Chat / LLM

FOTOhub provides two chat completion endpoints supporting multiple LLM providers. The OpenAI-compatible endpoint follows the standard OpenAI chat completions format, making it a drop-in replacement for existing integrations. The Bedrock-native endpoint provides access to AWS Bedrock models with token-based billing for precise cost control.

Both endpoints support streaming via Server-Sent Events, multi-turn conversations, system prompts, and return detailed billing information in every response.

OpenAI SDK Compatible

The /v1/ai/chat/completions endpoint is fully compatible with the OpenAI Python and TypeScript SDKs. Simply change the base URL to https://apis.fotohub.app/v1/ai and use your FOTOhub API key.

OpenAI-Compatible Chat Completions

POST /v1/ai/chat/completions

Auth: API Key (Bearer token) | Billing: 1-2 credits per request

Standard chat completions endpoint compatible with OpenAI SDKs. Supports Google Gemini, OpenAI GPT, and Anthropic Claude models with credit-based billing.

Parameters

Parameter	Type	Required	Default	Description
`model`	string	No	`gemini-flash`	Model ID to use for completion. See pricing table below for available models.
`messages`	array	Yes	--	Array of message objects. Each message has a `role` (system, user, or assistant) and `content` (string). System messages set behavior, user messages are inputs, assistant messages are for multi-turn context.
`temperature`	number	No	`0.7`	Controls randomness/creativity of the output. Range 0-2. Lower values (0.1-0.3) produce focused, deterministic output. Higher values (1.0-2.0) produce more creative, varied responses.
`max_tokens`	integer	No	`2048`	Maximum number of tokens to generate in the response. Limits output length. Does not guarantee exact length -- model may stop earlier at a natural completion point.
`stream`	boolean	No	`false`	Enable Server-Sent Events streaming. When true, partial message deltas are sent as they become available. See Streaming section below for parsing details.

Available Models

FOTOhub supports 35+ LLM models from 12 providers. All accessible through the same /v1/ai/chat/completions endpoint.

FOTOhub

Model	ID	Description
FOTOhub AI	`fotohub-ai`	Default model — fast, capable, balanced
FOTOhub Coder	`fotohub-coder`	Optimized for code generation

Anthropic Claude

Model	ID	Description
Claude Haiku 4.5	`claude-haiku-4-5`	Fast, cheap — good for simple tasks
Claude Sonnet 4.6	`claude-sonnet-4-6`	Intelligent + fast — best value
Claude Opus 4.5	`claude-opus-4-5`	Deep thinking, extended reasoning
Claude Opus 4.6	`claude-opus-4-6`	Maximum capabilities — latest

OpenAI

Model	ID	Description
GPT-5.4	`gpt-5.4`	Newest, fast, multimodal, versatile
GPT-5.4 Pro	`gpt-5.4-pro`	Premium deep reasoning, complex analysis
GPT-5.1	`gpt-5.1`	Creative, multi-modal

xAI Grok

Model	ID	Description
Grok 4 Fast	`grok-4-fast-reasoning`	Fast reasoning, 2M context
Grok 4.1 Fast	`grok-4-1-fast-reasoning`	Latest reasoning, 2M context
Grok 4.1 Fast NR	`grok-4-1-fast-non-reasoning`	No reasoning overhead

DeepSeek

Model	ID	Description	Tier
DeepSeek V3.2	`deepseek-v3.2`	Fast, cheap, general purpose (Bedrock native)	Free
DeepSeek V4 Flash	`deepseek-v4-flash-260425`	Fast reasoning, efficient	Starter
DeepSeek V4 Pro	`deepseek-v4-pro-260425`	Deep reasoning, math, code	Medium
DeepSeek R1	`deepseek-r1`	Chain-of-thought reasoning specialist	Starter

Alibaba Qwen

Model	ID	Description	Tier
Qwen Flash	`qwen-flash`	Efficient, cheapest Qwen	Free
Qwen Plus	`qwen-plus`	Reasoning-focused, general	Free
Qwen 3 Max	`qwen3-max`	Maximum capabilities, multilingual	Medium
Qwen3 Next 80B	`qwen3-next-80b`	MoE 80B, strong reasoning, multilingual	Starter
Qwen3 Coder	`qwen3-coder`	Code specialist MoE, generation + review	Starter

