Intelligent model routing and batching that picks the right LLM for every task, without sacrificing quality or speed
Teams building with large language models face a relentless cost pressure. GPT-4 costs 15x more per token than smaller models. Most teams waste millions annually by routing every request to their most powerful LLM, even when a smaller, faster, cheaper model would work just as well. Your product roadmap, your data pipeline, your customer support system: they all get routed to the same expensive endpoint. The result is bloated infrastructure spend and slower inference across the board.
Architect Loop solves this by orchestrating your LLM requests intelligently. Every task gets routed to the optimal model based on complexity, latency requirements, and cost. Simple summarization goes to a small model at 1/15th the cost. Complex reasoning still reaches GPT-4. The same codebase, one unified orchestration layer, and 80% savings on token spend.
Analyze the incoming request to understand complexity, latency constraints, and quality requirements without extra latency.
Select the optimal model from your fleet (Claude 3 Haiku for simple tasks, Sonnet for medium, Opus for complex reasoning) based on cost and performance tradeoffs you define.
Group compatible requests for batch processing where latency permits, further reducing per-token costs by 20-40%.
Track quality metrics per route. If a cheaper model starts underperforming, automatically escalate future similar requests to a higher tier.
Results from production deployments at 15 companies processing 2M+ LLM requests monthly. Integration takes under 5 minutes via a drop-in Python SDK or REST API.
Architect Loop runs as a lightweight proxy between your application and your LLM provider. No vendor lock-in. Works with any LLM: Claude, GPT-4, open-source models, your own fine-tuned endpoints. Observable via standard metrics (latency, cost per request, model distribution). Configurable routing rules: define cost thresholds, quality floors, and SLA requirements your way.
Route across Claude, GPT-4, Llama, Mistral, or your own models. No switching costs, no dependency traps.
Per-request cost tracking, model distribution charts, quality metrics by route. Know exactly where your budget goes.
Set your own cost thresholds, quality requirements, and latency SLAs. The system routes according to your rules.
Python SDK or REST API. Change one line in your code (the API endpoint) and Architect Loop handles the rest.
If a cheaper model returns a low-quality response, automatically retry with a higher-tier model. Quality guardrails included.
Async request batching for latency-insensitive workloads. Additional 20-40% savings beyond intelligent routing.
Product teams, research labs, and internal tools teams have deployed Architect Loop to reduce infrastructure costs while keeping quality high and latency predictable. From AI agencies handling customer workloads to in-house platforms serving thousands of internal users, teams report 75-85% token cost reductions within the first month.
Built for production deployments handling 2M+ requests monthly.
Get started in 5 minutes. First month free. No credit card required.
1500+ tokens saved on average per customer in month one.
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