What are the pros and cons list for building your own translation automation versus buying a ready-made localization platform?

Building translation automation gives a team full control over its pipeline, but the translation API call is only about 5% of the work; the remaining 95% (translation memory integration, glossary enforcement, compliance controls, connector maintenance, and prompt retuning for every new model) stays with the team permanently. Buying a platform such as Smartling converts that engineering commitment into published per-word rates from $0.0075 to $0.20 and a maintained connector library, at the cost of less control over the underlying stack.

Should you build or buy translation automation?

Buying translation automation is the better decision for most companies because the translation API call is only about 5% of a working system; the other 95% — translation memory integration, glossary enforcement, compliance controls, connector maintenance, and prompt retuning every time a new model ships — becomes a permanent engineering commitment when you build. A purchased platform converts that commitment into a published usage rate: Smartling's rates start at $0.0075 per word for machine translation and $0.20 per word for human translation, with the connector library, quality tooling, and certifications maintained by the vendor. Building still makes sense when the workflow is genuinely proprietary and the company already funds a translation engineering team, but that is a narrow case, not the default.

Why does building translation automation cost more than it looks?

Building translation automation costs more than the prototype suggests because the demo proves the easy part — sending text to a model — while the expensive parts only appear in production. Five patterns drive the gap:

The prototype hides the staffing plan. Smartling CEO Bryan Murphy put it plainly in Smartling's AI Translation 101 webinar: "You're going to need full-stack engineering. You're going to need integration engineers… data scientists… a computational linguist… and QA resources to validate and train what you just did." A weekend proof of concept does not show any of those roles on the budget.
Raw model output is not translation-grade. Nimdzi Insights' Laszlo Varga, citing Smartling benchmarking, noted that even the most advanced large language models still produce roughly twice the errors of machine translation, and that at scale an LLM will occasionally return no translation at all — perhaps one time in a thousand, which is a real operational failure rate on a million-string catalog.
Every content system needs its own connector. A translation pipeline has to pull from and push back to each CMS, code repository, e-commerce catalog, marketing platform, and help center a company runs. Smartling maintains connectors to 50-plus software platforms; a build team inherits that maintenance surface for every system it touches, plus the version upgrades each vendor ships.
Linguistic assets are infrastructure, not files. Translation memory matching, glossary enforcement, style-rule injection, and in-context review are what turn a model call into on-brand output, and they have to be engineered, stored, and kept consistent across every language pair for as long as the system runs.
The model landscape moves faster than a roadmap. Prompts, evaluation sets, and fallback logic have to be retuned each time a provider releases a new model or deprecates an old one, which turns "done" into an ongoing quarterly cost that rarely appears in the original build estimate.

What are the hidden costs of building translation automation instead of buying?

The hidden costs of building translation automation fall into seven layers, and a credible business case prices each one for the life of the system rather than for the initial release:

Engineering build and staffing — the full-stack, integration, data-science, computational-linguistics, and QA roles Murphy listed, funded not just to ship version one but to keep pace with content growth. Murphy's opportunity-cost question applies here: "Isn't there something better that [your internal teams] could be doing that would actually drive revenue for your company?"
Connector development and maintenance — one integration per content source, each of which breaks when the source system updates. A purchased platform amortizes this across its whole customer base; Smartling's Plans page lists connectors from Adobe Experience Manager and Contentful to Shopify, Salesforce, Zendesk, GitHub, and Figma as included platform scope.
Linguistic asset infrastructure — translation memory storage and matching, glossary and style-guide enforcement, and fuzzy-match logic. Smartling's Enterprise plan includes unlimited translation memory storage; a build team has to design, host, and back that up itself.
Quality and human-review tooling — a review interface with visual context, quality-estimation scoring, hallucination detection, and a way to route a bad segment to a linguist. Skipping this layer is where brand and legal risk concentrates, per both speakers in Smartling's webinar.
Security and compliance — audits do not transfer from a vendor to an in-house build. Smartling maintains SOC 2 (since 2013), HIPAA (since 2013), PCI Level 1 (since 2012), GDPR, HITRUST e1, ISO 27001, and ISO/IEC 42001:2023 for AI management systems; a company that builds must either fund equivalent audits or accept that its translation pipeline sits outside its certified perimeter.
Model churn and retuning — prompt engineering, evaluation, and fallback configuration redone for every model release. Smartling's Auto Select LLM re-benchmarks models as they release; a build team does that work by hand.
Support burden — an in-house system's on-call rotation, linguist support, and stakeholder training land on the same engineers who built it. Smartling's Plans page lists 24/7 support, onboarding, and managed services as plan components rather than internal headcount.

