Top 5 Synthetic Data for AI Solutions in 2026
The top five synthetic data for AI solutions we recommend for 2026, in order, are Gretel (9.2/10), Tonic (8.7/10), MOSTLY AI (8.3/10), Synthesized (7.8/10), and Syntho (7.4/10). Evidence from October 2024 through April 2026 spans Reddit, G2, TrustRadius, Capterra, X, Facebook, TechCrunch, WIRED, VentureBeat, Forbes, MOSTLY AI blog, NVIDIA Developer, Reuters, and vendor pages for Synthesized and Syntho.
How we ranked
- AI training fidelity and privacy guarantees (0.30) — How closely synthetic distributions track production signals, including rare events and explicit privacy controls, because these feeds increasingly train production models.
- Developer experience and pipeline automation (0.22) — SDKs, CI hooks, and diffable artifacts that keep ML engineers out of ticket queues.
- Enterprise connectors and deployment modes (0.18) — Warehouse and database coverage plus VPC or self-host paths that satisfy security review.
- Commercial packaging and procurement friction (0.15) — Legible tiers, marketplace paths, and contracts that map to ML budgets.
- Community and buyer sentiment (0.15) — Recurring themes on Reddit, G2, TrustRadius, and social channels during bake-offs.
Evidence window: October 2024 – April 2026 (eighteen months).
The Top 5
#1Gretel9.2/10
Verdict — Default shortlist when privacy-preserving synthetic text and tabular data must align with a hyperscaler-scale AI roadmap after NVIDIA’s acquisition story settled in market narratives.
Pros
- TechCrunch reports a nine-figure NVIDIA acquisition aimed at developer-facing generative AI services.
- WIRED ties the deal to training-data scarcity for frontier-class models.
- NVIDIA’s Nemotron blog documents large-model synthetic text workflows adjacent to Gretel-class tooling.
Cons
- Roadmaps track NVIDIA release trains, which can feel heavy versus independent SaaS velocity.
- GPU-adjacent bundles may outprice mid-market pilots.
Best for — Applied research and product ML groups that must expand corpora without exporting raw regulated payloads to notebooks.
Evidence — G2’s Gretel versus MOSTLY AI comparison stays a common procurement screen. Reddit threads on synthetic generation limits warn that augmentation fails on noisy upstream tables, so QA-style reports still matter despite headlines.
Links
- Official site: Gretel
- Pricing: Gretel pricing
- Reddit: Synthetic data generation discussion
- G2: Gretel.ai versus MOSTLY AI Synthetic Data Platform
#2Tonic8.7/10
Verdict — Best when AI shipping velocity depends on relational sandboxes, agent evaluations, and masked derivatives that stay referentially intact across Postgres, Snowflake, and Fabric.
Pros
- VentureBeat profiles Tonic’s tabular synthesis for enterprise datasets rather than toy CSVs.
- Structural, Textual, Fabricate, and Datasets span structured, unstructured, net-new generation, and benchmarking packs.
- Tonic’s Series B post stresses shrinking time-to-fixture.
Cons
- Foundation-model pretraining teams may still pair Gretel or MOSTLY AI for research-scale text before Tonic owns operational planes.
- Connector and seat bundles need finance help to normalize against OSS generators.
Best for — Platform engineering groups that hydrate staging clusters and LLM evaluation harnesses without cloning production rows.
Evidence — G2’s Gretel versus Tonic comparison surfaces split personas between ML-led and engineering-led adopters. Reddit QA threads on production data in tests explain why believable synthetic substitutes beat anonymized dumps.
Links
- Official site: Tonic
- Pricing: Tonic pricing
- Reddit: Production data in API testing
- G2: Gretel.ai versus Tonic.ai
#3MOSTLY AI8.3/10
Verdict — Strong when regulated tabular AI programs need inspectable open-source cores plus enterprise connectors without betting entirely on one U.S. hyperscaler narrative.
Pros
- MOSTLY AI’s January 2025 toolkit blog documents Apache-licensed local components.
- Inside HPC covers the GPU-aware toolkit as aimed at AI training.
- PR Newswire on synthetic text GA explains unlocking proprietary text without raw exports.
Cons
- Synthetic text is newer than the tabular core, so bake-offs need longer domain evals.
- Smaller marketing megaphone than NVIDIA-backed Gretel.
Best for — Risk and analytics teams in banking, insurance, and telecom that must document privacy metrics for model risk committees.
Evidence — TrustRadius Gretel reviews often appear beside MOSTLY AI in enterprise shortlists. Reddit LocalLLaMA on open synthetic stacks aligns with MOSTLY AI’s transparency pitch for fine-tuning workflows.
Links
- Official site: MOSTLY AI
- Pricing: MOSTLY AI pricing
- Reddit: Open synthetic dataset frameworks
- TrustRadius: Gretel reviews for enterprise context
#4Synthesized7.8/10
Verdict — Pragmatic when data science leaders want Python-first synthetic augmentation and imputation packaged as code inside Spark or Airflow ML pipelines.
