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Model Serving & Inference Platforms
Production serving on vLLM, TensorRT-LLM and Triton over Kubernetes — autoscaling, multi-model routing, A/B rollouts and GPU sharing that lifts utilization.
Capabilities
AI That Keeps Working After Launch
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CI/CD for Models & Prompts
Versioned models, prompts and datasets with gated deployments: no change hits production without passing your evaluation suite first.
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Observability & Drift Detection
Tracing, token-level cost attribution, quality dashboards and drift alerts — you see problems before your users tweet about them.
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Continuous Evaluation Pipelines
Automated eval harnesses that score production traffic samples for accuracy, groundedness and safety — the regression tests of the LLM era.
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GPU Cost Governance
Utilization monitoring, chargeback models, idle-capacity reclamation and workload scheduling — FinOps discipline applied to AI compute.
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Managed AI Operations
Don't want to staff a platform team yet? We run your AI stack as a managed service with SLAs and follow-the-sun coverage across time zones.
Engagement Shapes
Three Ways to Work with Us
Platform build: we design and stand up your MLOps/LLMOps stack on your infrastructure, then hand it over with runbooks and training.
Operations rescue: your AI system is in production but fragile or expensive — we stabilize, instrument and optimize it.
Managed service: we operate the platform long-term under SLA while your team focuses on product.
- Open-source first. Kubernetes, vLLM, MLflow, Prometheus — no proprietary lock-in unless it earns its keep.
- Cost-visible from day one. Every deployment ships with per-request cost attribution.
- Eval-gated releases. Quality regressions blocked in CI, not discovered in production.
- Works on-prem and in-cloud. The same stack patterns on your GPUs or your cloud account.
Is Your AI Stack Production-Grade?
Get a free operations health check: we review your serving, monitoring and cost posture and give you a prioritized fix list.
Request a Health Check