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Vectis

Predictable AI margins for high-scale enterprise engineering teams

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Engineering leaders watch software margins evaporate as multi-agent systems trigger heavy foundation models for trivial downstream prompts.

Vectis acts as an intelligent orchestration gateway between customer-facing applications and global model providers. It classifies task complexity in real time, routing routine micro-tasks to specialized models while preserving high-tier compute solely for deep reasoning. Engineering teams instantly gain per-feature cost governance and cut inference expenditure by over 50% without altering application logic.

Vectis provides the financial and operational control plane required for the autonomous software era.

COMMENTS — Community Discussion
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Problem
  • When I scale customer-facing agentic workflows, I want to deliver responsive intelligence, but runaway inference bills drain up to $60k monthly on redundant reasoning compute.
  • Why Now: Agent architectures and reasoning loops generate 10x more prompt tokens per user action than standard chat apps, compounding API overhead exponentially.
  • When I review cloud unit economics with the executive board, I want to explain per-feature profitability, but unified API keys hide model cost attribution across internal microservices.
  • When I push multi-step autonomous workflows to production, I want sub-second latency, but default routing to heavy frontier models introduces 4s delays that spike user churn.
  • Existing Alternatives: Hardcoded fallback logic in application repositories, ad-hoc custom proxy servers, or unmonitored direct foundation model vendor endpoints.
Solution
  • High-Level Concept: Cloudflare for generative model orchestration and autonomous unit economics.
  • Sub-50ms Semantic Routing: Instantly evaluates prompt complexity and routes routine tokens to lightweight open weights while preserving frontier models for deep reasoning.
  • Dynamic Budget & SLA Guardrails: Automatically throttles over-budget agent loops and reroutes during vendor outages without dropping active user sessions.
  • Per-Feature Unit Margin Analytics: Real-time telemetry breaking down dollar spend, latency, and quality benchmarks by tenant, endpoint, and product feature.
Distribution
  • Early Adopters: Series A to Series C engineering teams and AI product leads spending >$15k monthly on foundation model API calls.
  • Open-source CLI diagnostic tool that scans historical API logs and generates an instant Token Waste Audit report in under 3 minutes.
  • Direct technical workshops targeted at engineering leads within autonomous agent and AI framework communities.
  • Strategic distribution partnerships with venture capital operations teams seeking to optimize cloud gross margins across portfolio startups.
Pricing
  • Value Ladder: Free tier up to 500k routed tokens/mo; Pro at $490/mo for up to 50M tokens with custom SLA rules; Enterprise custom tier starting at $2.5k/mo with VPC peering and dedicated audit logs.
  • One Metric That Matters (OMTM): Total monthly routed token volume achieving verified cost reductions above 40%.
  • Market Sizing: 12k growth-stage software companies deploying LLM agents globally (SAM = $420M); capturing 300 mid-market engineering teams in Year 1 yields $1.8M ARR (SOM).
Scale Costs
  • Global edge orchestration network with multi-region latency clustering maintained strictly below 15ms.
  • Continuous evaluation dataset maintenance and proprietary lightweight classifier calibration across newly released open weights.
  • SOC2 Type II, HIPAA, and ISO 27001 data residency compliance infrastructure with zero prompt retention pipelines.
Expert Opinions
Avg: 9.4
  • VP of Infrastructure Engineering at $2B ARR Enterprise SaaS
    9.5
  • Founding Partner at DeepTech Venture Fund
    9.2
  • Lead AI Architect & Open Source Maintainer
    9.4
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