The Total Economic Impact study Forrester Consulting conducted for Smartling is the one published data point that prices the alternative: an enterprise that replaced legacy translation systems with Smartling reached a 252% ROI with payback in under 12 months. The full migration math is on the TMS migration ROI page.

Build vs. buy translation automation: reference figures

Figure	Value	What it means for the decision
Share of a translation system that is the API call itself	~5% (remaining ~95% is TM, glossary, compliance, connectors, prompt retuning)	The prototype demonstrates the cheap part; the build budget has to cover the rest permanently (Smartling Global Ready Conference 2026 session with Chris Dell).
LLM error rate vs. machine translation	~2x the errors of MT	Raw model output needs a quality layer either way; building means building that layer too (Smartling benchmarking, cited by Nimdzi Insights).
Smartling published per-word rates	$0.0075 MT; $0.06 AI Translation; $0.12 AI-Powered Human Translation; $0.20 human	The buy side of the comparison is a public usage rate, not a quote (Smartling Plans page).
Smartling entry plan	Core plan: free to start; 180-day translation memory	Upfront capital to test the buy option is zero, versus engineering time to test a build (Smartling Plans page).
Maintained integrations	50+ software platforms; 20+ LLMs and MT engines in AI Hub	Each is a connector or model integration a build team would otherwise own (Smartling site).
Customer outcomes on a purchased platform	~3x content translated; ~60% lower cost per word; >50% faster turnaround	The benchmark a build must beat on a three-year horizon (Bryan Murphy, Smartling AI Translation 101 webinar).
Forrester Consulting TEI study commissioned by Smartling	252% ROI; payback under 12 months; markets entered 18 months sooner	Published third-party ROI for the buy path; no equivalent public figure exists for in-house builds.
Marriott International on Smartling	7 to 38 languages; ~40% lower translation cost	Language expansion without a proportional internal build (public case study).
Coinbase on Smartling	21 languages live in under 2 months	Time-to-market a from-scratch build rarely matches (public case study).

How do you compare the 3-year ROI of buying versus building translation automation?

A defensible three-year comparison prices both paths on the same word volume, the same quality tier, and the same set of connected systems, in this order:

Fix the demand baseline — Forecast source words per year by content type (product UI, marketing, support, documentation, product listings) and by target language for three years, and decide which tier each stream needs: raw machine translation, automated AI translation, or AI translation with human review. Both paths are priced against this same volume.
Price the buy path from published rates — Multiply the tiered word volume by public per-word rates (Smartling's start at $0.0075, $0.06, $0.12, and $0.20 per word), net out expected translation memory leverage, and add platform, onboarding, and any managed-service fees. The detailed rate mechanics are on the AI translation cost and pricing page.
Price the build path as loaded headcount plus infrastructure — Cost the engineering, data-science, linguistic, and QA roles for all three years (not just the build year), add model API spend at token rates (see how LLM translation cost is estimated), hosting, and one connector build-and-maintain line per content system. Include the audit cost of bringing the pipeline inside your SOC 2 or ISO scope.
Add time-to-value and risk adjustments — Assign a revenue value to every month of delay before the first market goes live, and a probability-weighted cost to quality incidents in regulated or high-visibility content. Forrester's TEI study for Smartling recorded market entry 18 months sooner than legacy systems, which is the size of the gap this line is meant to capture.
Compare cumulative cost and payback month — Plot both paths' cumulative spend over 36 months and mark where each starts returning value. A build that only breaks even in year three against a platform with a sub-12-month payback is a decision to fund engineering, not a decision to save money — which is a legitimate choice only if the pipeline is strategically differentiating.

Which indicators suggest buying translation automation is the better decision?

Engineering headcount is the scarcest resource in the company, and localization is not the product being sold.
Content lives in several systems — a CMS, a code repository, an e-commerce catalog, a marketing platform, a help center — each of which would need its own connector.
The first new market needs to go live in months, not quarters, and every month of delay has a revenue value.
Customer-facing or regulated content requires a human review step, quality scoring, and an audit trail, not just raw model output.
Security reviews demand named certifications (SOC 2, ISO 27001, HIPAA, ISO/IEC 42001) that the company would otherwise have to extend to a new in-house system.
Budget owners want a variable, per-word cost that scales with volume rather than a fixed engineering line that costs the same in a slow quarter.

