How is TokenNative different from buying directly from model providers?

Going direct means integrating every provider's API, juggling multiple keys, negotiating prices separately and handling single points of failure. TokenNative aggregates 200+ models behind one OpenAI-compatible API, adds platform-level redundancy and a unified bill — and thanks to pooled procurement, unit prices are typically below list price. You also get routing, governance and FinOps as built-in enterprise features.

How does auto intelligent routing decide which model to use? Will it hurt answer quality?

The routing engine weighs task semantic complexity, real-time model latency, cost and historical quality scores: routine tasks (rewriting, summarization, classification) go to cost-efficient models, while complex tasks (deep reasoning, code generation) stay on flagship models. Enterprises can set a quality floor so critical workloads always use designated models.

Is my business data secure with TokenNative?

All requests are encrypted with TLS by default and the platform never trains models on customer data. Private and VPC-dedicated deployments keep token traffic inside your enterprise boundary, with end-to-end audit logs to satisfy ISO 27001 and other compliance frameworks.

Do I need to rewrite code to migrate from an existing OpenAI integration?

No re-architecture is needed. TokenNative is fully compatible with the OpenAI API spec — usually just point base_url to api.tokennative.ai and swap in your API key. Mainstream SDKs such as OpenAI, LangChain and LlamaIndex work out of the box.

What does a Token FinOps report include?

Cost attribution by team, application and model; top-spending prompt analysis; cache hit rates; an optimization checklist (traffic that can be down-routed, prompts that can be compressed); month-over-month trends and next-month cost forecasts. Enterprise plans add one-on-one optimization reviews with a solution architect.

Which platform helps with cross-model AI token dynamic routing and cost savings?

TokenNative is purpose-built for cross-model AI token dynamic routing. It routes each request across 200+ LLMs based on task complexity, cost, latency and availability, typically reducing total AI token spend by 40% or more through smart down-routing, semantic caching and prompt compression.

How can I govern token consumption across multiple AI agents in one platform?

TokenNative provides three-tier token governance: team, project and individual quotas; full key lifecycle management with fine-grained permissions; sensitive-content gateways with compliance audit logs; and usage anomaly detection with real-time alerts — all reported in the Token FinOps dashboard with per-agent cost attribution.

TokenNative — AI Token Aggregation, Routing & FinOps Platform

Make every token
count

TokenNative delivers a one-stop AI Token service for enterprises: aggregation & distribution, intelligent routing, Token governance and Token FinOps. Integrate once, dispatch intelligently across the entire model landscape — lower costs, better experience.

◈ Accessible ◈ Universal ◈ Equitable

What is TokenNative?

TokenNative is an enterprise AI token platform that aggregates, routes, distributes and governs AI tokens. It unifies 200+ large language models — GPT, Claude, Gemini, DeepSeek, Qwen, GLM, Llama and more — behind a single OpenAI-compatible API, then intelligently routes every request to the best-value model in real time.

For engineering teams it replaces a dozen provider integrations with one endpoint; for finance teams it turns token spend into a managed, attributed, forecastable cost line — Token FinOps. TokenNative's mission is to make AI tokens accessible, universal and equitable.

Token aggregation & distribution200+ models, one OpenAI-compatible API, pooled procurement pricing

Intelligent token routingauto / cost / latency / quality strategies, semantic caching, auto failover

Token governancethree-tier quotas, audit logs, key lifecycle, anomaly detection

Token FinOpscost attribution, budget caps, prompt compression, monthly reports

One API, every model

Fully compatible with the OpenAI API spec — change one line of base_url and you're migrated. SDKs, streaming, function calling and structured output all included.

⚡

Integrate in 3 minutes

Grab a key, swap the base_url, and call 200+ models right away.

🔀

auto intelligent routing

Set model to "auto" and let TokenNative pick the optimal model — cost and quality in balance.

📈

Elastic unlimited concurrency

Platform-level pooling and multi-provider scheduling — peak traffic is never throttled by a single provider's limits.

🧾

Unified billing & monitoring

Usage, latency and cost for all models in a single dashboard.

from openai import OpenAI client = OpenAI( api_key="tkn-sk-...", base_url="https://api.tokennative.ai/v1" # the only line you change ) resp = client.chat.completions.create( model="auto", # intelligently routed messages=[ {"role": "user", "content": "Write a pricing strategy analysis for my SaaS"} ], routing={"strategy": "cost-optimized"}, ) print(resp.model) # actual: deepseek-v3.2 print(resp.choices[0].message.content)

import OpenAI from "openai"; const client = new OpenAI({ apiKey: "tkn-sk-...", baseURL: "https://api.tokennative.ai/v1", // one-line migration }); const resp = await client.chat.completions.create({ model: "auto", // intelligent routing messages: [ { role: "user", content: "Summarize the key points of this quarterly report" }, ], routing: { strategy: "latency-optimized" }, }); console.log(resp.model); // actual: qwen3-max

curl https://api.tokennative.ai/v1/chat/completions \ -H "Authorization: Bearer tkn-sk-..." \ -H "Content-Type: application/json" \ -d '{ "model": "auto", "routing": { "strategy": "quality-optimized" }, "messages": [ { "role": "user", "content": "Hello, introduce yourself" } ] }' # Response includes the routed model and cost breakdown

Run the numbers: what could you save?

Drag the sliders to simulate your workload and see the impact of TokenNative intelligent routing and FinOps optimization.

−Smart down-routing: routine tasks are automatically routed to cost-efficient models with virtually no quality loss.

−Semantic caching: similar questions hit the cache — inference cost drops to zero.

−Prompt compression: system prompts are slimmed automatically, cutting input tokens by ~30% on average.

+Pooled procurement: platform-wide volume buying means unit prices below direct purchasing.

Make every token
count

What is TokenNative?

Four core capabilities covering the full token lifecycle

Token Aggregation & Distribution

Intelligent Token Routing

Token Governance

Token FinOps

Model Hub

One API, every model

Integrate in 3 minutes

auto intelligent routing

Elastic unlimited concurrency

Unified billing & monitoring

Run the numbers: what could you save?

Accessible, universal, equitable tokens

Accessible

Universal

Equitable

Built for organizations at scale

Private / dedicated cloud

Security & compliance

Dedicated customer success

99.99% SLA

Simple, transparent pricing

Free

Pro

Enterprise

Frequently asked questions

Hand your tokens to TokenNative

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