One API, every modality.

Why AIport.

• Top up in IDR. No FX markup, no monthly fee, no expiry. Each call deducts exactly what it costs from your balance.
• One key, every modality. Text, image, image-to-image, and video generation behind a single Authorization: Bearer header.
• Provider routing & auto-refund. When a provider errors mid-job, credits are refunded automatically.
• OpenAI-compatible text endpoint. POST /v1/chat/completions is a drop-in for the OpenAI SDK, including stream: true.

Base URL

https://aiport.id

All endpoints are JSON unless noted. Async modalities (image, img2img, video) return a job_id you poll via GET /v1/jobs/{job_id}.

Send your API key in the Authorization header:

Authorization: Bearer aip_live_...

Keys are scoped to your account and bill against your IDR balance. Rotate compromised keys immediately by deleting and re-creating from the dashboard. Never embed an API key in client-side code — proxy through your own backend.

Sync vs async — read this first.

Every AIport endpoint is a normal HTTP request: POST (or GET for jobs), Authorization: Bearer header, JSON body. The difference is only in how the result comes back:

• Synchronous — /v1/chat/completions (alias /v1/text/completions). The response body *is* the result, unless you opt into stream: true. Typical latency: 1–10 s.
• Asynchronous — /v1/image/generate, /v1/image/img2img, all /v1/video/*. The POST returns immediately with { "job_id": "...", "status": "pending" }. You then poll GET /v1/jobs/{job_id} every few seconds until status becomes done (read result_url) or failed. Typical latency: 30–120 s for video, 5–30 s for image.

Async exists because images and videos can take a minute or more — too long to hold open a single HTTP request reliably.

1. Synchronous: text completion

The text endpoint is OpenAI-compatible — point the OpenAI SDK at https://aiport.id/v1, or call it directly:

curl -X POST https://aiport.id/v1/chat/completions \
  -H "Authorization: Bearer $AIPORT_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"model":"deepseek-v4-flash","messages":[{"role":"user","content":"Hello"}]}'

Response (200) — final answer is already inside choices[0].message.content:

{ "choices": [ { "message": { "role": "assistant", "content": "Hi!" } } ], "usage": { ... } }

Add "stream": true to receive the answer incrementally as Server-Sent Events (OpenAI chat.completion.chunk objects, ending with data: [DONE]). See the Text completions section for the chunk shape.

2. Asynchronous: image generation — step by step

Step 2a — POST the request. Same shape as text, just a different URL:

curl -X POST https://aiport.id/v1/image/generate \
  -H "Authorization: Bearer $AIPORT_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"model":"gpt-image-2","prompt":"Sunset over Mount Bromo","resolution":"2k"}'

Response (202) — note: no image yet, only a job id to poll:

{ "job_id": "job_01HX...", "status": "pending" }

Step 2b — GET the job until it's done:

curl https://aiport.id/v1/jobs/job_01HX... \
  -H "Authorization: Bearer $AIPORT_API_KEY"

Response (200) — when finished, status is done and result_url points at the image:

{ "job_id": "job_01HX...", "status": "done", "result_url": "https://uploads.aiport.id/results/..." }

3. Same thing, scripted with a poll loop (optional)

The block below is just the two-step flow wrapped in a shell loop — useful for trying things in a terminal:

# Submit
JOB=$(curl -s -X POST https://aiport.id/v1/image/generate \
  -H "Authorization: Bearer $AIPORT_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"model":"gpt-image-2","prompt":"Sunset over Mount Bromo","resolution":"2k"}' | jq -r .job_id)

# Poll every 3s
while true; do
  STATUS=$(curl -s https://aiport.id/v1/jobs/$JOB \
    -H "Authorization: Bearer $AIPORT_API_KEY")
  echo "$STATUS"
  case "$(echo $STATUS | jq -r .status)" in
    done|failed) break ;;
  esac
  sleep 3
done

In production, replace the shell loop with a job-polling routine in your language of choice (or a webhook on your side if you don't want to poll).