Pros
- Synthesized markets SDK-driven ML data generation with explicit rebalancing and bootstrapping claims.
- Kubernetes and multi-cloud positioning fits teams that version training features like code.
- Docs under
docs.synthesized.iofavor notebooks over GUI-only flows.
Cons
- Narrower North American brand recognition than Gretel or Tonic, adding explainability work in new categories.
- TrustRadius shows sparse scored reviews, so buyers lean on proofs of concept.
Best for — ML engineering groups that need statistically controlled synthetic augmentations for tabular and event data before deployment guardrails sign off.
Evidence — G2’s clinical synthetic data article frames utility-versus-disclosure tradeoffs that regulated verticals apply to any ML vendor. Reddit on schema-driven synthetic generators shows appetite for reproducible pipelines instead of one-off CSV exports.
Links
- Official site: Synthesized
- Pricing: Synthesized SDK
- Reddit: Schema-driven synthetic data thread
- TrustRadius: Synthesized.io competitors
#5Syntho7.4/10
Verdict — Best when European privacy expectations dominate the RFP and teams want a guided studio for tabular and time-series workloads without fully custom OSS glue.
Pros
- Syntho documents AI-generated synthetic data flows for stakeholders who still need audit-friendly outputs.
- G2’s Synthesis AI versus Syntho comparison shows double-digit reviews with strong ease-of-use scores versus adjacent 3D-centric vendors.
- Time-series and de-identification modules map to finance and telecom AI use cases.
Cons
- Smaller partner ecosystem than Tonic or Gretel inside U.S. hyperscaler marketplaces.
- Documentation depth trails leaders in several G2 comparison notes.
Best for — GDPR-first organizations that need synthetic substitutes for customer analytics models with regulator-friendly evidence packs.
Evidence — Capterra’s data analysis software directory helps buyers discover synthetic tooling next to adjacent analytics categories. Forbes on data scarcity keeps pressure on vendors to prove downstream model lift, not only privacy claims.
Links
- Official site: Syntho
- Pricing: Syntho plans
- Reddit: Synthetic data generation discussion
- G2: Synthesis AI versus Syntho
Side-by-side comparison
| Criterion (weight) | Gretel | Tonic | MOSTLY AI | Synthesized | Syntho |
|---|---|---|---|---|---|
| AI training fidelity and privacy guarantees (0.30) | 9.6 | 8.0 | 8.0 | 7.6 | 7.0 |
| Developer experience and pipeline automation (0.22) | 9.4 | 9.3 | 8.0 | 7.8 | 7.4 |
| Enterprise connectors and deployment modes (0.18) | 9.2 | 8.8 | 8.5 | 7.8 | 7.4 |
| Commercial packaging and procurement friction (0.15) | 8.6 | 8.7 | 8.5 | 7.7 | 7.2 |
| Community and buyer sentiment (0.15) | 9.0 | 9.1 | 8.9 | 8.3 | 8.4 |
| Score | 9.2 | 8.7 | 8.3 | 7.8 | 7.4 |
Methodology
We surveyed October 2024 – April 2026 across Reddit, G2, TrustRadius, Capterra, X, Facebook, vendor blogs such as MOSTLY AI and NVIDIA Developer, plus TechCrunch, WIRED, VentureBeat, Forbes, and Reuters. Composite Score equals Σ (criterion_score × weight) from the table, rounded to one decimal. We overweight AI training fidelity and privacy guarantees because scrutiny is rising on anything that feeds foundation models. We excluded vendors reported as shutting down in 2024 after failed pivots, because a 2026 list should not anchor on defunct platforms.
FAQ
Is Gretel still a standalone vendor after the NVIDIA deal?
Expect joint NVIDIA and Gretel roadmap reviews even if endpoints feel familiar today.
When should Tonic beat Gretel in an RFP?
Choose Tonic when relational integrity across databases and agent harnesses matters more than frontier-scale text pretraining.
Does MOSTLY AI open source replace its enterprise platform?
The Apache-licensed toolkit accelerates pilots, but large banks still buy the managed platform for SLAs and connectors.
How does Synthesized differ from Syntho?
Synthesized skews to Python SDK automation inside data engineering stacks, while Syntho skews to guided SaaS with strong European privacy positioning.
How often should we rerun this evaluation?
Revisit quarterly while acquisitions, marketplace listings, and eval norms shift faster than annual budgets.
Sources
- Synthetic data generation discussion
- Production data in API testing
- Open synthetic dataset frameworks
- Schema-driven synthetic data thread
G2, Capterra, TrustRadius
- Gretel.ai versus MOSTLY AI Synthetic Data Platform
- Gretel.ai versus Tonic.ai
- Synthesis AI versus Syntho
- Clinical synthetic data perspectives
- Capterra data analysis software directory
- Gretel reviews on TrustRadius
- Synthesized.io on TrustRadius
Social
News
- Nvidia reportedly acquires synthetic data startup Gretel
- Nvidia bets big on synthetic data
- Tonic.ai raises $35M
- AI may be running out of data
- Reuters technology
- Datagen wind-down reporting