MigrationJul 13, 20265 min read
Migrating from Scale AI to Synthsis Labs
A practical guide to migrating from Scale AI to Synthsis Labs: a more cost-effective, tailored data partner for mid-sized labs, plus a step-by-step switch plan.
Scale AI is built for large, enterprise-scale programs. That is a strength if you are a frontier lab with nine-figure data budgets, but it is often a poor fit for the mid-sized labs and startups that make up most of the market. If you are paying enterprise prices for a standardized product when what you really need is a tailored, cost-effective partner, it may be time to switch.
This is a practical guide to moving from Scale AI to Synthsis Labs without disrupting your training and evaluation pipelines. It covers why teams switch, what maps to what, and a step-by-step migration plan.
Why teams migrate off Scale AI
Three reasons come up most often for smaller and mid-sized teams.
Cost. Scale is priced and structured for large programs, with the minimums and overhead that come with an enterprise platform. Mid-sized labs often end up paying for scale they do not use. A leaner partner can deliver the same quality of human data at a price that actually fits an iterative research budget.
Tailored service. A large platform runs a standardized product. Smaller teams usually want the opposite: dataset design shaped around their exact objective, direct collaboration with the people doing the work, and the flexibility to change scope mid-project. That is hard to get as a small account on a big platform.
Speed and attention. No heavyweight procurement, no long enterprise sales cycle, and no waiting in a queue behind much larger accounts. A focused partner can start a pilot quickly and give your program real attention.
We covered this trade-off in more depth in Scale AI alternatives for mid-sized labs. Migration is where that decision becomes concrete.
What Synthsis Labs replaces
Synthsis Labs operates in the same category: producing high-quality human data for teams training and evaluating models. If you use Scale for any of the following, there is a direct equivalent.
| What you use Scale AI for | The Synthsis Labs equivalent |
|---|---|
| Data labeling and annotation | Purpose-built human data produced to your spec |
| RLHF and preference data | Expert reasoning traces, evaluations, and preference data |
| Model evaluations | Evaluation-style workflows and domain-specific eval sets |
| Domain expert sourcing | Expert data from working professionals in medicine, engineering, law, and finance |
| Robotics and physical data | Physical and embodied demonstrations capturing vision, depth, force, and motion |
The difference is the price point and the engagement model, not the category of work.
A step-by-step migration plan
Migrating a data vendor is mostly about protecting model quality during the switch. The goal is no regression in your training or evaluation metrics. Here is the sequence we recommend.
- Inventory what Scale delivers today. List every workstream: label types, RLHF workflows, eval benchmarks, data schemas, volumes, turnaround times, and acceptance criteria. This becomes your migration checklist.
- Set your budget and scope target. Decide what you actually need produced and what you want to spend. This is where most teams find they were over-paying for capacity they did not use.
- Run a scoped pilot. Pick one representative workstream, for example a domain evaluation set or a single expert-data batch, and run it with Synthsis. Keep it small enough to move fast and real enough to be a fair test.
- Define the quality bar up front. Share your rubrics, gold sets, and inter-annotator agreement targets. A good partner hits your existing bar rather than defining a new one.
- Shadow-run against your Scale baseline. Produce the same task through both vendors for a short window and compare quality and cost side by side. Confirm Synthsis matches or beats your current output before you move volume.
- Port your specs and guidelines. Migrate your annotation guidelines, schemas, and taskonomy. Most of this transfers directly. Settle edge cases during the pilot so they are resolved before scale-up.
- Cut over incrementally. Move one workstream at a time, keep an evaluation holdout, and watch your model metrics after each move. Incremental cutover means any issue shows up on a small surface, not across your whole pipeline.
- Offboard cleanly from Scale. Export your data, confirm ownership and licensing terms, and wind down the contract. Keep copies of your guidelines and gold sets, since they are reusable with any vendor.
What to expect during the switch
- Timeline. A single-workstream pilot can run in the time it takes to agree on a spec and produce a first batch. Full migration is usually staged across workstreams rather than done all at once.
- Cost. Migration is a good moment to right-size. Many teams find a tailored engagement is meaningfully cheaper than a large platform minimum for the same quality.
- Quality. With shadow-running and a clear rubric, you should see no regression. Tighter collaboration on a smaller program often improves edge-case handling.
- Ownership. Confirm data ownership and licensing in writing with any vendor, including on the way out of Scale.
Is migrating right for you?
Moving is likely worth it if your team is:
- paying enterprise prices for work that does not need an enterprise platform,
- too advanced for generic labeling but too small to be a priority account at a large vendor,
- or looking for a partner that shapes the work around your exact objective rather than a fixed product.
If Scale is serving you well and the price fits, there is no need to move for its own sake. But if any of the above sounds familiar, a scoped pilot is a low-risk way to find out.
Get started
The lowest-risk first step is a single-workstream pilot with a clear quality bar and a fixed budget. Talk to our team about the workstream you would move first, and we will scope a pilot against your current baseline.