Overview

Step 2: Reduce Per-Seat Costs

Seats are the largest cost driver. Strategies to minimize:

import { LokaliseApi } from "@lokalise/node-api";
const lok = new LokaliseApi({ apiKey: process.env.LOKALISE_API_TOKEN! });

// Audit: Find inactive contributors (no activity in 90 days)
async function findInactiveContributors(projectId: string): Promise<void> {
  const contributors = await lok.contributors().list({
    project_id: projectId,
    limit: 500,
  });

  console.log("=== Contributor Activity Audit ===");
  for (const c of contributors.items) {
    const langs = c.languages
      .map((l: { lang_iso: string }) => l.lang_iso)
      .join(", ");
    console.log(
      `${c.fullname} <${c.email}> — ` +
      `admin: ${c.is_admin}, reviewer: ${c.is_reviewer}, ` +
      `languages: [${langs}]`
    );
  }

  console.log(`\nTotal contributors: ${contributors.items.length}`);
  console.log(
    "Review: Remove freelancers between tasks. " +
    "Use contributor groups for batch management."
  );
}

// Strategy: Use task-based access for freelance translators
// - Add freelancers when a translation task opens
// - Remove them when the task closes
// - This avoids paying for idle seats
// Cost example: 10 individual seats = ~$1,200/month
//               3 permanent + task-based freelancers = ~$360/month

Step 3: Maximize Translation Memory (TM) Hits

TM matches reduce human translation volume. Keys with 100% TM match cost zero for translation.

// Strategy: Translate similar projects sequentially to build TM
// Don't translate 3 apps in parallel — do one first, seed the TM,
// then the others get 30-50% free matches on shared strings

// Enable automations on upload to apply TM automatically
const uploadResult = await lok.files().upload(projectId, {
  data: base64FileData,
  filename: "en.json",
  lang_iso: "en",
  use_automations: true,      // Apply TM + MT suggestions
  replace_modified: true,
  detect_icu_plurals: true,
});

// Check TM coverage after upload
const languages = await lok.languages().list({ project_id: projectId, limit: 50 });
for (const lang of languages.items) {
  console.log(
    `${lang.lang_iso}: ${lang.statistics?.progress ?? 0}% translated, ` +
    `${lang.statistics?.words_to_do ?? "?"} words remaining`
  );
}

Step 4: Machine Translation Triage

Pre-translate low-risk content with MT. Reserve human translation for critical strings.

set -euo pipefail
# Identify untranslated key volume per language
curl -sf "https://api.lokalise.com/api2/projects/${LOKALISE_PROJECT_ID}/languages" \
  -H "X-Api-Token: ${LOKALISE_API_TOKEN}" \
  | jq '.languages[] | {
    locale: .lang_iso,
    progress: .statistics.progress,
    words_to_do: .statistics.words_to_do
  }'

MT triage matrix — decide by key prefix:

Step 5: Clean Up Dead Keys

Orphaned keys waste per-word costs and clutter the project.

import { readFileSync } from "fs";

async function findOrphanedKeys(
  projectId: string,
  sourceCodeDir: string
): Promise<string[]> {
  // Get all keys from Lokalise
  const allKeys: string[] = [];
  let cursor: string | undefined;
  do {
    const page = await lok.keys().list({
      project_id: projectId,
      limit: 500,
      ...(cursor ? { cursor } : {}),
    });
    for (const k of page.items) {
      allKeys.push(k.key_name.web ?? k.key_name.other ?? "");
    }
    cursor = page.hasNextCursor() ? page.nextCursor() : undefined;
  } while (cursor);

  console.log(`Lokalise keys: ${allKeys.length}`);

  // Compare against source code references
  // (simplified — adjust grep pattern for your i18n framework)
  const { execSync } = await import("child_process");
  const sourceRefs = execSync(
    `grep -roh "t(['\"][^'\"]*['\"])" ${sourceCodeDir} 2>/dev/null || true`,
    { encoding: "utf-8" }
  )
    .split("\n")
    .map((line) => line.replace(/^t\(['"]/, "").replace(/['"]\)$/, ""))
    .filter(Boolean);

  const sourceKeySet = new Set(sourceRefs);
  const orphaned = allKeys.filter((k) => !sourceKeySet.has(k));

  console.log(`Source code references: ${sourceKeySet.size}`);
  console.log(`Orphaned keys: ${orphaned.length}`);

  return orphaned;
}

// Archive orphaned keys to stop paying for their translations
async function archiveKeys(projectId: string, keyNames: string[]): Promise<void> {
  // Look up key IDs
  for (const name of keyNames.slice(0, 50)) {
    const result = await lok.keys().list({
      project_id: projectId,
      filter_keys: name,
      limit: 1,
    });
    if (result.items.length > 0) {
      await lok.keys().update(result.items[0].key_id, {
        project_id: projectId,
        is_archived: true,
      });
    }
    await new Promise((r) => setTimeout(r, 170)); // Rate limit
  }
}

Step 6: Monitor Monthly Spend

set -euo pipefail
echo "=== Monthly Cost Estimate ==="

# Count total seats across teams
SEAT_COUNT=$(curl -sf "https://api.lokalise.com/api2/teams" \
  -H "X-Api-Token: ${LOKALISE_API_TOKEN}" \
  | jq '[.teams[].users_count] | add')

# Estimate based on plan tier (adjust rate for your plan)
RATE_PER_SEAT=120  # Essential plan — adjust to 290 for Pro
MONTHLY_COST=$((SEAT_COUNT * RATE_PER_SEAT))

echo "Active seats: ${SEAT_COUNT}"
echo "Estimated monthly cost: \$${MONTHLY_COST} (at \$${RATE_PER_SEAT}/seat)"
echo ""
echo "Cost reduction levers:"
echo "  1. Remove inactive contributors (task-based access)"
echo "  2. Use contributor groups instead of individual invites"
echo "  3. Pre-translate with MT to reduce human translation volume"
echo "  4. Archive orphaned keys to reduce per-word charges"
echo "  5. Translate similar projects sequentially to maximize TM"

Output

• Usage audit report: projects, keys, languages, contributor seat count
• Inactive contributor identification for seat optimization
• TM leverage strategy (sequential translation, automation-enabled uploads)
• MT triage matrix mapping key prefixes to translation method
• Orphaned key detection and archival workflow
• Monthly cost estimate with reduction levers

Error Handling

Examples

Cost Comparison Scenarios

Solo project with 5 languages: 2 full-time translators + 8 freelancers. Move freelancers to task-based access. Seats drop from 10 to 2, saving ~$960/month.

Multi-app suite sharing terminology: Three apps share UI strings. Translate the largest first to seed TM, then translate the others. TM matches on shared strings cut human translation volume by 30-50%.

10,000-key project MT triage: Tag keys by content type. Apply MT to tooltip.*, help.*, log.* prefixes (40% of keys). Route legal.*, marketing.*, ui.cta.* to humans. Saves ~$2,000 per target language.

Resources

• Lokalise Pricing Plans
• Lokalise API: Project Statistics
• Translation Memory in Lokalise
• Lokalise Machine Translation
• Keys API: List and Filter

Next Steps

For monitoring translation pipeline health and costs over time, see lokalise-observability.