Consumer AI — $80K/month inference bill cut to $22K
Audited the inference path. Found a 30% duplicate-call rate, moved high-volume prompts to a smaller model, added prompt caching, set per-tenant budgets. Same quality, 73% lower bill.
The MLOps platform that turns your AI demo into a product.
When the AI demo becomes a product, you need model versioning, training pipelines, A/B routing, canary releases, cost controls, and a way to roll back when a model version regresses. We build the platform — or harden the one you have.
Rate $100 – $130 USD / hour · fixed-fee and retainer available
What you get
- Model registry
- versioned, signed, with a clear promotion path (dev → staging → canary → prod).
- Training pipelines
- Kubeflow / Vertex AI / SageMaker, with data versioning, hyperparameter sweeps, and a real test set.
- Inference routing
- shadow, canary, A/B, with explicit per-route latency / cost / quality budgets.
- Evals
- frozen golden set, regression alerts on every prompt or model change, cost per call.
- Cost controls
- per-tenant budgets, model-tier routing, prompt caching, monthly cost review.
- Observability
- per-call traces, output schema validation, drift detection.
How we engage
- 01
MLOps audit (1–2 weeks)
We review your training, serving, and eval setup. Written report of the gaps and a phased plan.
- 02
Reference build (4–8 weeks)
One end-to-end model lifecycle — train, register, canary, monitor, rollback.
- 03
On-call model retainer
We run the model health, alert on regressions, and roll back when needed.
Stack we work in
Training
Vertex AI, SageMaker, Kubeflow, Ray, Modal, Anyscale
Serving
vLLM, Triton, TGI, Ollama, BentoML, custom
Registry
MLflow, Vertex Model Registry, SageMaker Model Registry, custom
Feature store
Feast, Tecton, Vertex Feature Store
Evals
Promptfoo, Braintrust, custom labeled sets
Vector DB
Pinecone, Weaviate, pgvector, Qdrant
Reference architectures
Anonymized patterns from real engagements. Client names omitted; details available under NDA.
Series A AI — 4 models in production, 1 rolled back in 90s
Built a canary routing layer with auto-rollback on quality regression. The first bad model promotion was rolled back in 90s, before any customer noticed.
Healthtech — HIPAA-compliant training pipeline
On-prem training with PHI isolation, encrypted model weights, signed promotion artifacts, full audit log. Cleared the BAA security review on the first submission.
Questions we get asked
Do you do fine-tuning?
When it actually helps. Most startups do not need it — good retrieval, good prompts, and a long enough context window get you 90% of the way. We tell you honestly when fine-tuning is the right call.
How do you handle model versioning?
Every model version is registered, signed, and tagged. The serving layer routes by version. We never overwrite a production model — promotions are explicit, with a roll-forward and roll-back path.
Can you take over an in-house model team?
Yes. We usually start with an audit, then either embed with the team (retainer) or take over a specific workstream (fixed-fee milestones).
What’s your take on LangChain / LlamaIndex?
Both are fine for prototypes. For production, we usually build a thin custom orchestration layer — the framework is in the way more often than it helps at scale.
Do you do GPU infrastructure?
We design and deploy it (Lambda Labs, RunPod, AWS, GCP, on-prem). We don’t sell GPUs. The right answer is sometimes a single A100; sometimes it’s a 16-GPU node. We tell you which.
Let’s scope it properly.
A 30-minute call. No deck, no pitch — we read your repo or your architecture diagram and tell you what’s realistic.