Chat-style text generation. OpenAI-compatible request and response shapes — a drop-in for the OpenAI SDK (set base_url to https://aiport.id/v1). The legacy path /v1/text/completions is a backward-compatible alias.

curl -X POST https://aiport.id/v1/chat/completions \
  -H "Authorization: Bearer $AIPORT_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "deepseek-v4-flash",
    "messages": [
      {"role": "user", "content": "Write a haiku about Jakarta traffic."}
    ]
  }'

{
  "id": "chatcmpl_...",
  "object": "chat.completion",
  "created": 1716480000,
  "model": "deepseek-v4-flash",
  "choices": [
    {
      "index": 0,
      "message": {"role": "assistant", "content": "..."},
      "finish_reason": "stop"
    }
  ],
  "usage": {
    "prompt_tokens": 12,
    "completion_tokens": 38,
    "total_tokens": 50,
    "prompt_tokens_details": {"cached_tokens": 0},
    "completion_tokens_details": {"reasoning_tokens": 0}
  }
}

Synchronous unless stream is true. prompt_tokens_details.cached_tokens reflects prompt-cache hits (billed at a reduced input rate when the upstream reports them, currently DeepSeek); completion_tokens_details.reasoning_tokens counts reasoning output. Standard OpenAI params (top_p, stop, presence_penalty, frequency_penalty, seed, …) are forwarded verbatim to OpenAI-compatible upstreams; unknown params are dropped.

With stream: true the response is text/event-stream. Each event is a chat.completion.chunk: the first carries delta.role, the rest carry delta.content, then a chunk with finish_reason: stop, a final usage-only chunk (choices: []), and the data: [DONE] terminator. If the upstream fails mid-stream the connection ends with an in-band error event — there is no provider switch once the first byte is sent, and a stream that already emitted output is not refunded.

data: {"id":"chatcmpl-...","object":"chat.completion.chunk","created":1716480000,"model":"deepseek-v4-flash","choices":[{"index":0,"delta":{"role":"assistant"},"finish_reason":null}]}

data: {"id":"chatcmpl-...","object":"chat.completion.chunk","created":1716480000,"model":"deepseek-v4-flash","choices":[{"index":0,"delta":{"content":"Hi"},"finish_reason":null}]}

data: {"id":"chatcmpl-...","object":"chat.completion.chunk","created":1716480000,"model":"deepseek-v4-flash","choices":[{"index":0,"delta":{},"finish_reason":"stop"}]}

data: {"id":"chatcmpl-...","object":"chat.completion.chunk","created":1716480000,"model":"deepseek-v4-flash","choices":[],"usage":{"prompt_tokens":12,"completion_tokens":38,"total_tokens":50,"prompt_tokens_details":{"cached_tokens":0},"completion_tokens_details":{"reasoning_tokens":0}}}

data: [DONE]

• 400 invalid_request_errormessages missing, content too long, invalid temperature, or reasoning_effort on a non-reasoning model.
• 401 unauthorizedMissing or invalid API key.
• 404 MODEL_NOT_FOUNDRequested model id does not exist or is disabled.
• 429 insufficient_quotaIDR balance cannot cover the request — top up to continue.
• 502 provider_errorUpstream AI provider failed. Non-streamed calls are refunded; a stream that already emitted output is not.

List the text/chat models available to your key, in the OpenAI models shape. OpenAI-compatible clients call this to verify the base URL and populate model pickers. GET /v1/models/{model} retrieves a single model.

curl https://aiport.id/v1/models \
  -H "Authorization: Bearer $AIPORT_API_KEY"

{
  "object": "list",
  "data": [
    {
      "id": "deepseek-v4-flash",
      "object": "model",
      "created": 1716480000,
      "owned_by": "aiport"
    }
  ]
}

Only enabled text/chat models are returned — the same ids you can pass as model to /v1/chat/completions. Image and video models are not listed here (those modalities are async and not OpenAI-compatible). created is a Unix timestamp in seconds. GET /v1/models/{model} returns a single model object, or 404 model_not_found if the id is unknown, disabled, or not a text model.

• 401 unauthorizedMissing or invalid API key.
• 404 model_not_foundGET /v1/models/{model}: id does not exist, is disabled, or is not a text model.

Tool calling is a round-trip you drive. You describe the functions the model is allowed to call; the model replies with the function name and JSON arguments; you execute it and send the result back; the model uses that result to answer.

