Key facts
| Indonesian text | Latin script, regular spelling and extensive affixation |
| Register | Formal written and colloquial Jakarta usage differ — keep one per product |
| Tokenisation | Affixes and chat abbreviations split into subwords — measure with the Plugsky token calculator |
| API compatibility | OpenAI-compatible POST https://api.plugsky.com/v1/chat/completions — chat, streaming, JSON mode and function calling |
| Models | 30+ models behind one endpoint, from free tiers to frontier reasoning |
| Free tier | Free plan with plugsky-micro and plugsky-lite, no card required |
| Deployment | Plugsky cloud, your VPC, on-prem and air-gapped options |
| Product status | Chat, streaming, JSON mode, function calling, embeddings, RAG and agents live; audio, images, batch and fine-tuning coming soon |
TL;DR
- Indonesian works through the standard endpoint with one base_url change.
- Regular spelling keeps token costs moderate — verify on chat text.
- Separate formal and colloquial registers in prompts and evals.
- Handle affixes for keyword search, not for display.
- plugsky-embed-multilingual covers Indonesian and English retrieval.
How it works, step by step
- Create a Plugsky API key on the free plan (no card) and set base_url to https://api.plugsky.com/v1.
- Send a small set of real Indonesian prompts to /v1/chat/completions and compare output across two or three models.
- Count tokens for those prompts with the Plugsky token calculator and set chunk sizes that fit your model context.
- Normalise text before indexing or prompting: standardise abbreviations and keep affixes intact for display.
- For RAG, embed with plugsky-embed-multilingual and test cross-language queries alongside Indonesian-only queries.
- Score candidate models on a Indonesian gold set with native-speaker review, then cut production traffic over.
Try it yourself
Open the OpenAI-compatible API tester →
How Plugsky handles Indonesian text
Indonesian (Bahasa Indonesia) uses the Latin alphabet and reads left to right, with transparent spelling and heavy affixation.
Formal written Indonesian differs from colloquial Jakarta usage, and Indonesian overlaps with Malay but differs in vocabulary and some spelling conventions.
Evaluate on formal and colloquial samples and check affix consistency; Indonesian readers forgive English loans but notice wrong affixes and mixed registers.
Tokenisation and cost in Indonesian
Indonesian is relatively token-efficient because spelling is regular, but affixes such as me-, -kan and -nya split words, and informal text full of slang and abbreviations needs its own counts.
- Handle formal and colloquial registers separately in prompts.
- Keep affixes attached for display; add stemmed keys only for search.
- Treat Indonesian and Malay text as distinct eval slices.
- Count tokens on chat-style text, where slang and abbreviations concentrate.
Indonesian retrieval and RAG
One multilingual collection serves Indonesian documents and Indonesian or English queries; test colloquial queries and Malay overlap before launch.
- Use plugsky-embed-multilingual for Indonesian and English retrieval.
- Add stemming or synonyms for affixed forms in keyword search.
- Separate formal and colloquial query sets.
- Check Malay queries if your audience spans both markets.
Code example: a Indonesian request
Point your existing OpenAI client at https://api.plugsky.com/v1 and pass Indonesian text in the content field — no language flag and no separate endpoint. Streaming, JSON mode and function calling keep the same request shapes.
client = OpenAI(base_url="https://api.plugsky.com/v1", api_key=os.environ["PLUGSKY_API_KEY"])
client.chat.completions.create(model="plugsky-pro", messages=[{"role": "user", "content": "Ringkas kontrak ini dalam tiga poin dalam bahasa Indonesia."}])
Start on the free plan with plugsky-micro and plugsky-lite, then compare paid models on a Indonesian gold set before cutover. See the docs for request details.
For production, log the model name and your normalisation settings with each request, and re-run the Indonesian gold set whenever either changes — language quality regressions usually come from prompt or preprocessing drift, not from the model alone.
Honest comparison
| Capability | Plugsky | Indonesian workflow today | Building in-house |
|---|---|---|---|
| API compatibility | OpenAI-compatible — change base_url and model name | Varies by provider and SDK | Full rewrite |
| Indonesian text handling | Formal and colloquial registers with affix-aware search | Depends on provider tokeniser and prompt hygiene | You build normalisation, segmentation and evals |
| Token budget | Fixed tokeniser per model; measure with the Plugsky token calculator and chunk to fit | Varies by provider and model | You host and tune each tokeniser |
| Multilingual retrieval | plugsky-embed-multilingual available for cross-language RAG | Often needs a separate embedding vendor | You serve and maintain embeddings |
| Sovereignty | Cloud, VPC, on-prem and air-gapped with residency options | Usually US/EU public endpoints | You own the full stack |
Frequently asked questions
Can Plugsky handle Indonesian text?
Yes. The API accepts UTF-8 Indonesian input on the OpenAI-compatible chat endpoint; output quality depends on the model, so compare two or three on your own prompts before choosing.
How do I estimate token usage for Indonesian?
Indonesian is relatively token-efficient because spelling is regular, but affixes such as me-, -kan and -nya split words, and informal text full of slang and abbreviations needs its own counts. Use the token calculator at /tools/llm-token-calculator before sizing context windows or chunk lengths.
Formal or colloquial Indonesian?
Match the product: formal written Indonesian for finance and government, colloquial Jakarta style for chat and consumer apps. State the choice in the system prompt.
Does Malay work with the same setup?
It is close enough to share a multilingual collection, but vocabulary differs. Keep Malay queries in a separate eval slice before assuming coverage.
Is there a multilingual embedding model?
Yes — plugsky-embed-multilingual is part of the 30+ model catalogue and is built for cross-language retrieval. Keep one embedding model per vector collection.
Can I keep data in my region?
Plugsky supports cloud, VPC, on-prem and air-gapped deployment with data-residency options; confirm your requirements with the docs and the enterprise team.
How do I migrate an existing app?
Change base_url to https://api.plugsky.com/v1 and map the model name. Streaming, JSON mode, function calling and embeddings keep the same request shapes.
Is there a free plan?
Yes — the free plan includes two free models, plugsky-micro and plugsky-lite, with no card. A 14-day full-access trial unlocks the paid catalogue.