Moonshot Kimi

Model	ID	Description	Tier
Kimi K2.5	`kimi-k2.5`	Latest, intelligent, 1M context	Starter
Kimi K2 Thinking	`kimi-k2-thinking`	Deep reasoning, 1M context, chain-of-thought	Medium

Amazon Nova

Model	ID	Description	Tier
Nova Micro	`nova-micro`	Ultra fast, text-only, classification	Free
Nova Lite	`nova-lite`	Fast, vision, budget	Free
Nova 2 Lite	`nova-2-lite`	New gen, fast, vision	Free
Nova Pro	`nova-pro`	Balanced, vision, reasoning	Starter

Mistral AI

Model	ID	Description	Tier
Mistral Large 3	`mistral-large-3`	675B params, vision, max capabilities	Medium
Devstral 2	`devstral-2`	123B code specialist, software engineering	Starter
Magistral Small	`magistral-small`	Reasoning specialist, vision, extended thinking	Starter

NVIDIA

Model	ID	Description	Tier
Nemotron 120B	`nemotron-120b`	MoE (12B active), fast reasoning, cheap	Starter

OpenAI (Bedrock)

Model	ID	Description	Tier
GPT-OSS 120B	`gpt-oss-120b`	Open-source 120B, general purpose	Starter

MiniMax

Model	ID	Description	Tier
MiniMax M2.5	`minimax-m2.5`	Reasoning, creative, good value	Starter

Z.AI

Model	ID	Description	Tier
GLM 5	`glm-5`	Intelligent, balanced	Starter
GLM 4.7	`glm-4.7`	Budget, fast, multilingual	Free

Model Selection Guide

Free & ultra-cheap: fotohub-ai, nova-micro, deepseek-v3.2, glm-4.7, qwen-flash
Best balance: claude-sonnet-4-6, qwen3-next-80b, nemotron-120b, devstral-2
Maximum quality: claude-opus-4-6, gpt-5.4-pro, gpt-5.4, mistral-large-3, grok-4-1-fast-reasoning
Reasoning/thinking: deepseek-r1, kimi-k2-thinking, magistral-small, qwen3-next-80b, claude-opus-4-5
Code generation: fotohub-coder, devstral-2, qwen3-coder, claude-sonnet-4-6
Long context (1M+): kimi-k2.5, kimi-k2-thinking, grok-4-fast-reasoning

Response

json

{
  "id": "chatcmpl-fh-abc123",
  "object": "chat.completion",
  "created": 1719849600,
  "model": "gemini-flash",
  "credits_used": 1,
  "billing": {
    "method": "credits",
    "credits_used": 1,
    "pln_charged": 0.0375
  },
  "choices": [
    {
      "index": 0,
      "message": {
        "role": "assistant",
        "content": "The Eiffel Tower is a wrought-iron lattice tower located on the Champ de Mars in Paris, France. It was constructed from 1887 to 1889 as the centerpiece of the 1889 World's Fair."
      },
      "finish_reason": "stop"
    }
  ],
  "usage": {
    "prompt_tokens": 25,
    "completion_tokens": 150,
    "total_tokens": 175
  }
}

Bedrock-Native Chat (Token-Based Billing)

POST /v1/ai/chat/bedrock

Auth: API Key (Bearer token) | Billing: token-based (per-token pricing)

Direct access to AWS Bedrock models including Claude (Anthropic) and Nova (Amazon) families. Uses precise token-based billing with per-token cost breakdowns.

Parameters

Parameter	Type	Required	Default	Description
`model`	string	Yes	--	Bedrock model ID. Must be one of the supported models listed in the pricing table below. No default -- model must be explicitly specified.
`messages`	array	Yes	--	Array of message objects with `role` (user or assistant) and `content` (string). Note: system messages are passed via the separate `system` parameter for Bedrock models.
`temperature`	number	No	`0.7`	Controls randomness. Range 0-1 for Claude models, 0-2 for Nova models. Lower = more deterministic.
`max_tokens`	integer	No	`4096`	Maximum tokens to generate. Claude models support up to 8192. Nova models support up to 4096.
`system`	string	No	--	System prompt that defines the assistant's behavior and persona. Passed separately from messages for Bedrock's native format. Supports multi-paragraph instructions.
`stream`	boolean	No	`false`	Enable SSE streaming for real-time token delivery. Same format as the OpenAI-compatible endpoint.