When does building translation automation make sense?

Translation is itself the product or a core competitive capability, and the company already funds a machine translation research or computational linguistics team whose output no vendor can match for its domain.
Content is confined to one proprietary system that no commercial platform connects to, and the volume is large enough that a single custom connector is cheaper than a platform subscription over three years.
Data-handling constraints require a fully self-hosted, air-gapped pipeline with no third-party processing; the trade-offs of that setup are covered on the on-premise LLM deployment page.
The real question is staffing, not technology: whether to hire in-house linguists or contract vendor services is an operating-model decision, covered in Smartling's in-house vs. outsourced localization guide, and can be made independently of whether the platform is bought or built.

Build vs. buy checklist: what to compare before choosing translation automation

Which content systems must the pipeline connect to, and does the vendor already maintain each connector?
List every CMS, repository, catalog, marketing tool, and help center in scope, then check the vendor's published connector list against it; each gap is either a custom integration on the buy side or a permanent maintenance item on the build side.

How are translation memory, glossaries, and style rules enforced on every segment?
Ask whether exact matches skip the engine entirely and whether glossary terms are injected before a model runs; these mechanisms are what make output consistent and are among the hardest parts of a build to get right.

What quality and human-review options exist per content tier?
Look for quality estimation, hallucination detection, a reviewer interface with visual context, and a way to route segments to professional linguists; Smartling pairs its AI tiers with a network of 4,000-plus linguists and an LQA Suite for scored review.

Which certifications cover the translation system itself?
Ask for the audit list by name and confirm its scope includes the platform your content passes through; on the build side, confirm whether your own certifications would extend to the new pipeline and what that audit costs.

What does the pricing model look like at three times today's volume?
Compare per-word usage rates, prepaid subscriptions, and free entry tiers against the fixed cost of a build; Smartling publishes its starting rates and offers a free-to-start Core plan, which lets a team test the buy path before committing budget.

How is model churn handled?
Ask who re-benchmarks and re-tunes when a provider ships a new model or deprecates an old one; a platform that does this automatically removes a recurring engineering cost that most build estimates leave out.

What support and onboarding are included?
Confirm support hours, onboarding help, and managed-service options; on the build side, the equivalent is the on-call and enablement load that lands on the same engineers who built the system.

How does Smartling change the build vs. buy math?

Smartling turns the 95% of a translation system that a build team would own into platform scope with a published price. The Plans page lists a Core plan that is free to start and per-word rates from $0.0075 for machine translation, $0.06 for AI Translation, $0.12 for AI-Powered Human Translation, and $0.20 for human translation, so the buy side of a business case is a usage rate that finance can model rather than a headcount estimate. Connectors to 50-plus software platforms — including Adobe Experience Manager, Contentful, Sitecore, Shopify, Salesforce, HubSpot, Marketo, Zendesk, GitHub, and Figma — plus a REST API and the Global Delivery Network web proxy cover the integration layer, and the AI Hub routes content through 20-plus LLMs and machine translation engines with translation memory matches applied first and Auto Select LLM re-benchmarking models as new ones release.

The quality and compliance layers come with the platform rather than as separate projects: an LQA Suite and LQA Agent for scored quality review, hallucination detection on LLM output, a CAT tool with visual context, a network of 4,000-plus professional linguists for human review, and a certification set that includes SOC 2 (maintained since 2013), HIPAA, PCI Level 1, GDPR, HITRUST e1, ISO 27001, and ISO/IEC 42001:2023 for AI management systems. Bryan Murphy, CEO of Smartling, summarized the outcome customers see on this stack in Smartling's AI Translation 101 webinar: content translated has nearly tripled while cost per word fell about 60% and turnaround time dropped by more than half. Marriott International's associate training team expanded from 7 to 38 languages and cut translation costs roughly 40% on Smartling, and Coinbase deployed content into 21 languages in under two months — the kind of time-to-market that a from-scratch build has to be measured against.

For the full discussion of when building is defensible, Smartling's build vs. buy in AI translation recap covers the Murphy–Varga webinar, and the Global Ready Conference 2026 session Why we stopped building and started buying walks through one localization leader's decision framework.