Company profile · 3 min read
Lingo.dev gives localization teams quality-scored, stateful translation infrastructure
Lingo.dev combines LLM translation engines, glossary and brand rules, quality scoring, CI/CD integrations and native-speaker review for production localization.
Cofounder of Lazyweb (a16z Speedrun). Previously grew subscriptions and built native ads at Duolingo. Writes First1000 (90,000+ subscribers).
Published · Updated
Lingo.dev is for product and engineering teams that need translations to behave like production infrastructure: consistent terminology, measurable quality, repeatable locale rules and a human review path when the score is not good enough.
What it does
Lingo.dev lets teams build stateful localization engines around LLMs. An engine can hold glossaries, brand voice, locale-specific rules and model preferences, then run through an API, CLI, CI/CD pipeline, code repository or CMS. Translations receive quality scores, and the team can route low-scoring content to native speakers before it goes live. Lingo.dev homepage Lingo.dev docs
The advantage is visibility in languages the product team cannot read. Instead of choosing one translation model and hoping, a team can define its own criteria, trace a low score to a model, rule or missing term, and use corrections to tune the engine. That is a different product from a one-off “translate this string” API.
The public pricing page lists Sandbox at $0 per month, Production at $99 per month plus usage, and Enterprise as custom. LLM provider cost passes through without markup; Lingo.dev infrastructure is listed at $2 per million tokens and AI review runs at $0.01 each. Production includes five million tokens per day, 30-day retention and repository integrations; Enterprise adds human review, governance, SSO, RBAC and data-residency options. Lingo.dev pricing
The tradeoff is operational discipline. A glossary and scorecard can make quality measurable, but they still need good locale criteria and native review for consequential copy. The company says it is SOC 2 Type II compliant and has processed more than 300 million words; those are company-published claims, not an independent quality audit.
The YC profile identifies Max Prilutskiy as an engineering leader with Typeform experience and Veronica Prilutskaya as a data-science and AI operator. Their own localization experience explains the product wedge.
My editorial take
I would shortlist Lingo.dev when translation drift is slowing releases or damaging a product’s voice across markets. Start with one product surface and a small locale set, define the scorecard and decide which strings require human approval. If the team only needs occasional translation, the production infrastructure will be unnecessary.
Quick facts
| Field | Sourced detail |
|---|---|
| Buyer | Product, engineering and localization teams |
| Product | Stateful LLM translation engines, scoring and human review |
| Pricing | Sandbox $0; Production $99/month + usage; Enterprise custom |
| Integrations | API, CLI, CI/CD, repositories, CMS and webhooks |
| Main fit question | Is localization quality and release consistency the real bottleneck? |
Sources checked
Lingo.dev’s YC profile, homepage, pricing page and platform docs were checked on 2026-09-19.
Cohort context
Lingo.dev is listed in Fall 2024. In our 2026-09-18 directory snapshot, 57 of 94 listed companies in that cohort have YC’s primary industry label B2B (60.6%). This is a current-directory comparison, not an original intake count or a performance ranking. Nine-cohort dataset.
Public website snapshot
Observed 2026-09-19T16:14:40.575Z in raw homepage HTML. This records visible metadata and advertised links, not agent execution or product quality.
| Signal | Homepage observation |
|---|---|
| Product description metadata | Observed |
| Canonical link | Observed |
| H1 or H2 heading | Observed |
| Typed structured data | Not observed in this response |
| Docs/developer link | Observed |
| Pricing link | Observed |
| llms.txt link | Not observed in this response |
| Markdown alternate | Not observed in this response |
Public observations · Collection method. Missing links here do not establish that a capability or file is absent elsewhere.
About the author
Cofounder of Lazyweb (a16z Speedrun). Previously grew subscriptions and built native ads at Duolingo. Writes First1000 (90,000+ subscribers).
I cofound Lazyweb and publish Mudpie. This is an owner-written publication, not an independent testing organization. Research notes distinguish observations, sourced reporting and editorial judgment.
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