Bedrock Models (Token-Based Pricing)

All models below run natively on AWS Bedrock with per-token PLN billing. Prices shown are blended average (input + output).

Anthropic Claude

Model	ID	Input ($/1M)	Output ($/1M)	Credits	Features
Claude Sonnet 4.6	`claude-sonnet-4-6`	$3	$15	2	Vision, 200K ctx, tool-use
Claude Opus 4.6	`claude-opus-4-6`	$15	$75	5	Vision, 200K ctx, deepest thinking
Claude Opus 4.5	`claude-opus-4-5`	$15	$75	5	Vision, 200K ctx, extended reasoning
Claude Haiku 4.5	`claude-haiku-4-5`	$1	$5	1	Vision, 200K ctx, fastest

Amazon Nova

Model	ID	Input ($/1M)	Output ($/1M)	Credits	Features
Nova Pro	`nova-pro`	$0.80	$3.20	0.5	Vision, reasoning
Nova Lite	`nova-lite`	$0.06	$0.24	0.01	Vision, fast
Nova 2 Lite	`nova-2-lite`	$0.04	$0.16	0.01	Vision, new gen
Nova Micro	`nova-micro`	$0.035	$0.14	0.01	Text-only, cheapest

DeepSeek (Bedrock)

Model	ID	Input ($/1M)	Output ($/1M)	Credits	Features
DeepSeek V3.2	`deepseek-v3.2`	$0.28	$1.12	0.5	128K ctx, general
DeepSeek R1	`deepseek-r1`	$0.45	$1.80	1	Reasoning, CoT

Mistral AI (Bedrock)

Model	ID	Input ($/1M)	Output ($/1M)	Credits	Features
Mistral Large 3	`mistral-large-3`	$2.00	$6.00	2	675B, vision
Devstral 2	`devstral-2`	$0.20	$0.80	1.5	123B, code specialist
Magistral Small	`magistral-small`	$0.20	$0.60	1	Reasoning, vision

Qwen (Bedrock)

Model	ID	Input ($/1M)	Output ($/1M)	Credits	Features
Qwen3 Next 80B	`qwen3-next-80b`	$0.20	$0.60	1	MoE, reasoning
Qwen3 Coder	`qwen3-coder`	$0.12	$0.32	1	Code specialist

NVIDIA (Bedrock)

Model	ID	Input ($/1M)	Output ($/1M)	Credits	Features
Nemotron 120B	`nemotron-120b`	$0.19	$0.48	0.8	MoE 12B active, fast

Moonshot (Bedrock)

Model	ID	Input ($/1M)	Output ($/1M)	Credits	Features
Kimi K2 Thinking	`kimi-k2-thinking`	$0.20	$0.80	2	1M ctx, reasoning
Kimi K2.5	`kimi-k2.5`	$0.60	$2.50	1	1M ctx, vision

OpenAI (Bedrock)

Model	ID	Input ($/1M)	Output ($/1M)	Credits	Features
GPT-OSS 120B	`gpt-oss-120b`	$0.20	$0.60	1.5	Open-source, general

MiniMax (Bedrock)

Model	ID	Input ($/1M)	Output ($/1M)	Credits	Features
MiniMax M2.5	`minimax-m2.5`	$1.00	$4.00	1	Reasoning, creative

Z.AI (Bedrock)

Model	ID	Input ($/1M)	Output ($/1M)	Credits	Features
GLM 5	`glm-5`	$0.40	$1.60	1	Multilingual
GLM 4.7	`glm-4.7`	$0.08	$0.20	0.3	Budget, fast

Token-Based Billing

Bedrock models use token-based billing, meaning costs are calculated per-token after generation completes. The billing.cost_breakdown field in the response provides exact input/output token counts and the resulting PLN charge. Credits are also deducted as a minimum charge per request.

