Technical support teams face a growing mismatch between ticket volume and available engineering hours. Large language models offer a path out, but production deployments often stall when costs scale linearly with every extra line of documentation, log file, or conversation history fed into the prompt. Oxlo.ai removes that friction with request-based pricing: one flat cost per API call regardless of how much context you include. For support workflows that naturally involve long documents and multi-turn threads, this changes the economics from prohibitive to practical.
The Pricing Problem with Long-Context Support
Token-based inference platforms charge by the slice. When your knowledge base article is 4,000 tokens, the user description is 2,000 tokens, and the system prompt is 1,500 tokens, the meter is running before the model generates a single troubleshooting step. For agentic support bots that iterate through tool calls and maintain conversation state, costs compound quickly.
Oxlo.ai uses a flat per-request model. Whether you send a terse one-line question or a 50,000-token log dump, the price is the same. For support teams, this means you can pass full ticket threads, internal wiki pages, and error traces into a single request without budget shock. See the details at https://oxlo.ai/pricing.
Retrieval and Generation with Oxlo.ai
Most production support bots rely on retrieval-augmented generation, or RAG. The pattern is straightforward: embed your docs, search for relevant passages, then inject them into a chat prompt. Oxlo.ai supports the full pipeline through a single OpenAI-compatible endpoint.
For embeddings, you can call BGE-Large or E5-Large. For generation, Llama 3.3 70B handles general-purpose troubleshooting, while Qwen 3 32B excels at multilingual agent workflows. If a ticket requires reasoning across complex system logs, DeepSeek V4 Flash offers a 1 million token context window, and Kimi K2.6 brings advanced reasoning and coding ability to agentic tasks.
import openai
client = openai.OpenAI(
base_url="https://api.oxlo.ai/v1",
api_key="YOUR_OXLO_API_KEY"
)
# Embed a knowledge base article
embedding = client.embeddings.create(
model="bge-large",
input="How to reset a SAML configuration..."
)
# Generate a response with full context
response = client.chat.completions.create(
model="llama-3.3-70b",
messages=[
{"role": "system", "content": "You are a senior support engineer."},
{"role": "user", "content": f"Ticket: {ticket_text}\n\nRelevant docs: {retrieved_docs}"}
],
stream=True
)
for chunk in response:
print(chunk.choices[0].delta.content or "", end="")
When RAG Is Not Enough
Sometimes retrieval strips away too much nuance. A customer might paste a 10,000-line stack trace, or you might need to compare a current ticket against twenty related conversations. Token-based providers penalize this behavior. On Oxlo.ai, it is still one request.
Models like DeepSeek V4 Flash and Kimi K2.6 are built for this. DeepSeek V4 Flash is an efficient MoE model with a 1 million token context window, which lets you dump entire log files and configuration snapshots into the prompt. Kimi K2.6 offers 131K context alongside vision and coding strengths, so it can analyze screenshots of error dialogs while reading surrounding documentation. GLM 5, a 744B parameter MoE model, targets long-horizon agentic tasks where the support bot must plan across many tool calls.
Vision and Code-Specific Tasks
Technical support is not only text. Users attach screenshots of error messages, and developers share snippets of YAML or Python. Oxlo.ai offers models for both modalities.
For vision, Gemma 3 27B and Kimi VL A3B accept image inputs through the chat completions endpoint. You can pass a base64-encoded screenshot alongside the ticket description and ask the model to extract the error code or UI state.
For code-heavy tickets, Qwen 3 Coder 30B and Oxlo.ai Coder Fast specialize in software engineering contexts. If the issue requires step-by-step reasoning, DeepSeek R1 671B MoE or Kimi K2 Thinking expose chain-of-thought reasoning that traces through complex dependencies before answering.
Building Agentic Workflows
Advanced support bots do not just answer questions. They query ticket status, create internal notes, and escalate based on sentiment. These agentic patterns require function calling and reliable JSON output.
Oxlo.ai supports both features on compatible models. You can define tools for CRM lookups or runbook execution, then let the model decide when to invoke them. For guardrails, JSON mode constrains the output to a predictable schema, which makes it easier to post-process responses in your ticketing pipeline.
tools = [
{
"type": "function",
"function": {
"name": "query_ticket_status",
"description": "Look up the current status of a support ticket",
"parameters": {
"type": "object",
"properties": {
"ticket_id": {"type": "string"}
},
"required": ["ticket_id"]
}
}
}
]
response = client.chat.completions.create(
model="qwen-3-32b",
messages=[{"role": "user", "content": "Why is ticket 4812 still open?"}],
tools=tools,
tool_choice="auto"
)
Getting Started
Because Oxlo.ai is fully OpenAI-compatible, migrating an existing support prototype requires changing two lines: the base URL and the API key. There are no cold starts on popular models, so the first request after a quiet period returns at full speed. This matters for support bots that sit idle overnight and must respond instantly when a UTC+8 user opens a ticket.
Start with the Free plan to test the 16+ available models, including DeepSeek V3.2 on the free tier. When you move to production, the per-request pricing model keeps long-context tickets affordable. Review the full model catalog and pricing structure at https://oxlo.ai/pricing.
Conclusion
LLMs can transform tech support from a cost center into a self-service engine, but only if the infrastructure bill does not grow faster than the headcount savings. By switching from token-based metering to flat per-request pricing, Oxlo.ai lets engineering teams feed models the full context they need to be accurate. With 45+ models spanning text, vision, code, and audio, and full OpenAI SDK compatibility, it is a practical backbone for modern support automation.
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