Models
Bring the AI models your product needs into one developer-ready API experience.
ExploreTensorSolid · AI Infrastructure
From API
to rack.
Access leading AI models, deploy GPU compute worldwide, and build dedicated AI infrastructure — all with one partner.
TensorSolid / StackBuilt to scale
01
MODELS
02
COMPUTE
03
INFRASTRUCTURE
01 / The full stack
Three layers.
One infrastructure partner.
02 / Models
One API.
The models you need.
A familiar developer experience for building, evaluating, and shipping AI products with less infrastructure overhead.
Model catalog / by capability
ReasoningRoute intelligently
CodingRoute intelligently
MultimodalRoute intelligently
LanguageRoute intelligently
OpenAI-compatible API
Python
from openai import OpenAI
client = OpenAI(
base_url="https://api.tensorsolid.com/v1",
api_key="..."
)
response = client.chat.completions.create(
model="your-model",
messages=[{"role": "user",
"content": "Build with confidence."}]
)03 / Compute
Compute that
scales with you.
From focused experiments to steady production workloads, choose the level of control and capacity that fits.
Flexible capacity / built for momentum
Start small.
Scale without changing partners.
For fast iterationGPU Instances
For stable workloadsDedicated Servers
For sustained scaleDedicated Clusters
Availability
Capacity should be clear.
Share your workload and target region. We will scope the right configuration.
Engineering-led
Not a generic GPU catalogue.
A practical route from one GPU to infrastructure that can carry your next stage.
04 / Infrastructure
Build your own
AI infrastructure.
Go beyond cloud capacity with a practical path to physical AI infrastructure, shaped around your requirements.
Plan
Capacity · Region · TCO
Source
GPUs · Servers · Network
Build
Racks · Power · Cooling
Deploy
Cluster · Software · Network
Operate
Monitoring · Support · Expansion
05 / Why TensorSolid
Built for the journey
from product to physical scale.
[ 01 ]
One coherent stack.
Models, compute, and infrastructure are designed as a continuous path — not disconnected services.
[ 02 ]
Technical when it matters.
Move beyond generic sales conversations with an engineering-first approach to real workload decisions.
[ 03 ]
Solid by design.
Build durable AI capacity with a partner that understands the physical layer beneath the model.
TensorSolid / Start here
Build on solid
infrastructure.
Whether you are calling your first model or planning a dedicated cluster, start with the right foundation.
06 / How we engage
Start where you are.
Scale when you need to.
A clear engagement path for teams exploring models, planning capacity, or designing physical AI infrastructure.
Three ways to start
One conversation.
Multiple paths.
Start with the level of infrastructure that matches the decision in front of you. The architecture can evolve as the workload does.
Initial discussions can cover workload profile, desired region, capacity needs and deployment preferences.
07 / Common workloads
Infrastructure should match
the job, not the buzzword.
A practical starting point for the kinds of decisions teams commonly bring to TensorSolid.
01 /
AI product launch
Choose models, establish an API workflow, and create a clear route from prototype to production.
02 /
Production inference
Plan reliable capacity for an application with predictable traffic, performance, and deployment requirements.
03 /
Private AI systems
Design a deployment path with more control over the underlying compute and infrastructure environment.
04 /
Regional expansion
Evaluate where capacity should sit as a product, team, or AI footprint grows across markets.
08 / Questions, answered
Start with the
right conversation.
Not sure which layer you need? These are useful places to begin.
I am building an AI product. Where should I start?
Start with the model and API workflow. Once the product has a clearer workload profile, the same conversation can extend to compute capacity and deployment design.
When does dedicated compute make sense?
Dedicated capacity becomes relevant when a workload needs more predictable resources, a specific hardware configuration, or a clearer path to sustained production operation.
Can TensorSolid help with physical infrastructure planning?
Yes. Start with the intended capacity, region, deployment goals, and operational constraints. This provides the basis for a practical infrastructure discussion.
Do I need to know the final architecture before contacting you?
No. A useful first brief can be as simple as the product goal, expected workload, preferred region, and the decision your team needs to make next.