Response

json

{
  "id": "chatcmpl-bedrock-xyz789",
  "object": "chat.completion",
  "created": 1719849600,
  "model": "claude-sonnet-4.6",
  "billing": {
    "method": "tokens",
    "credits_used": 2,
    "pln_charged": 0.0945,
    "cost_breakdown": {
      "input_tokens": 150,
      "output_tokens": 500,
      "total_tokens": 650,
      "cost_pln": 0.0945
    }
  },
  "choices": [
    {
      "index": 0,
      "message": {
        "role": "assistant",
        "content": "Here is a detailed analysis of the architectural patterns used in your codebase..."
      },
      "finish_reason": "stop"
    }
  ],
  "usage": {
    "input_tokens": 150,
    "output_tokens": 500,
    "total_tokens": 650
  }
}

Streaming

Both endpoints support streaming via Server-Sent Events (SSE). Set stream: true in your request to receive partial responses as they are generated. Each chunk contains a delta with the new content fragment. The stream ends with a [DONE] message.

SSE Format

Each event is prefixed with data: followed by a JSON object. The final event is data: [DONE]. Events are separated by two newlines. The billing field is included only in the final chunk before [DONE].

Stream Chunk Format

data: {"id":"chatcmpl-fh-abc123","object":"chat.completion.chunk","model":"gemini-flash","choices":[{"index":0,"delta":{"role":"assistant","content":"The"},"finish_reason":null}]}

data: {"id":"chatcmpl-fh-abc123","object":"chat.completion.chunk","model":"gemini-flash","choices":[{"index":0,"delta":{"content":" Eiffel"},"finish_reason":null}]}

data: {"id":"chatcmpl-fh-abc123","object":"chat.completion.chunk","model":"gemini-flash","choices":[{"index":0,"delta":{"content":" Tower"},"finish_reason":null}]}

data: {"id":"chatcmpl-fh-abc123","object":"chat.completion.chunk","model":"gemini-flash","choices":[{"index":0,"delta":{},"finish_reason":"stop"}],"billing":{"method":"credits","credits_used":1,"pln_charged":0.0375},"usage":{"prompt_tokens":25,"completion_tokens":48,"total_tokens":73}}

data: [DONE]

Streaming Example -- SSE Parsing

PythonTypeScriptcURL

python

import requests
import json

url = "https://apis.fotohub.app/v1/ai/chat/completions"
headers = {
    "Authorization": "Bearer fh_live_your_api_key",
    "Content-Type": "application/json"
}
payload = {
    "model": "gemini-flash",
    "messages": [{"role": "user", "content": "Explain quantum computing"}],
    "stream": True
}

response = requests.post(url, json=payload, headers=headers, stream=True)

full_content = ""
for line in response.iter_lines():
    if line:
        line = line.decode("utf-8")
        if line.startswith("data: "):
            data = line[6:]  # Remove "data: " prefix
            if data == "[DONE]":
                break
            chunk = json.loads(data)
            delta = chunk["choices"][0]["delta"]
            if "content" in delta:
                full_content += delta["content"]
                print(delta["content"], end="", flush=True)

print()  # Final newline
print(f"Total: {full_content}")

typescript

const response = await fetch("https://apis.fotohub.app/v1/ai/chat/completions", {
  method: "POST",
  headers: {
    "Authorization": "Bearer fh_live_your_api_key",
    "Content-Type": "application/json",
  },
  body: JSON.stringify({
    model: "gemini-flash",
    messages: [{ role: "user", content: "Explain quantum computing" }],
    stream: true,
  }),
});

const reader = response.body!.getReader();
const decoder = new TextDecoder();
let fullContent = "";

while (true) {
  const { done, value } = await reader.read();
  if (done) break;

  const text = decoder.decode(value, { stream: true });
  const lines = text.split("\n");

  for (const line of lines) {
    if (line.startsWith("data: ")) {
      const data = line.slice(6);
      if (data === "[DONE]") break;

      const chunk = JSON.parse(data);
      const content = chunk.choices[0]?.delta?.content;
      if (content) {
        fullContent += content;
        process.stdout.write(content);
      }
    }
  }
}

bash

curl -X POST "https://apis.fotohub.app/v1/ai/chat/completions" \
  -H "Authorization: Bearer fh_live_your_api_key" \
  -H "Content-Type: application/json" \
  -N \
  -d '{
    "model": "gemini-flash",
    "messages": [
      {"role": "user", "content": "Explain quantum computing"}
    ],
    "stream": true
  }'

Token Counting & Billing

Tokens are the fundamental unit of text processing for LLMs. A token is approximately 4 characters or 0.75 words in English. Both input (prompt) and output (completion) tokens are counted toward billing.

