Vidman AI Adaptive — enterprise grade inference
One call. The right answer, at the right price.
Vidman AI Adaptive reads each request and answers it at the depth the work deserves — light prompts stay light, hard problems get flagship-grade answers. A single API call, and your AI spend can fall by more than half.
The problem
Most prompts get billed at rates they never required
The strongest model becomes the default for every task
It feels safe — and it quietly bills routine prompts at flagship rates they never required.
No single model fits every kind of work
A quick classification and a multi-step reasoning chain share nothing. Flagship pricing on both burns budget; bargain pricing on both burns quality.
Any one provider can go down
Pin a single model id into your product and you have built a point of failure you will meet for the first time mid-outage.
How it works
One request in, the best-fit answer back
Your call lands
The same OpenAI-compatible schema you already run — no SDK swap, no new client library.
The work sets the level
Answers come back at the depth the prompt demands — flagship strength when the task needs it, budget speed when it does not.
The reply comes back
Down the same endpoint you called, failover already inside — an upstream hiccup stays their problem, not your outage.
Who it is for
One endpoint, a different win for every seat
For engineers
One integration, nothing to babysit. Aim your existing OpenAI client here and stop there — a single id covers every kind of task.
For product teams
A quality floor where the work is hard, without flagship prices where it is easy. Light prompts stay cheap; hard ones still get strong, accurate answers — automatically.
For the business
The invoice tracks the work each answer actually took — the band below, with nothing layered on top of it. Every model Adaptive can reach is served from inside India.
Pricing
The band is the savings, made visible
| Vidman AI Adaptive | Rate, per 1M tokens |
|---|---|
| Context window | 1M tokens |
| Input | ₹41.60 – ₹118.19 |
| Cached input | ₹13.24 |
| Output | ₹124.81 – ₹373.47 |
The price moves because the work moves — and the invoice only ever counts what the answer took. A light prompt bills near the bottom of the band; a demanding one bills near the top. Nothing gets layered on top of the rate itself.
The ceiling is printed, not hidden. No request can bill above ₹118.19 input / ₹373.47 output per 1M tokens. Plan against the ceiling — every request that lands cheaper is savings nobody had to negotiate for.
All figures in ₹ per 1M tokens (₹94.55 per USD, committed rate 2026-09-07), current as of 29 August 2026. Each request's rate is itemised on the invoice — you always see what an answer cost.
An honest fork
Pin a model, or let Adaptive read each task
Pin a model when
- Every prompt in the workload looks alike — one decision covers them all.
- You are evaluating or benchmarking one named model.
- A contract or budget locks you to a single guaranteed per-token rate.
Use Adaptive when
- The workload mixes easy prompts with multi-step reasoning.
- The right price-quality point shifts from request to request.
- You want failover as a default, not an afterthought.
Integration
Two lines to switch
OpenAI-compatible — the client you already run, the code you already wrote.
from openai import OpenAI
client = OpenAI(
base_url="https://api.vidman.ai/v1", # was api.openai.com
api_key="sk-vidman-...",
)
resp = client.chat.completions.create(
model="bios-adaptive", # was a pinned model id
messages=[{"role": "user", "content": "Classify this ticket"}],
)Prefer a pinned model? See models & pricing