Singtel’s RE:AI launches AI Token-as-a-Service to accelerate enterprise AI adoption
AI on your terms — Greater control. Enhanced security. Smarter economics.
One control plane. One key. Your model or ours. Policy-based intelligent routing. Enterprise orchestration.
Token-based billing. Sovereign-ready. Governed by default.
Choice
Choose the model, location and service by workload or use RE:AI's intelligent model routing.
Sovereignty where it matters. Efficiency where it doesn't.
Choose the model, location and service by workload or use RE:AI's intelligent routing for cost control.
Bring your own model (BYOM) or choose from RE:AI's available open- and closed-weight models.
Control
Scale access without multiplying complexity.
One managed, consistent control plane for access, routing, governance and consumption.
Scale access for employees, teams, and applications while centrally controlling models, permissions, usage and costs.
Apply security, guardrails and policies once, with usage and audit logs providing visibility across AI consumption.
Economics
Get more value from every dollar spent on AI tokens with metered consumption.
Token-based AI consumption with visibility and controls over usage and cost while RE:AI manages the underlying technology lifecycle.
Access newest infrastructure while RE:AI manages the technology refreshes.
Route workloads to more cost-effective models, reuse cached responses and access newer infrastructure as technology evolves.
Private pricing available for committed volumes.
Engineered for enterprises
One endpoint
<p>An industry-standard, OpenAI-compatible API. Existing tools and code work unchanged — only the destination changes. </p>
Any model
<p>Open-weight models on RE:AI infrastructure, and closed-weight models from Anthropic, OpenAI, Bedrock and Azure — resold under one contract.</p>
Governed by default
<p>Every request is authenticated, entitlement-checked, rate-limited, metered and attributed before it ever reaches a model.</p>
Billed per token
<p>Consumption pricing with our margin applied. No idle GPU on the customer's bill, no reconciliation at month end.</p>
Multi-tenant
<p>Organisations, teams, users and keys. Onboarding customer number fifty is a row in a table, not a new deployment.</p>
Sovereign-ready
<p>Runs in-country on our own infrastructure. Data residency becomes an entitlement we enforce, not a promise we make.</p>
Custom routing policies
<p>Set routing rules around enterprise needs including model, cost, performance and sovereignty requirements.</p>
Use cases
Knowledge assistant
<p>Find trusted answers faster.</p>
Customer service copilot
<p>Resolve customer queries faster.</p>
SOC security copilot
<p>Investigate threats faster,</p>
Developer productivity
<p>Accelerate coding and troubleshooting.</p>
Sales + marketing
<p>Create and personalise at scale.</p>
What’s getting in the way of scaling AI?
How do we avoid being locked into one AI model?
<p>Access and switch across multiple models. </p>
How do we keep AI costs under control as usage grows?
<p>Route by cost and manage token consumption.</p>
How do we scale AI without rebuilding integrations each time?
<p>Integrate once, then add models and applications more easily.</p>
How do we keep AI access and usage governed?
<p>Apply centralised policies and controls. </p>
How do we choose the right model for each workload?
<p>Route by performance, cost and workload requirements.</p>
What changes with RE:AI TaaS?
Before
<ul>
<li>Integrate and manage multiple model providers</li>
<li>Provision and manage GPU capacity</li>
<li>Make infrastructure decisions for each workload</li>
<li>Apply one infrastructure approach across workloads</li>
<li>Apply one infrastructure approach across workloads</li>
<li>Track AI usage across separate environments</li>
<li>Carry more of the technology refresh burden</li>
</ul>
With RE:AI TaaS
<ul>
<li>Access supported AI models through one managed service</li>
<li>Consume managed AI compute through tokens</li>
<li>Match infrastructure to workload requirements</li>
<li>Choose sovereign or cost-optimised options by workload</li>
<li>Change models with less application rework</li>
<li>Centralised consumption visibility and controls</li>
<li>RE:AI manages the service as models and infrastructure evolve</li>
</ul>
Frequently Asked Questions
What is Token-as-a-Service (TaaS)?
Token-as-a-Service is a managed service for consuming AI models through a unified control plane, with model choice, intelligent routing, governance and visibility over token usage and spend.
How does RE:AI TaaS provide choice across AI models?
RE:AI TaaS enables enterprises to use multiple AI models through a managed service, including the option to bring their own models, subject to the supported configuration.
How does intelligent model routing work in RE:AI TaaS?
Intelligent routing directs requests to the appropriate available model according to configured policies and workload requirements, helping enterprises manage model usage through one service.
How does TaaS help enterprises control AI usage and spend?
TaaS provides visibility over token usage and spend, together with governance and routing controls, helping enterprises manage how AI models are consumed.
What is the difference between TaaS and MaaS?
MaaS provides access to AI models, while TaaS provides a managed consumption layer for using models with capabilities such as intelligent routing, governance and token-based usage visibility.
Related services
Related products
Model-as-a-Service — Access ready-to-use AI models or bring your own without deploying and managing the underlying model infrastructure./ai-cloud-services/model-as-a-service