Credit-Based (OpenAI-Compatible Endpoint)

Fixed credit cost per request (1-2 credits)
PLN charge calculated from total tokens used
Credits deducted immediately on request start
If generation fails, credits are refunded
Token counts included in response for transparency

Token-Based (Bedrock Endpoint)

Per-token pricing (input + output separately)
Input tokens typically cheaper than output tokens
Exact cost shown in the cost_breakdown field
Minimum credit charge applies per request
Cost = (input_tokens x input_rate) + (output_tokens x output_rate)

Estimating Costs

A typical conversational exchange (100-word prompt, 200-word response) uses approximately 75 input tokens + 150 output tokens = 225 total tokens. With gemini-flash, this costs approximately 0.0001 PLN. With claude-sonnet-4.6 on Bedrock, approximately 0.012 PLN.

Code Examples

Basic Chat Completion

PythonTypeScriptcURL

python

import requests

url = "https://apis.fotohub.app/v1/ai/chat/completions"
headers = {
    "Authorization": "Bearer fh_live_your_api_key",
    "Content-Type": "application/json"
}

response = requests.post(url, json={
    "model": "gemini-flash",
    "messages": [
        {"role": "user", "content": "What is the capital of France?"}
    ],
    "temperature": 0.3,
    "max_tokens": 256
}, headers=headers)

data = response.json()
print(data["choices"][0]["message"]["content"])
print(f"Tokens used: {data['usage']['total_tokens']}")
print(f"Credits used: {data['credits_used']}")

typescript

const response = await fetch("https://apis.fotohub.app/v1/ai/chat/completions", {
  method: "POST",
  headers: {
    "Authorization": "Bearer fh_live_your_api_key",
    "Content-Type": "application/json",
  },
  body: JSON.stringify({
    model: "gemini-flash",
    messages: [
      { role: "user", content: "What is the capital of France?" }
    ],
    temperature: 0.3,
    max_tokens: 256,
  }),
});

const data = await response.json();
console.log(data.choices[0].message.content);
console.log(`Tokens used: ${data.usage.total_tokens}`);
console.log(`Credits used: ${data.credits_used}`);

bash

curl -X POST "https://apis.fotohub.app/v1/ai/chat/completions" \
  -H "Authorization: Bearer fh_live_your_api_key" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "gemini-flash",
    "messages": [
      {"role": "user", "content": "What is the capital of France?"}
    ],
    "temperature": 0.3,
    "max_tokens": 256
  }'

Multi-Turn Conversation

PythonTypeScriptcURL

python

import requests

url = "https://apis.fotohub.app/v1/ai/chat/completions"
headers = {
    "Authorization": "Bearer fh_live_your_api_key",
    "Content-Type": "application/json"
}

# Build conversation history
messages = [
    {"role": "system", "content": "You are a helpful coding assistant. Be concise."},
    {"role": "user", "content": "How do I read a file in Python?"},
    {"role": "assistant", "content": "Use open() with a context manager:\n\nwith open('file.txt', 'r') as f:\n    content = f.read()"},
    {"role": "user", "content": "What about reading it line by line?"}
]

response = requests.post(url, json={
    "model": "gemini-pro",
    "messages": messages,
    "temperature": 0.5,
    "max_tokens": 512
}, headers=headers)

data = response.json()
print(data["choices"][0]["message"]["content"])

# Append assistant response for next turn
messages.append(data["choices"][0]["message"])

typescript

interface Message {
  role: "system" | "user" | "assistant";
  content: string;
}

const messages: Message[] = [
  { role: "system", content: "You are a helpful coding assistant. Be concise." },
  { role: "user", content: "How do I read a file in Python?" },
  { role: "assistant", content: "Use open() with a context manager:\n\nwith open('file.txt', 'r') as f:\n    content = f.read()" },
  { role: "user", content: "What about reading it line by line?" },
];

const response = await fetch("https://apis.fotohub.app/v1/ai/chat/completions", {
  method: "POST",
  headers: {
    "Authorization": "Bearer fh_live_your_api_key",
    "Content-Type": "application/json",
  },
  body: JSON.stringify({
    model: "gemini-pro",
    messages,
    temperature: 0.5,
    max_tokens: 512,
  }),
});

const data = await response.json();
console.log(data.choices[0].message.content);

