy-preserved customer behavior data), cost is approximately $400. Both have a free tier of 100K records/month, so small workloads are free.

**Mostly AI** prices tabular synthesis at approximately $0.80 per 1K rows with a sliding scale at volume (high-volume customers can negotiate down to $0.20-0.40/1K rows). The free tier matches Gretel's at 100K records/month. The premium versus Gretel reflects the more rigorous differential-privacy infrastructure (computing privacy guarantees has real compute cost).

**The total cost reality**: synthetic data is cheap per unit but adds up at scale. A typical fine-tuning workflow that uses 10-50K synthetic examples per training run, run 5-10 times over a quarter, costs $200-2,000 on Gretel for text — small compared to the GPU cost of the fine-tuning itself. Image synthesis on Bria is the main exception where cost can dominate.

If your synthetic data is generated from real personal data (customer behavior, healthcare records, financial transactions), the privacy guarantee on the synthesis is often the binding requirement.

**Bria** sidesteps this entirely — source data is licensed clean, so PII concerns do not apply. This is the right answer for image use cases where the relevant question is copyright clean, not personal-data clean.

**Gretel** offers differential privacy with tunable epsilon, k-anonymity controls on output rows, and a privacy risk report that quantifies re-identification likelihood for each generated dataset. Differential privacy with epsilon < 1 is generally considered strong; Gretel lets you set epsilon up front and the synthesis algorithm enforces it. The privacy risk report includes both DP-based mathematical guarantees and empirical re-identification testing (attempting to match synthetic rows back to source rows using auxiliary information).

**Mostly AI** has the deepest DP story — its synthesis engine is built around differential privacy as a first-class constraint, not a tunable knob bolted on. The QA reports include both fidelity metrics (statistical similarity between synthetic and real distributions) and privacy metrics (DP epsilon, k-anonymity, identifying-info leakage tests). For HIPAA, GDPR, and financial-services use cases where you need to produce formal documentation of the privacy properties of your training data, Mostly AI's reports are the most defensible.

**The honest summary**: if you are training on data with PII exposure and you need a defensible privacy story for compliance, Mostly AI is the strongest. Gretel is a strong second and cheaper. Bria is irrelevant for this question — its model is licensing-based, not DP-based.

How the synthetic data plugs into your fine-tuning pipeline matters more than the per-example price for most teams.

**Bria** exports images in PNG, JPEG, and WebP formats with optional metadata files (captions, tags) that map directly to the input formats expected by image model fine-tuning frameworks. Direct integrations exist for SDXL, Flux, and other diffusion model fine-tuning pipelines via Replicate, Hugging Face, and Bria's own fine-tune offering.

**Gretel** exports synthetic text in jsonl format compatible with OpenAI, Anthropic, Together, and Fireworks fine-tuning. The chat-format jsonl drops directly into any of those platforms' training job submission. For tabular data, exports go to CSV, Parquet, or direct database load.

**Mostly AI** is the deepest on tabular integrations: CSV, Parquet, SQL dumps, and direct database write to common warehouses (Snowflake, BigQuery, Databricks). For tabular ML training, the data lands in the right place automatically — no glue ETL needed.

**The decision implication**: pick the platform whose output format matches your downstream training workflow. The cost of glue code to convert between formats is small for one-off jobs but compounds across many runs.

Mapping platforms to common use cases.

**Computer vision fine-tuning with rights-cleared training data** → Bria. The licensing posture is the differentiator and no other platform matches it.

**LLM fine-tuning where real data is too small** → Gretel. Synthesizing 10-100x more text examples in the same distribution is the canonical use case and Gretel's price/quality is the best in this slot.

**Tabular ML training with HIPAA / GDPR / financial-services compliance** → Mostly AI. Differential-privacy guarantees and formal QA reports are the strongest in the market.

**Tabular ML training without strict regulatory framework** → Gretel. Cheaper than Mostly AI, sufficient privacy controls for most non-regulated workloads.

**Augmenting eval sets with edge cases** → Gretel (text) or Bria (images). Synthesis lets you generate the rare cases your production traffic has not produced yet.

**Multimodal use cases combining text and images** → Use Gretel for text + Bria for images and join downstream. None of the three is end-to-end multimodal in 2026.

Three failure modes show up repeatedly in synthetic data workflows.

**Pitfall 1: Treating synthetic data as a substitute for real data when there is none.** Synthetic data generation needs a real seed dataset — the synthesizer learns the distribution of the seed and generates more samples from that distribution. If your seed is 50 examples, the synthesis quality is bounded by what 50 examples can teach. Synthetic data is a multiplier on real data, not a replacement for it.

**Pitfall 2: Ignoring distribution drift between synthetic and real.** Synthetic data tends to live in the dense regions of the real data distribution and underrepresent the tails. Models fine-tuned on heavily-synthetic datasets often perform well on common cases and worse on rare edge cases. The fix: weight synthetic examples lower than real examples in the training loss, or use a mix ratio (e.g., 1 real : 3 synthetic) rather than synthetic-only.

**Pitfall 3: Not validating privacy claims empirically.** Differential-privacy math is mathematically sound but the privacy of a synthesized dataset depends on the entire pipeline (source data, synthesis, post-processing, downstream use). Always run empirical re-identification testing on a held-out subset before deploying synthetic data based on real-PII source. Mostly AI's QA reports and Gretel's privacy risk reports include this — read them.

Synthetic data is not free, and the ROI depends on the alternative cost of real data.

**For text fine-tuning**: synthetic examples at $1.20/1K are dramatically cheaper than human-labeled examples ($1-10 per example depending on task complexity). If a human-labeled example costs $3 and a synthetic one costs $0.001, the synthetic platform pays back at 3,000:1 unit economics — synthetic wins immediately unless the quality gap is large.

**For tabular**: synthetic data unblocks use cases where regulatory or contract restrictions prohibit using real data at all (sharing data with vendors, training models that will be open-sourced, etc.). The economics here are not synthetic-vs-real cost but synthetic-vs-impossible.

**For images**: Bria's $1.50/image is more expensive than free-use scraped data but cheaper than commissioning new photography ($50-500/image). For e-commerce product imagery at scale, Bria can be 100x cheaper than the photography alternative.

**Use our synthetic data cost per 1K examples calculator** to project your specific spend across all three platforms at your volume.