Unlocking LLM Potential in Architecture

Large software systems accrue technical debt faster than most teams can document them. When you need to reverse engineer a monolith, evaluate a migration strategy, or draft an Architecture Decision Record, the bottleneck is rarely creativity. It is context. You need to feed thousands of lines of code, configuration, and requirements into a model and receive a structured, reasoned output. Token-based inference platforms make this prohibitively expensive because costs scale linearly with every line of context you add. Oxlo.ai approaches this differently.

The Context Problem in Architecture

Architectural reasoning is inherently long-context work. A single microservice repository can exceed the context window of older models, and cross-service analysis requires stitching together multiple sources. Whether you are mapping dependencies, detecting circular references, or proposing a refactoring plan, the input prompts are large and the outputs must be precise.

Traditional token-based billing from providers like Together AI, Fireworks AI, OpenRouter, Replicate, or Anyscale means that every additional source file increases your cost. For architecture review, where a prompt might include a full API schema, infrastructure-as-code definitions, and logs, this pricing model discourages thoroughness.

Flat Pricing for Long-Context Analysis

Oxlo.ai uses request-based pricing: one flat cost per API request regardless of prompt length. For architecture workflows, this changes the economics entirely. You can submit an entire system boundary definition, a package dependency graph, and a set of performance benchmarks in a single call without watching the meter run on every token.

This is especially relevant for models that excel at reasoning over large inputs. DeepSeek V4 Flash offers a 1M context window and near state-of-the-art open-source reasoning, making it ideal for ingesting multi-file codebases. GLM 5, a 744B parameter MoE, is built for long-horizon agentic tasks, such as tracing execution paths across distributed systems. With Oxlo.ai, you are not penalized for using that capacity.

Generating Architecture Decision Records

ADRs require consistency. They need a concise context, decision, consequences, and compliance notes. You can automate their generation by combining a structured prompt with a model strong in instruction following.

Llama 3.3 70B serves as a reliable general-purpose flagship for this, while Qwen 3 32B provides strong multilingual reasoning if your architecture spans international teams or legacy documentation in multiple languages. By leveraging JSON mode, you can enforce a strict schema for each ADR section and validate it programmatically.

from openai import OpenAI
import json

client = OpenAI(
    base_url="https://api.oxlo.ai/v1",
    api_key="your_oxlo_api_key"
)

system_prompt = """You are a principal software architect.
Generate an Architecture Decision Record as a JSON object with these exact keys:
title, context, decision, consequences, compliance_notes."""

user_prompt = """Context: We are migrating from a monolithic Django application to a set of FastAPI microservices.
Proposed change: Extract the billing module into an independent service with its own PostgreSQL instance.
Return valid JSON only."""

response = client.chat.completions.create(
    model="llama-3.3-70b",
    messages=[
        {"role": "system", "content": system_prompt},
        {"role": "user", "content": user_prompt}
    ],
    response_format={"type": "json_object"}
)

adr = json.loads(response.choices[0].message.content)
print(json.dumps(adr, indent=2))

Agentic Design Review and Tool Use

Architecture is not a single prompt task. It is iterative. A model might need to query a dependency graph, check a service catalog, or validate a naming convention against existing resources. This requires function calling and multi-turn conversations.

Oxlo.ai supports function calling and tool use across its chat models. You can build an agent that invokes static analysis tools, fetches infrastructure state, and synthesizes findings into a coherent review. Because Oxlo.ai charges per request, an agentic loop that builds a large context window over multiple turns does not incur the escalating token costs you would see on token-based platforms.

Models like DeepSeek R1 671B MoE and Kimi K2.6 are well suited here. DeepSeek R1 delivers deep reasoning for complex coding and design trade-offs, while Kimi K2.6 brings advanced reasoning, agentic coding, and vision capabilities with a 131K context window.

Multimodal Inputs and Vision

Not all architecture is text. System diagrams, whiteboard sketches, and infrastructure flowcharts are common inputs. Oxlo.ai offers vision models such as Gemma 3 27B and Kimi VL A3B that accept image input. You can photograph a whiteboard session and ask the model to generate the corresponding Terraform schema or to identify bottlenecks in the drawn data flow.

SDK Integration and Model Selection

Oxlo.ai is fully OpenAI SDK compatible. If you already have tooling built for OpenAI, migration is a base_url change. There are no cold starts on popular models, so CI pipelines that trigger architectural linting or documentation generation get predictable latency.

The platform hosts over 45 models across 7 categories. For code-specific reasoning, Qwen 3 Coder 30B and DeepSeek Coder provide targeted assistance. For semantic search over documentation, embedding models like BGE-Large and E5-Large let you build retrieval pipelines that feed context into your architecture agents. If you need to transcribe architecture review meetings, Whisper Large v3 and Kokoro 82M are available through the same API.

Plans and Pricing

For teams experimenting, the Free plan offers 60 requests per day across more than 16 models with a 7-day full-access trial. For professional use, Pro provides 1,000 requests per day, and Premium offers 5,000 requests per day with priority queue access. Enterprise plans include dedicated GPUs and a guarantee of 30% savings versus your current provider.

Because Oxlo.ai does not charge by the token, long-context architectural analysis can be 10 to 100 times cheaper than token-based alternatives. See the details at https://oxlo.ai/pricing.

Conclusion

LLMs are becoming essential infrastructure for software architecture, but only if the economics support real-world context sizes. By removing the token tax, Oxlo.ai makes it practical to analyze entire systems, generate rigorous documentation, and run agentic design reviews without budget anxiety. If your architecture work demands long prompts and precise reasoning, Oxlo.ai is built for it.