// Append for next turn
messages.push(data.choices[0].message);

bash

curl -X POST "https://apis.fotohub.app/v1/ai/chat/completions" \
  -H "Authorization: Bearer fh_live_your_api_key" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "gemini-pro",
    "messages": [
      {"role": "system", "content": "You are a helpful coding assistant."},
      {"role": "user", "content": "How do I read a file in Python?"},
      {"role": "assistant", "content": "Use open() with a context manager."},
      {"role": "user", "content": "What about reading it line by line?"}
    ],
    "temperature": 0.5,
    "max_tokens": 512
  }'

Bedrock with System Prompt

PythonTypeScriptcURL

python

import requests

url = "https://apis.fotohub.app/v1/ai/chat/bedrock"
headers = {
    "Authorization": "Bearer fh_live_your_api_key",
    "Content-Type": "application/json"
}

response = requests.post(url, json={
    "model": "claude-sonnet-4.6",
    "system": """You are a senior software architect. Provide detailed,
production-ready advice. Include code examples when relevant.
Consider scalability, maintainability, and security in all recommendations.""",
    "messages": [
        {"role": "user", "content": "Design a rate limiting system for a REST API that handles 10K req/s"}
    ],
    "temperature": 0.4,
    "max_tokens": 4096
}, headers=headers)

data = response.json()
content = data["choices"][0]["message"]["content"]
billing = data["billing"]

print(content)
print(f"\n--- Billing ---")
print(f"Method: {billing['method']}")
print(f"Input tokens: {billing['cost_breakdown']['input_tokens']}")
print(f"Output tokens: {billing['cost_breakdown']['output_tokens']}")
print(f"Total cost: {billing['pln_charged']} PLN")

typescript

const response = await fetch("https://apis.fotohub.app/v1/ai/chat/bedrock", {
  method: "POST",
  headers: {
    "Authorization": "Bearer fh_live_your_api_key",
    "Content-Type": "application/json",
  },
  body: JSON.stringify({
    model: "claude-sonnet-4.6",
    system: `You are a senior software architect. Provide detailed,
production-ready advice. Include code examples when relevant.
Consider scalability, maintainability, and security.`,
    messages: [
      { role: "user", content: "Design a rate limiting system for a REST API that handles 10K req/s" }
    ],
    temperature: 0.4,
    max_tokens: 4096,
  }),
});

const data = await response.json();
const content = data.choices[0].message.content;
const billing = data.billing;

console.log(content);
console.log("\n--- Billing ---");
console.log(`Method: ${billing.method}`);
console.log(`Input tokens: ${billing.cost_breakdown.input_tokens}`);
console.log(`Output tokens: ${billing.cost_breakdown.output_tokens}`);
console.log(`Total cost: ${billing.pln_charged} PLN`);

bash

curl -X POST "https://apis.fotohub.app/v1/ai/chat/bedrock" \
  -H "Authorization: Bearer fh_live_your_api_key" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "claude-sonnet-4.6",
    "system": "You are a senior software architect. Provide production-ready advice.",
    "messages": [
      {"role": "user", "content": "Design a rate limiting system for a REST API"}
    ],
    "temperature": 0.4,
    "max_tokens": 4096
  }'

Budget-Friendly with Nova

PythonTypeScriptcURL

python

import requests

url = "https://apis.fotohub.app/v1/ai/chat/bedrock"
headers = {
    "Authorization": "Bearer fh_live_your_api_key",
    "Content-Type": "application/json"
}

# Nova Micro — ultra-budget option for simple tasks
# At 0.000525 PLN/1K tokens, process 1M tokens for ~0.53 PLN
response = requests.post(url, json={
    "model": "nova-micro",
    "messages": [
        {"role": "user", "content": "Classify this review as positive/negative: 'Great product, fast shipping!'"}
    ],
    "temperature": 0.1,  # Low temp for classification
    "max_tokens": 10
}, headers=headers)

data = response.json()
print(data["choices"][0]["message"]["content"])  # "positive"
print(f"Cost: {data['billing']['pln_charged']} PLN")  # ~0.000005 PLN

typescript

// Nova Micro — ultra-budget for classification, extraction, simple tasks
const response = await fetch("https://apis.fotohub.app/v1/ai/chat/bedrock", {
  method: "POST",
  headers: {
    "Authorization": "Bearer fh_live_your_api_key",
    "Content-Type": "application/json",
  },
  body: JSON.stringify({
    model: "nova-micro",
    messages: [
      { role: "user", content: "Classify this review as positive/negative: 'Great product, fast shipping!'" }
    ],
    temperature: 0.1,
    max_tokens: 10,
  }),
});

