Secured GPU capacity
Blackwell-class clusters sourced and operated across regions, with capacity planned ahead of your demand curve.
- B300 / B200
- Multi-region
- Capacity planning
■ Token Factory for Enterprise AI
Tokens,
made to spec.
WELA runs the factory between GPUs and your applications. We tune every layer of the inference stack so your enterprise gets the best output per dollar, at any volume.
- Interface
- OpenAI-compatible API
- Models
- Leading open-weight families
- Deployment
- Serverless · Dedicated · Reserved
- Hardware
- NVIDIA Blackwell-class GPUs
- Data
- Zero retention available
- Headquarters
- Singapore
Spec sheet · WELA token factory
Line 01 · Compute → Engine → Router → Delivery● Simulation
Output
0 tok
■ How the factory works
Four stations from silicon to sentence.
A token is the unit of work in enterprise AI. Its price and quality are set at every step of production, so we own every step and optimise them together.
Tuned inference runtime
Custom kernels, low-precision serving and speculative decoding raise tokens per second on every GPU we run.
- FP8 / FP4
- Speculative decoding
- KV-cache reuse
Right model per request
Each request goes to the lowest-cost model that clears your quality bar, measured against your own evaluation set.
- Quality gates
- Fallbacks
- Cost caps
Tokens on contract
One API, latency targets in writing, and per-team usage reporting so finance can see where every token went.
- SLA
- Usage reports
- Observability
■ Product lines
Six lines, one factory.
Start on pay-per-token, move to dedicated GPUs as volume grows, and lock in capacity when the workload is critical. Same API at every stage.
WELA-SInference
Serverless Inference
Call any hosted model by name and pay only for tokens used. Priority and fast lanes for latency-sensitive traffic.
- Billing
- Per 1M tokens
- Best for
- Prototypes, spiky traffic
- Interface
- OpenAI-compatible
WELA-DInference
Dedicated Deployments
Single-tenant GPUs running your chosen model or your own fine-tuned weights, with autoscaling across regions.
- Billing
- Per GPU-hour
- Best for
- Steady production load
- Isolation
- Single tenant
WELA-REnterprise
Reserved Capacity
Guaranteed throughput on reserved clusters with latency and uptime in the contract. Built for workloads that cannot queue.
- Billing
- Monthly commitment
- Best for
- Mission-critical AI
- SLA
- Contractual
WELA-TTraining
Fine-tuning & RL
Supervised fine-tuning, LoRA and reinforcement fine-tuning on your data. Serve hundreds of adapters on one base model.
- Methods
- SFT · LoRA · RFT
- Serving
- Multi-LoRA
- Deploy
- One click to WELA-D
WELA-XRouting
Model Router
A drop-in endpoint for teams on closed-model APIs. Routes each task to open or closed models against your quality bar.
- Billing
- Per 1M tokens
- Best for
- Cutting API spend
- Control
- Eval-gated routing
WELA-BInference
Batch Processing
Submit large offline jobs and collect results within a set window, priced below real-time inference.
- Billing
- Discounted per 1M tokens
- Best for
- Extraction, labelling, evals
- Window
- Up to 24 h
■ Output × cost
Quality held. Cost per token driven down.
We treat cost and output quality as one engineering problem. Every lever below is measured against your own evaluations before it reaches production.
QuantisationFP8 and FP4 serving, validated per model so accuracy stays within your tolerance.
Speculative decodingDraft models propose tokens that the main model verifies, cutting generation time.
Prompt cachingShared system prompts and documents are computed once and reused across requests.
BatchingContinuous batching keeps GPUs saturated without hurting time to first token.
DistillationFine-tune a smaller model on your task so it matches a larger one at a fraction of the cost.
RoutingEasy requests go to small models, hard ones to large models, decided per request.
Spend estimatorIllustrative
90%lower monthly spend
Example rates for illustration only (WELA-S $0.90, WELA-D $0.60 effective, WELA-B $0.45, WELA-X $1.40 per 1M blended tokens). Actual pricing depends on model, volume and SLA.
■ Model library
Open models, production-tuned.
We serve the leading open-weight families on our optimised engine and add new releases as they land. Bring your own weights on any dedicated line.
| Family | Developer | Strengths | Lines |
|---|
■ Developers
Switch in one line.
WELA speaks the OpenAI API format, so existing SDKs and agent frameworks work after changing the base URL and model name.
- Get a key. Issued per team, with spend limits you set.
- Point your SDK at WELA. Change the base URL and keep your code.
- Pick a model. Benchmark against your current provider on your own prompts.
■ Enterprise
Your data stays your data.
Enterprise AI runs on sensitive information. WELA is built so security, residency and procurement teams can sign off.
Retention
Zero data retention
Prompts and outputs are processed in memory and not stored, on request.
Training
Never trained on
Your inputs and outputs are never used to train any model.
Deployment
Private deployments
Run in a dedicated cluster, your VPC or on-premises hardware.
Residency
Regional data residency
Keep inference inside Singapore or a region your regulator requires.
Access
SSO and audit logs
Role-based access, per-team keys and a full audit trail of usage.
Support
Named engineers
A solutions engineer works with your team from evaluation to launch.
■ Contact
Tell us what you need to produce.
Share your workload and current spend. We will come back with a model shortlist, a benchmark plan and a cost estimate.
- info@wela.sg
- Office
- Singapore
- Response
- Within one business day