Misata vs Syntho
Prices and facts verified 24 September 2026
Syntho is a funded enterprise platform built around privacy-preserving synthesis from real data. You connect it to your existing databases, it learns statistical patterns, and it produces synthetic copies that preserve those patterns. It is a serious product aimed at banks, insurers, and large healthcare systems that need a vendor relationship, compliance artifacts, and a support contract. Misata solves a different problem: you describe the dataset you want and a deterministic math engine builds it from scratch, with no real data involved at any point, exact declared outcomes, and an integrity proof attached. If you have production data and need a managed enterprise platform to generate privacy-safe copies of it, Syntho is worth evaluating. If you need relational data from a description or specification, free to start and with no real data involved, Misata Studio is the tool, and the open-source library behind it runs entirely on your machine.
| Capability | Misata | Syntho |
|---|---|---|
| Requires real data to start | No. Generates from a specification. | Yes. Connects to your existing databases. |
| Pricing model (checked 21 Sep 2026) | Free to start, then credits by volume | Feature-based Basic, Standard and Ultimate tiers by quote, with no consumption charges |
| Where your data goes | Studio never needs real data, so there is none to send. The open-source library runs entirely on your machine. | A platform that connects to your databases and learns from the data in them. |
| Open source | Yes. MIT licensed. | No. Proprietary. |
| Exact declared aggregates | Yes. Closed-form math, to the cent. | Approximate. Learned from training data. |
| MCP server for AI agents | Yes. Built in. | No. |
| Free to use | Free to start with 20 credits a month (two complete builds); the open-source library has no limits. | No. Paid tiers by quote. |
| Privacy-safe copy of existing data | Use Misata mimic mode for single-table. For full production replication, SDV goes further. | Yes, its core strength. |
| Enterprise compliance (SSO, RBAC, audit trails) | No. It is a library, not a platform. | Yes. |
| Vendor support contract | No. Direct access to the developer. | Yes. |
Why these tools target different situations
Syntho's product requires connecting to a production database and using it as training data. The platform learns the statistical relationships in your real data and produces synthetic rows that resemble them. This is the right approach for a regulated enterprise with production systems, a compliance team that needs a vendor on record, and a requirement for privacy-safe copies of data that already exists. Misata starts with a blank slate. You tell it what the dataset should look like. The engine builds it from that specification, not from any real records. Foreign keys resolve because the engine wires them in the correct order, not because it learned them from production. Aggregates hit declared targets because the math enforces them, not because a trained model approximated them. The two tools serve different moments in a company's lifecycle and different kinds of problems.
Pricing and ownership
Syntho prices by feature tier rather than by usage: its Basic, Standard and Ultimate plans differ in how many database connections and connectors they include, and it states there are no per-generation or consumption charges. Plans are quoted rather than listed, so the price is not public. Syntho also announced on 9 June 2026 that it acquired the MOSTLY AI brand, which now trades as MOSTLY AI powered by Syntho. Misata Studio is the other shape: free to start, credits by volume, and no real data to connect.
The exact outcomes problem
When you tell a trained model that monthly revenue should grow from $80k to $200k, it does not understand that as a constraint. It generates data that looks like whatever it learned from the training distribution. If your training data had flat revenue, the synthetic output will have approximately flat revenue. Misata treats your declaration as a hard constraint and solves it in closed form, which is the approach we published in a research paper because it is formally different from approximation. If you need to build a pipeline test where you know the exact answer in advance, or a demo where the chart hits a specific number you promised a client, a trained model cannot give you that guarantee. Misata can.
Where Syntho is the better choice
If your team sits inside a regulated enterprise that requires ISO 27001 evidence, GDPR data processing agreements, a named vendor, and quantifiable re-identification risk scores on synthetic output, Syntho provides all of that. Misata does not. It is a library and a studio, not a compliance product. For teams that need a privacy-safe statistical copy of an existing production dataset, and have the data residency clearance to move that data through a cloud platform, Syntho is a serious option. We are honest about this because credibility is more useful than persuasion.
Frequently asked
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