const data = await response.json();
console.log(data.choices[0].message.content); // "positive"
console.log(`Cost: ${data.billing.pln_charged} PLN`);

bash

curl -X POST "https://apis.fotohub.app/v1/ai/chat/bedrock" \
  -H "Authorization: Bearer fh_live_your_api_key" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "nova-micro",
    "messages": [
      {"role": "user", "content": "Classify: positive or negative? Great product!"}
    ],
    "temperature": 0.1,
    "max_tokens": 10
  }'

Error Responses

400 -- Invalid Request

json

{
  "error": {
    "type": "invalid_request",
    "message": "messages array is required and must contain at least one message",
    "code": "missing_messages"
  }
}

402 -- Insufficient Credits

json

{
  "error": {
    "type": "billing_error",
    "message": "Insufficient credits. Required: 2, available: 0",
    "code": "insufficient_credits",
    "credits_required": 2,
    "credits_available": 0
  }
}

429 -- Rate Limited

json

{
  "error": {
    "type": "rate_limit",
    "message": "Rate limit exceeded. Please retry after 2 seconds.",
    "code": "rate_limited",
    "retry_after": 2
  }
}

Rate Limits

Rate Limits by Tier

Free tier: 10 requests/minute, 100 requests/day
Pro tier: 60 requests/minute, 5,000 requests/day
Enterprise: Custom limits -- contact support
Streaming requests count as 1 request regardless of duration

Chat / LLM ​

OpenAI-Compatible Chat Completions ​

Parameters ​

Available Models ​

FOTOhub ​

Anthropic Claude ​

OpenAI ​

xAI Grok ​

DeepSeek ​

Alibaba Qwen ​

Moonshot Kimi ​

Amazon Nova ​

Mistral AI ​

NVIDIA ​

OpenAI (Bedrock) ​

MiniMax ​

Z.AI ​

Response ​

Bedrock-Native Chat (Token-Based Billing) ​

Parameters ​

Bedrock Models (Token-Based Pricing) ​

Anthropic Claude ​

Amazon Nova ​

DeepSeek (Bedrock) ​

Mistral AI (Bedrock) ​

Qwen (Bedrock) ​

NVIDIA (Bedrock) ​

Moonshot (Bedrock) ​

OpenAI (Bedrock) ​

MiniMax (Bedrock) ​

Z.AI (Bedrock) ​

Response ​

Streaming ​

Stream Chunk Format ​

Streaming Example -- SSE Parsing ​

Token Counting & Billing ​

Credit-Based (OpenAI-Compatible Endpoint) ​

Token-Based (Bedrock Endpoint) ​

Code Examples ​

Basic Chat Completion ​

Multi-Turn Conversation ​

Bedrock with System Prompt ​

Budget-Friendly with Nova ​

Error Responses ​

400 -- Invalid Request ​

402 -- Insufficient Credits ​

429 -- Rate Limited ​

Rate Limits ​

Chat / LLM

OpenAI-Compatible Chat Completions

Parameters

Available Models

FOTOhub

Anthropic Claude

OpenAI

xAI Grok

DeepSeek

Alibaba Qwen

Moonshot Kimi

Amazon Nova

Mistral AI

NVIDIA

OpenAI (Bedrock)

MiniMax

Z.AI

Response

Bedrock-Native Chat (Token-Based Billing)

Parameters

Bedrock Models (Token-Based Pricing)

Anthropic Claude

Amazon Nova

DeepSeek (Bedrock)

Mistral AI (Bedrock)

Qwen (Bedrock)

NVIDIA (Bedrock)

Moonshot (Bedrock)

OpenAI (Bedrock)

MiniMax (Bedrock)

Z.AI (Bedrock)

Response

Streaming

Stream Chunk Format

Streaming Example -- SSE Parsing

Token Counting & Billing

Credit-Based (OpenAI-Compatible Endpoint)

Token-Based (Bedrock Endpoint)

Code Examples

Basic Chat Completion

Multi-Turn Conversation

Bedrock with System Prompt

Budget-Friendly with Nova

Error Responses

400 -- Invalid Request

402 -- Insufficient Credits

429 -- Rate Limited

Rate Limits