1. Send a request with tools.

curl -X POST https://aiport.id/v1/chat/completions \
  -H "Authorization: Bearer $AIPORT_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "deepseek-v4-flash",
    "messages": [
      {"role": "user", "content": "What is the weather in Jakarta?"}
    ],
    "tools": [
      {
        "type": "function",
        "function": {
          "name": "get_weather",
          "description": "Get the current weather for a city",
          "parameters": {
            "type": "object",
            "properties": {
              "city": {"type": "string", "description": "City name"}
            },
            "required": ["city"]
          }
        }
      }
    ]
  }'

2. The model asks to call the tool. Instead of content, the assistant message carries tool_calls and finish_reason is tool_calls. arguments is a JSON string you must parse:

{
  "choices": [
    {
      "index": 0,
      "message": {
        "role": "assistant",
        "content": null,
        "tool_calls": [
          {
            "id": "call_abc123",
            "type": "function",
            "function": {
              "name": "get_weather",
              "arguments": "{\"city\":\"Jakarta\"}"
            }
          }
        ]
      },
      "finish_reason": "tool_calls"
    }
  ]
}

3. Run the function and reply with a tool message. Append the assistant's tool_calls message verbatim, then one role: "tool" message per call, echoing its tool_call_id. Keep sending tools on this follow-up request too — resubmit it on every turn of the conversation, exactly as the OpenAI SDK does, or some models reject the replayed tool history:

curl -X POST https://aiport.id/v1/chat/completions \
  -H "Authorization: Bearer $AIPORT_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "deepseek-v4-flash",
    "messages": [
      {"role": "user", "content": "What is the weather in Jakarta?"},
      {"role": "assistant", "content": null, "tool_calls": [
        {"id": "call_abc123", "type": "function",
         "function": {"name": "get_weather", "arguments": "{\"city\":\"Jakarta\"}"}}
      ]},
      {"role": "tool", "tool_call_id": "call_abc123", "content": "{\"temp_c\":31,\"sky\":\"partly cloudy\"}"}
    ],
    "tools": [
      {"type": "function", "function": {"name": "get_weather",
        "description": "Get the current weather for a city",
        "parameters": {"type": "object",
          "properties": {"city": {"type": "string", "description": "City name"}},
          "required": ["city"]}}}
    ]
  }'

The model now replies with normal content (finish_reason: "stop"):

{ "choices": [ { "message": { "role": "assistant", "content": "It's 31°C and partly cloudy in Jakarta." }, "finish_reason": "stop" } ] }

Forcing or disabling calls. Use tool_choice: "auto" (default when tools is set) lets the model decide, "required" forces some tool call, "none" forces a plain-text reply, and { "type": "function", "function": { "name": "get_weather" } } forces one specific function. Set parallel_tool_calls: false to limit the model to one call per turn.

Streaming. With stream: true, tool calls arrive incrementally on delta.tool_calls (each fragment carries an index; function.arguments is concatenated across chunks). Accumulate by index until the terminating chunk with finish_reason: "tool_calls".

Round-trip integrity. Send each assistant tool_calls message back exactly as you received it — including the id on every call — and pair it with a matching tool_call_id in your tool reply. Some models (e.g. Gemini reasoning models) attach opaque state to the call id that must survive the round-trip, so don't rewrite or drop it.

Text-to-image. Returns an async job_id — poll GET /v1/jobs/{job_id} until status is done.

curl -X POST https://aiport.id/v1/image/generate \
  -H "Authorization: Bearer $AIPORT_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "gpt-image-2",
    "prompt": "A traditional Balinese temple at golden hour, cinematic",
    "aspect_ratio": "16:9",
    "resolution": "2k"
  }'

{
  "job_id": "job_01HX...",
  "status": "pending"
}

Poll GET /v1/jobs/{job_id} every 2–5 seconds until status is 'done' (then read result_url) or 'failed'. Image generation typically takes 10–60 seconds at 1k, 30–120 seconds at 2k, and up to 180 seconds at 4k. Jobs that exceed 10 minutes are auto-failed and credits are refunded.

• 400 invalid_requestMissing prompt, invalid aspect_ratio, or invalid resolution.
• 401 unauthorizedMissing or invalid API key.
• 402 insufficient_balanceIDR balance cannot cover the request.
• 404 MODEL_NOT_FOUNDRequested model id does not exist or is disabled.

Upload a reference image to be used by /v1/image/img2img. Returns an upload_id you reference in the img2img call.

curl -X POST https://aiport.id/v1/image/upload \
  -H "Authorization: Bearer $AIPORT_API_KEY" \
  -F "file=@./reference.jpg"

{
  "upload_id": "upl_01HX...",
  "url": "https://uploads.aiport.id/uploads/..."
}

• 400Missing file or unsupported MIME type.
• 401 unauthorizedMissing or invalid API key.
• 413File exceeds the per-modality size limit.

