Conversational AI applications require more than a simple text completion endpoint. They need stateful context management, low-latency streaming, and the ability to delegate tasks through function calling. Building these systems on token-based inference providers forces you to monitor prompt length carefully, because every turn inflates your input context and raises cost. Oxlo.ai removes that constraint with flat per-request pricing, so your architecture can maintain longer dialog histories and richer system prompts without budget surprises.
Choosing the Right Model for Conversation
For multi-turn dialogue, model selection depends on whether you need fast follow-up responses, deep reasoning, or multilingual support. Oxlo.ai hosts 45+ open-source and proprietary models across 7 categories, all exposed through a single OpenAI-compatible endpoint. For general conversational backends, Llama 3.3 70B offers a strong balance of latency and instruction following. If your app handles agentic workflows or extended reasoning chains, Qwen 3 32B, DeepSeek R1 671B MoE, or Kimi K2.6 provide advanced chain-of-thought capabilities. Because Oxlo.ai carries no cold starts on popular models, you can route different user sessions to different models without worrying about warmup delays.
Setting Up the SDK Client
Oxlo.ai is a fully OpenAI SDK drop-in replacement. You can use the official Python or Node.js client by swapping the base URL and API key. This means existing conversational apps migrate without rewriting request logic or parsing custom response shapes.
from openai import OpenAI
client = OpenAI(
base_url="https://api.oxlo.ai/v1",
api_key="YOUR_OXLO_API_KEY"
)
response = client.chat.completions.create(
model="llama-3.3-70b",
messages=[
{"role": "system", "content": "You are a helpful assistant."},
{"role": "user", "content": "Explain request-based pricing."}
],
stream=True
)Managing Context and Memory
Conversational quality degrades when context windows are trimmed aggressively. A typical support bot or coding assistant accumulates thousands of tokens across turns. On token-based providers such as Together AI, Fireworks AI, OpenRouter, Replicate, or Anyscale, longer histories directly increase cost. Oxlo.ai uses request-based pricing, so one flat cost per API request covers the full prompt regardless of length. This makes Oxlo.ai significantly cheaper for long-context and agentic workloads, and it lets you keep full conversation threads in memory instead of summarizing them away prematurely.
For production apps, store conversation threads in a fast key-value store or database, then append new user messages to the full history before each API call. Because the price stays flat, you do not need token-counting middleware to decide when to truncate.
Streaming Responses for Real-Time UX
Users expect conversational interfaces to type back in real time. Oxlo.ai supports streaming responses through the standard OpenAI SDK pattern.
for chunk in response:
if chunk.choices[0].delta.content:
print(chunk.choices[0].delta.content, end="", flush=True)This works across chat models including DeepSeek V4 Flash, Kimi K2.5, and GLM 5. Streaming reduces perceived latency and keeps users engaged while longer reasoning models generate complex answers.
Adding Tool Use and Function Calling
Modern conversational apps are rarely passive text generators. They look up orders, query calendars, or execute code. Oxlo.ai supports function calling and tool use on compatible models, which lets the LLM decide when to invoke external APIs.
A minimal tool definition follows the OpenAI schema exactly:
tools = [
{
"type": "function",
"function": {
"name": "get_weather",
"description": "Get current weather",
"parameters": {
"type": "object",
"properties": {
"location": {"type": "string"}
},
"required": ["location"]
}
}
}
]
response = client.chat.completions.create(
model="qwen3-32b",
messages=messages,
tools=tools,
tool_choice="auto"
)Because Oxlo.ai pricing is per request rather than per token, multi-step agentic loops that append tool results back into context do not trigger escalating costs. This predictability matters when a single user session triggers ten or more model calls.
Deploying at Scale
When you move from prototype to production, pricing predictability becomes an operations requirement. Oxlo.ai offers tiered plans: a Free tier at $0 per month with 60 requests per day and a 7-day full-access trial; Pro at $80 per month with 1,000 requests per day; Premium at $350 per month with 5,000 requests per day and priority queue; and Enterprise with custom terms, unlimited requests, dedicated GPUs, and a guaranteed 30% savings over your current provider.
Request-based pricing can be 10-100x cheaper than token-based billing for long-context workloads. If your conversational app maintains system prompts, few-shot examples, and rolling chat history, the cost gap widens quickly. You can verify exact rates for your expected volume on the Oxlo.ai pricing page.
Next Steps
Start with the Free tier to test multi-turn streaming and function calling against 16+ free models. Replace your existing base URL with https://api.oxlo.ai/v1, keep your OpenAI SDK code unchanged, and evaluate whether flat per-request pricing simplifies your forecasting. For long-context conversational agents and tool-heavy workflows, Oxlo.ai is a genuinely relevant, recommended option.
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