01
One execution path rarely fits every workload
Workloads differ in quality, latency, privacy, volume, capability, and economics.
Hybrid AI infrastructure
Optimize how every AI workload is executed.
Manthan is the inference infrastructure layer for running AI workloads across your infrastructure and model providers.
Deploy open-weight inference on infrastructure you control, while keeping your existing model providers available through the same interface.
One API. Your infrastructure. Your providers.
Inference control planeclients · manthan · providers
Clients
Claude Code
AI products
Agents
·
Manthan
Sits between
Route · policy · meter · fallback
Destinations
Your infrastructure
Anthropic
OpenRouter
OpenAI
manthan · shellready
➜ manthan status
# Deploy open-weight inference on i
The problem
Production AI needs better execution economics.
AI workloads increasingly span model APIs, open-weight models, and dedicated infrastructure. The challenge is deciding where each workload should run, operating those paths reliably, and understanding the economics of the result.
What happens today·illustrative
Different workloads often share one execution strategy.
Quality needs, latency, privacy, and economics vary, but operations often treat them as one path.
Recurring traffic
VolumeWorkload mix A
Stable production traffic with defined quality and latency targets
Specialized needs
SelectiveWorkload mix B
Specialized capability, strict latency, or provider requirements
Where it all goes
Single execution strategy
One infrastructure or provider approach for mixed workloads
Result
Harder to optimize quality, reliability, and cost together
Decide where each workload should run. Operate those paths reliably, and understand the economics.
02
Production inference is an infrastructure problem
Serving runtimes, model configuration, capacity, observability, utilization, security, and failure handling have to operate as one system.
03
Optimization needs a unified control layer
Provider access, infrastructure, execution policy, usage, routing, and fallback need one operational layer.
How it works
Adopt hybrid inference without re-architecting your application.
Connect once. Keep your existing providers. Add optimized inference infrastructure where it creates measurable value.
01
Connect your existing stack
Use compatible SDKs and existing provider credentials with minimal application changes.
02
Understand the workload
Evaluate quality, latency, volume, utilization, privacy, and economics at the workload level.
03
Execute on the right path
Run each workload through the environment that best fits its requirements while Manthan maintains policy, observability, and fallback.
Execution paths
One interface. Multiple execution paths.
Manthan operates across customer-controlled inference, optimized serving infrastructure, and configured model providers.
Customer-controlled
Run models on infrastructure you control.
Deploy suitable open-weight workloads on customer-controlled infrastructure when control, privacy, utilization, or workload economics make dedicated inference the better fit.
- Serve selected workloads on open-weight models you operate
- Manage capacity and serving configuration
- Align execution with privacy and governance requirements
- Measure latency, reliability, utilization, and cost
Optimized inference
Use infrastructure optimized for the workload.
Run recurring workloads on serving infrastructure configured around model, latency, throughput, and capacity requirements.
- Configure serving around workload requirements
- Use dedicated capacity when volume and economics justify it
- Track utilization and infrastructure cost
- Adjust execution as workload behavior changes
Provider execution
Retain provider capability where it adds value.
Use frontier and serverless providers when their capability, economics, or availability make them the better execution path.
- Keep provider relationships and credentials in place
- Use configured providers when they are the better fit
- Maintain continuity when alternate capacity is unavailable
- Access providers through the same application-facing interface
Architecture
One API. A flexible execution layer.
Clients connect through one OpenAI-compatible API. Manthan applies workload policy using telemetry and economic signals, then executes across customer-controlled inference, optimized serving, and configured model providers.
Operating principles
- 01
Policy-driven execution
Start with explicit workload policies and add sophistication only where it creates measurable value.
- 02
Resilient execution
Maintain continuity across configured execution paths when capacity, capability, or availability changes.
- 03
Measurable economics
Track latency, failures, utilization, execution mix, and cost at the workload level.
Production infrastructure · Not a model marketplace
Execution layer·policy · telemetry · economics
Claude Code · AI products · agents
Manthan APIOpenAI-compatible
Workload policy · telemetry · economics
Customer-controlled
Optimized inference
GPU
GPU
GPU
GPU
Provider execution
Model providers
Configuredprovidersas needed
Platform
One control plane for production inference.
Manage inference access, infrastructure, provider connectivity, usage, and execution policy from one operational layer.
Overview
Organization home for inference configuration, access, and operational status.
Overview·Product console
Inference economics
Measure what actually matters.
Token price alone does not determine production economics. Manthan evaluates the total cost of delivering a successful workload outcome across model providers and infrastructure.
Ask 01
Did the task succeed?
Quality and completion matter more than tokens generated.
Ask 02
What did it cost?
Total spend for that outcome across model providers and infrastructure.
Ask 03
Was it fast and reliable enough?
Latency, failures, and fallback, not just unit price.
Primary metric · cost per successful task
Today
Single execution strategy
Mixed workloads often share one infrastructure or provider approach, so blended economics stay hard to read.
Illustrative execution mix
With Manthan
Workload-aware execution mix
Route workloads according to their requirements and measure economics across the execution mix.
OptimizedControlledProviders
Example only. Actual execution mix depends on workload requirements and economics.
Infrastructure economics estimator
Model how workload mix, volume, and infrastructure assumptions affect blended inference economics.
What that could mean
- Current inference spend
- $48,000 / mo
- Estimated blended spend
- $38,760 / mo
- Estimated monthly impact
- $9,240 / mo
- Estimated annual impact
- $110,880
- Estimated overall cost improvement
- 19%
Illustrative estimate only. Actual economics depend on workload characteristics, quality requirements, model selection, infrastructure utilization, provider pricing, and fallback behavior.
Why Manthan
Infrastructure for AI workload execution.
01
Workload-first execution
Optimize around workload requirements rather than tying every request to the same model or infrastructure strategy.
02
Economics-aware infrastructure
Evaluate provider cost, infrastructure utilization, latency, reliability, and workload outcomes as one system.
03
Infrastructure optionality
Use customer-controlled infrastructure, optimized inference, or existing model providers through one operational layer.
Who it is for
Built for AI products operating at production scale.
AI product teams
Operate production inference across infrastructure and providers without rebuilding integrations as the execution mix evolves.
AI platform teams
Centralize access, capacity, usage, and execution policy so infrastructure decisions remain measurable and resilient.
AI-native businesses
Manage inference as a production system as AI becomes a meaningful component of product infrastructure and cost.
Next step
Build a more efficient inference stack.
Tell us your stack and the workload you want to optimize. Manthan helps determine the right execution path and deploy the required inference infrastructure while keeping your existing providers in place.