Agency vs Automation: Cost, Speed & ROI Compared

Understanding Agency Pricing Models

Per-Word Costs and Project-Based Fees

When assessing the cost of localisation through traditional agencies, it’s essential to understand their core pricing structures. Typically, agencies charge clients either per word or per project. The per-word model is incredibly common and varies depending on language pair, content complexity, and turnaround time. Prices can range from £0.10 to £0.30 per word, depending on the expertise required. For brands scaling across multiple markets, this structure can lead to significant expense if large volumes of text are involved.

On the other hand, project-based fees are calculated depending on scope. This may include translation, editing, proofreading (TEP), content review cycles, and localisation of rich media. While this model provides a fixed overall cost, it lacks flexibility. Any changes to content or scope often incur additional charges. These hidden fees can disrupt budgets, especially when agile marketing timelines demand content updates after initial submission.

Beyond word count and project size, agencies also factor in administrative overheads, quality assurance stages, and human resource allocation. These components, while increasing reliability, also result in a slow and often expensive delivery process. The cost predictability clients seek can sometimes become elusive when last-minute revisions or additional language pairs are introduced.

Exploring Automated Localisation Models

Fixed Setup Costs and Scalability

Automated localisation solutions take a radically different approach. Instead of billing per word or project, these platforms often charge a fixed setup fee in combination with subscription-based pricing. This setup covers integration with a client’s content management system (CMS), machine translation engine calibration, and linguistic asset creation (e.g., style guides, glossaries).

Once in place, automated localisation scales effortlessly. Each new page or product added to your site is automatically translated based on predefined workflows. This eliminates the need to manually submit content for translation, review proofs, or manage back-and-forth emails with project managers. Predictable monthly fees make it easier to forecast localisation budgets—even during campaign spikes or site overhauls.

“Automation eliminates the human bottleneck, enabling true velocity at scale—perfect for businesses aiming to expand rapidly.”

Cost Comparison: Per-Word vs Fixed Setup

To accurately compare agency vs automation costs, we must evaluate beyond surface pricing. While agencies may start at £0.12 per word, translating 100,000 words soon balloons the cost to £12,000. Automation platforms, by contrast, may charge an initial implementation fee of £3,000 with a monthly operational fee of £500. Over 12 months, the total cost reaches £9,000—even with ongoing updates and added languages.

Moreover, agencies frequently add markups for services like formatting, DTP (desktop publishing), or urgent delivery. In contrast, automation tools bundle these features as part of an integrated offering. When projects increase in frequency or volume, the automation model remains constant, while agency costs escalate linearly. Thus, automation provides better predictability and long-term value for content-heavy industries like e-commerce, finance, and SaaS businesses.

Time-to-Market: Agency vs Automation

A key differentiator between agency vs automation is time-to-market. In today’s global digital economy, delays in content localisation can result in missed opportunities. Agencies operate with project queues, involving multiple human touchpoints—linguists, editors, account managers—which inherently slows the process. From quote requests to delivery, a basic campaign localisation can take days or even weeks.

Automated solutions eliminate this friction. Once configured, new content is translated instantaneously or within minutes depending on the setup. For brands deploying continuous content (blogs, landing pages, product listings), this speed is transformative. Reduced lag enables synchronised launches across markets, improving first-mover advantage in seasonal campaigns and product drops.

While some may argue that speed compromises quality, modern automation tools use adaptive neural translation engines, coupled with linguistic QA models that detect anomalies. This blend ensures both speed and a baseline quality assurance framework.

Quality Control and Consistency

Quality has traditionally been the domain where agencies thrive. Their approach includes multiple human reviewers ensuring tone, terminology compliance, and stylistic consistency. However, this multi-tiered editing process can introduce subjectivity and, ironically, inconsistency—especially when different linguists rotate across projects.

Automated systems, by contrast, rely on structured data. Translation memories (TMs), glossaries, and AI-driven post-editing modules help achieve greater consistency. When combined with human-in-the-loop review cycles, the result is a hybrid model many leading firms now prefer. This is especially effective for high-volume content, such as support documentation or in-app messaging, where absolute perfection isn’t necessary but uniformity is crucial.

Moreover, automation ensures that updates to branding or terminology are disseminated instantly across markets. With agencies, deploying such updates often requires retraining linguists or manually editing existing assets, which is both costly and time-intensive.

SEO Impacts of Each Translation Method

In the realm of multilingual SEO, the method of localisation can significantly influence results. Language agencies, when briefed correctly, can deliver nuanced translations that preserve search intent and use regionally optimised keywords. However, this requires clear communication and sometimes additional keyword research for each locale—often billed as an add-on service.

Modern automation platforms now integrate multilingual SEO into their translation pipelines. These systems detect metadata tags (such as `