SaaS AI features
Connect your product to open-source LLMs through one API. Ship assistants, summaries and workflow automation faster.
Build product AI →Run the bestopen source LLMswithout touching GPU infrastructure.
Xgpu gives developers one OpenAI-compatible API for serverless GPU inference. Access leading open model families, stream responses, and scale from prototype to production without provisioning GPUs, managing servers, or planning idle capacity.
Serverless Inference
Pay only per token generated. Zero idle GPU costs, zero capacity planning guesswork.
OpenAI Compatible
Change one line of code in Python/Node SDK to point to Xgpu endpoints.
Multi-Model Routing
Switch dynamically between Llama 4, DeepSeek R1, Qwen3, and Mistral.
Enterprise Reliability
ISO/IEC 27001:2022 certified infrastructure built on 25 years of discipline.
Elastic Autoscaling
Traffic spikes handled automatically with zero MLOps headcount required.
GPU rental is the wrong starting point for most AI products.
GPU rental solves access to hardware. It does not solve the application problem. Your team still has to provision, configure, monitor, scale, optimize and pay for capacity before product demand is predictable.
⚠️ TRADITIONAL APPROACH
Always-on GPU infrastructure
High fixed cost, heavy MLOps overhead, and wasted idle capacity.
High Fixed Costs
H100 costs $2.50–$3.50/hr — ~$2,000/month even when completely idle.
Slow Provisioning
Days to provision; requires specialized MLOps expertise to configure.
MLOps Maintenance Burden
Autoscaling, utilisation monitoring, and failure handling — all your team's problem.
Isolated Serving Infrastructure
Each model family needs its own serving setup and infrastructure pipeline.
Headcount Overhead
2–3 engineers minimum required to operate serving clusters at production reliability.
Capacity Planning Risk
Capacity planning is guesswork — either over-provisioned or hitting bottlenecks.
✓ XGPU AI SERVERLESS
RECOMMENDED SOLUTION
Pay only for what you use
Zero idle cost, single endpoint integration, and automated elastic capacity.
Zero Idle Cost
Billed per million input / output tokens — pay only when generating responses.
Instant Integration
First response in under 60 seconds — just update a single config base_url line.
Managed Infrastructure
Autoscaling, smart model routing, and 24/7 monitoring — all platform responsibility.
Unified Model API
All model families accessible through one consistent OpenAI-compatible API.
Zero MLOps Headcount
No dedicated MLOps headcount required to run production AI inference at scale.
Elastic Autoscaling
Capacity scales seamlessly — traffic spikes and unexpected bursts handled automatically.
App Request
Xgpu API
Model Output
How it works
One API between your application and the inference layer.
Xgpu abstracts the operational path of AI inference: authentication, model selection, request routing, GPU execution, streaming output and usage visibility behind one developer-friendly API.
OpenAI-compatible interface
Serverless GPU execution
Streaming by design
Usage visibility
The best open source LLMs. Through one API.
Build with Llama, DeepSeek, Qwen, Mistral and other open model families through a consistent API surface. Switch model families by changing the model parameter instead of rebuilding your serving stack.
Pay for inference. Not idle infrastructure.
Xgpu is designed around measurable AI inference usage. Instead of reserving GPU capacity before you know demand, pay for the tokens your application processes.
Token-oriented economics
Every request has an input side and an output side. Input tokens carry your prompt, context and instructions. Output tokens are generated as the model responds. Pricing should make both visible before you scale.
Launch pricing pending
Plans for every stage
Developer for prototypes. Growth for production AI features. Enterprise for security review, procurement, dedicated needs and structured evaluation.
DeveloperGrowthEnterprise
AI inference for the products your users actually touch.
Use Xgpu to add assistants, copilots, search, summaries, extraction and reasoning workflows to your application without building a GPU infrastructure team first.
RAG and search
Create embeddings, retrieve relevant content and send grounded context into model responses.
Explore retrieval →Code generation
Use coding-oriented models for completion, review, test generation and internal developer tools.
Run coding models →Document intelligence
Summarize, classify and extract information from reports, contracts, policies and support records.
Process documents →Customer operations
Power response drafts, ticket summaries, knowledge search and internal assistants.
Automate support →Model experiments
Try open model families without standing up separate GPU infrastructure for every experiment.
Compare models →Built for teams that need speed and reviewability.
Xgpu brings serverless GPU inference into an operating environment designed for serious product teams, security reviewers and enterprise buyers.
ISO/IEC 27001:2022Indsoft Systems is ISO/IEC 27001:2022 certified. Exact scope details should be added after certificate verification.
Data handling clarityExplain what enters the platform, what is processed, what is logged and what is retained.
Access controlsClarify API keys, team access and administrative controls as capabilities are finalized.
Enterprise reviewBring security, compliance and deployment questions to the Xgpu team for structured evaluation.
Keep the request shape. Change the endpoint.
Already using OpenAI-compatible clients? Xgpu is designed to minimize migration friction. Keep the request format familiar and route inference to open model families through Xgpu.
Before
client = OpenAI( api_key="$OPENAI_API_KEY" ) response = client.chat.completions.create( model="gpt-model", messages=messages )
With Xgpu
client = OpenAI( base_url="https://api.xgpu.io/v1", api_key="$XGPU_API_KEY" ) response = client.chat.completions.create( model="llama-4", messages=messages, stream=True )
Get API Access
Start with one request. Scale when the product is ready.
Get early access to Xgpu and start building with serverless GPU inference for open models. We will use the first developer preview to validate workloads, model demand and production requirements.