Most AI inference bills are built on tokens. You estimate prompt length, guess completion size, and hope your monthly usage stays inside a budget that balloons every time a user pastes a long document. For teams running long-context retrieval, agent loops, or unpredictable user inputs, token-based pricing turns forecasting into a guessing game. Request-based pricing removes that variable. You pay one flat cost per API call, regardless of whether the prompt is ten tokens or ten thousand.
The Token Problem in Production Workloads
Token-based providers charge for both input and output tokens. A single long-context request carrying a 100,000-token prompt can cost as much as dozens of short queries. Agentic workflows compound the issue: each tool call, reflection step, and retry loop adds input tokens to the bill. The pricing curve rewards brevity and punishes complexity, which creates tension for applications that need rich context to perform well.
How Request-Based Pricing Works
Under a request-based model, the unit of cost is the API call itself, not the volume of tokens inside it. The provider absorbs variance in prompt length and completion size. This shifts operational risk away from the developer and simplifies capacity planning. If your application sends one thousand requests today, you already know exactly what you spent, with no surprise overages from verbose outputs.
Workloads That Benefit Most
Three patterns consistently see better economics under request-based pricing.
- Long-context ingestion. Summarizing legal documents, analyzing multi-file codebases, or querying large knowledge bases with extensive system prompts.
- Agentic workflows. Autonomous agents that call tools, evaluate intermediate results, and iterate. Each loop is billed as one request, not as a growing stack of input tokens.
- Unpredictable user traffic. When end users paste arbitrary content, prompt length varies wildly. Request pricing caps the worst-case cost per interaction.
Oxlo.ai and Request-Based Pricing
Oxlo.ai is a developer-first AI inference platform built around a flat per-request pricing model. Unlike token-based providers such as Together AI, Fireworks AI, OpenRouter, Replicate, or Anyscale, cost at Oxlo.ai does not scale with input length. For long-context and agentic workloads, this architecture can be significantly cheaper.
The platform hosts 45+ open-source and proprietary models across seven categories, including LLMs and chat models like Qwen 3 32B, Llama 3.3 70B, DeepSeek R1 671B MoE, Kimi K2.6, and GLM 5; code models like Qwen 3 Coder 30B; vision models like Gemma 3 27B; and endpoints for image generation, audio transcription, text-to-speech, embeddings, and object detection. All endpoints are fully OpenAI SDK compatible, and popular models start with no cold starts.
Pricing tiers are straightforward. The Free plan offers 60 requests per day across 16+ models with a 7-day full-access trial. Pro provides 1,000 requests per day for $80 per month. Premium offers 5,000 requests per day for $350 per month with priority queue access. Enterprise plans include custom unlimited volume, dedicated GPUs, and guaranteed 30% off your current provider. For exact per-request rates, see the Oxlo.ai pricing page.
A Practical Cost Comparison Framework
To evaluate whether request-based pricing fits your workload, calculate your effective cost per user session. Count the requests in a typical workflow, multiply by the per-request rate, and compare that total to your last token-based bill. Because Oxlo.ai request-based pricing can be 10-100x cheaper than token-based alternatives for long-context workloads, the crossover point often arrives quickly once prompts exceed a few thousand tokens.
SDK Integration
Oxlo.ai exposes a fully OpenAI-compatible API at https://api.oxlo.ai/v1. Switching from another provider usually requires changing only the base URL and API key.
import openai
client = openai.OpenAI(
base_url="https://api.oxlo.ai/v1",
api_key="your-oxlo.ai-api-key"
)
response = client.chat.completions.create(
model="llama-3.3-70b",
messages=[
{"role": "system", "content": "You are a helpful assistant."},
{"role": "user", "
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