Now in beta · India-first · OpenAI + Anthropic + Gemini

The AI proxy that cuts
your API bill by up to 70%

VectorFlo sits between your app and any LLM provider. It automatically caches repeated prompts, routes simple queries to cheaper models, and gives you a real-time dashboard of every rupee saved — with one line of code change.

⚡ Semantic caching — serve repeated prompts for free
⇄ Smart routing — gpt-4o-mini for simple, gpt-4o for complex
📊 Live dashboard — see savings per feature, per model, per day
Python
Node.js
curl
# Before
client = OpenAI(api_key="sk-...")

# After — one line change
client = OpenAI(
  api_key="vf-your-key",
  base_url="https://api.vectorflo.co/v1"
)
# Optional: tag requests by feature
extra_body={"feature_tag": "support-bot"}
01
⚡
Semantic caching
Every prompt is stored as a vector embedding. When a similar prompt arrives — even with different wording — VectorFlo returns the cached response instantly. No OpenAI call. No cost.
"How do I get a refund?"HIT → free
"I want my money back"HIT → free
"Can I return my order?"HIT → free
↓ up to 70% fewer API calls
02
⇄
Smart model routing
VectorFlo scores every query for complexity in under 1ms. Simple questions go to gpt-4o-mini or claude-haiku — 16x cheaper. Complex ones stay on the full model. Quality is never sacrificed.
"What are your hours?"→ gpt-4o-mini
"Write a quicksort in Python"→ gpt-4o
"Analyze this legal clause"→ claude-opus
↓ up to 95% on routed queries
03
📊
Real-time dashboard
Every request is logged with full cost breakdown. See what you actually spent vs what you would have paid without VectorFlo. Broken down by model, by feature, by day.
Spent this month$41.20
Without VectorFlo$96.50
You saved$55.30 (57%)
↑ full spend visibility
// works with
OpenAI
Anthropic Claude
Google Gemini
Azure OpenAI
70%
Average cost reduction
<20ms
Proxy overhead added
1 line
Integration effort
// see it in action
Same requests. Fraction of the cost.
Watch a real support bot session — with and without VectorFlo.
// without VectorFlo
direct OpenAI
$0.000000
total API calls0
cost per request$0.000000
// with VectorFlo
optimising
$0.000000
total saved$0.000000
savings rate0%
// smart model routing
Right model for every query. Automatically.
VectorFlo analyses each request in real time and routes it to the cheapest model capable of handling it well.
01 / analyse
Query arrives
VectorFlo reads the prompt in under 1ms. No extra API call, zero latency to your users.
→
02 / score
Complexity scored
Our engine assigns a complexity score based on task type, context, and reasoning depth required.
→
03 / route
Cheapest capable model
Simple queries go to nano models. Complex ones stay on full models. Quality is never sacrificed.
// simple → routed to cheap model (95% savings)
"what are your business hours?"
simple
mini
"translate hello to Hindi"
simple
mini
"classify as positive or negative"
simple
mini
"summarise this email in 2 lines"
simple
mini
// complex → stays on full model (full quality)
"write a Python class for a BST"
complex
gpt-4o
"analyze this contract for risks"
complex
claude-opus
"compare microservices vs monolith"
complex
gpt-4o
"debug this error in my React app"
complex
gpt-4o
aggressive
Maximum savings. Routes everything possible to cheaper models.
↓ up to 95% savings
balanced default
Smart tradeoff. Routes simple down, keeps complex on full models.
↓ up to 70% savings
quality
Never routes down. Always uses the model you requested.
↓ native cache only
// feature tagging
Know exactly where your AI budget goes.
Tag each request with a feature name. See cost and savings broken down per product feature in real time.
// add one field to your request
client.chat.completions.create(
  model="gpt-4o",
  messages=[...],
  extra_body={
    "feature_tag": "support-bot"
  }
)
Tag any feature — support-bot, search, onboarding, content-gen. Works with any provider and any model.
// dashboard breakdown by feature
support-bot
$0.0421
search
$0.0105
onboarding
$0.0058
content-gen
$0.0035
Your support bot is 72% of AI spend. Semantic caching saved you $0.029 this week on those requests alone.
// everything included
Built for production from day one.
Every feature you need to run AI reliably and cheaply.
⚡
Semantic caching
Similar prompts served from cache instantly. "How do I get a refund?" and "I want my money back" both hit cache after the first call.
↓ up to 70% fewer API calls
⇄
Smart model routing
Automatically routes queries to the cheapest capable model. Configure per feature — aggressive savings for bots, quality mode for legal analysis.
↓ up to 95% on routed queries
📊
Real-time dashboard
See what you spent, what you saved, cache hit rate, and cost per feature — updated after every single request.
↑ full spend visibility
🛡
Automatic fallback
If OpenAI returns an error or times out, VectorFlo retries with a cheaper model automatically. Your app never shows an error to users.
↑ 99.9% uptime
🔀
Multi-provider gateway
OpenAI, Anthropic Claude, Google Gemini, Azure OpenAI — all behind one endpoint. Switch providers without touching your code.
→ one key for all providers
🇮🇳
India-first infra
AWS Mumbai servers. Data never leaves India. DPDP compliant. USD billing. WhatsApp support for all plans.
→ built for Indian startups

See VectorFlo in action

Tell us about your setup and we'll show you exactly how much you'd save — with real numbers from your usage.

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