Production operations
AI Infrastructure & MLOps
Deploy and operate AI systems with the observability, reliability, security, and cost controls needed for production.
Discuss your project ↗What this solves
From promising capability to an operating product.
Production AI needs an operating layer around the application. TensorPeak Labs builds that layer so teams can deploy confidently, understand system behavior, and improve it without guesswork.
- ✓Move AI workloads from experiments into repeatable production environments
- ✓See model quality, latency, failures, and spend in one operating picture
- ✓Scale infrastructure without locking the product to one model provider
Capabilities
The parts required to make it work.
AI deployment architecture and cloud infrastructure
Model gateways, provider routing, fallbacks, and cost controls
Evaluation pipelines, tracing, monitoring, and alerting
CI/CD, secrets, access control, and operational runbooks
Delivery approach
A clear sequence from uncertainty to operation.
- 01
Assess
Review workloads, providers, data boundaries, reliability needs, and existing infrastructure.
- 02
Design
Define deployment, routing, evaluation, observability, security, and recovery patterns.
- 03
Automate
Build repeatable environments, delivery pipelines, tests, dashboards, and alerts.
- 04
Operate
Measure production behavior and improve reliability, performance, quality, and cost.
FAQ
Questions about this service.
Can you improve an AI system that is already running?+
Yes. We can assess an existing system and improve its deployment, observability, reliability, evaluation, and cost controls.
Do we need Kubernetes for AI infrastructure?+
Not necessarily. We choose the simplest infrastructure that satisfies the workload, reliability, security, and scaling requirements.
Can the architecture support more than one model provider?+
Yes. Where it creates practical value, we can design model routing and fallback paths that reduce provider lock-in and improve resilience.
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