Image-to-image editing. Provide reference image URL(s) (from /v1/image/upload or any HTTPS URL) plus a prompt. Returns an async job_id.

curl -X POST https://aiport.id/v1/image/img2img \
  -H "Authorization: Bearer $AIPORT_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "prompt": "Make it look like a Studio Ghibli scene",
    "images": ["https://uploads.aiport.id/uploads/.../ref.jpg"],
    "resolution": "2k"
  }'

{ "job_id": "job_01HX...", "status": "pending" }

Poll GET /v1/jobs/{job_id} every 2–5 seconds. Image-to-image typically takes 10–60 seconds at 1k and 30–180 seconds at higher resolutions. Jobs that exceed 10 minutes are auto-failed and credits are refunded.

• 400Missing prompt, invalid image URL, or unsupported model parameter.
• 401 unauthorizedMissing or invalid API key.
• 402 insufficient_balanceIDR balance cannot cover the request.

Text-to-video. Async — returns job_id, then poll /v1/jobs/{job_id} until done. Billing is per generated second by output resolution.

curl -X POST https://aiport.id/v1/video/generate \
  -H "Authorization: Bearer $AIPORT_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "seedance-2.0-fast",
    "prompt": "A drone shot flying over rice terraces at sunrise",
    "resolution": "1080p",
    "duration": 6,
    "ratio": "16:9"
  }'

{ "job_id": "job_01HX...", "status": "pending" }

Video jobs typically take 30–120 seconds. Poll every 3–5 seconds.

• 400Invalid resolution, duration, ratio, or missing prompt.
• 401 unauthorizedMissing or invalid API key.
• 402 insufficient_balanceIDR balance cannot cover the request.

Image-to-video. Provide a first frame (and optional last frame) URL; the model interpolates motion. Async job.

curl -X POST https://aiport.id/v1/video/img2video \
  -H "Authorization: Bearer $AIPORT_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "seedance-2.0-fast-img2video",
    "prompt": "Camera slowly pushes in, leaves drift across frame",
    "first_frame_url": "https://uploads.aiport.id/uploads/.../start.jpg",
    "resolution": "720p",
    "duration": 5
  }'

{ "job_id": "job_01HX...", "status": "pending" }

• 400Missing first_frame_url, invalid duration/resolution.
• 401 unauthorizedMissing or invalid API key.
• 402 insufficient_balanceIDR balance cannot cover the request.

Multimodal video generation — combine up to 9 reference images, 3 reference videos, and 3 reference audio tracks with a text prompt. Per-resolution per-second billing with a min-billable-seconds floor (see /pricing).

curl -X POST https://aiport.id/v1/video/multimodal \
  -H "Authorization: Bearer $AIPORT_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "seedance-2.0-fast-multimodal",
    "prompt": "Subject from image1 walking through scene from video1",
    "images": ["https://uploads.aiport.id/uploads/.../subject.jpg"],
    "videos": ["https://uploads.aiport.id/uploads/.../scene.mp4"],
    "resolution": "720p",
    "duration": 6
  }'

{ "job_id": "job_01HX...", "status": "pending" }

When any reference asset is supplied, billing uses the 'with reference' tier and a per-duration min-billable floor — see /pricing for the exact table.

• 400Too many reference assets, invalid URL, or invalid resolution/duration.
• 401 unauthorizedMissing or invalid API key.
• 402 insufficient_balanceIDR balance cannot cover the request.

Poll an async job (image, img2img, or video). Returns the current status and — when done — the result URL.

curl https://aiport.id/v1/jobs/job_01HX... \
  -H "Authorization: Bearer $AIPORT_API_KEY"

{
  "job_id": "job_01HX...",
  "status": "done",
  "result_url": "https://uploads.aiport.id/results/...",
  "error_message": null,
  "created_at": "2026-05-23T08:00:00Z"
}

status is one of: 'pending', 'processing', 'done', 'failed'. On 'failed', check error_message for details (e.g. 'Provider failed. Please try again.' or 'Job timed out after N minutes'). Credits are auto-refunded on failure.

• 401 unauthorizedMissing or invalid API key.
• 404Job does not exist or does not belong to this API key's owner.