Private AI deployments
Models and data inside your own AWS, Azure or Google Cloud account.
Cloud and LLMOps
Cloud and LLMOps for AI that runs in production.
We take AI from a demo to a system you can trust. We deploy in your cloud, add evals, tracing and guardrails, and keep costs in check as usage grows.
- AWS Bedrock
- Azure AI Foundry
- Vertex AI
- Kubernetes
- Terraform
- LangSmith
- Langfuse
What is LLMOps?
LLMOps is the practice of running language model apps in production. It covers deployment, evaluation, monitoring, prompt and model versions, safety and cost control. It is to AI apps what DevOps is to regular software.
Signs you are ready
Sounds familiar?
- Your AI works in a demo but nobody trusts it in production.
- You cannot tell if a prompt or model change made things better or worse.
- Your AI bill grows faster than your usage.
- Security or compliance teams blocked your AI rollout.
Use cases
What we build.
Evals and regression tests
Automatic tests that score quality on every prompt, model or code change.
Tracing and monitoring
See every request, tool call and cost. Get alerts when quality drops.
Cost optimization
Model routing, caching and batching to cut spend without hurting quality.
Guardrails and safety
PII redaction, content filters and red teaming before and after launch.
Deliverables
What you get.
Everything ships in your repo and your cloud. You own all of it.
- 01
Infrastructure as code
Terraform and Kubernetes setups your team can review and repeat.
- 02
Model gateway
One place to route between models, with fallbacks and rate limits.
- 03
Eval suite
Test sets and scoring wired into your CI, so bad changes never ship.
- 04
Observability
Tracing and dashboards in LangSmith, Langfuse or the tools you already use.
- 05
Security review pack
Clear docs on data flow, access and controls for your security team.
How we work
Our approach.
Every project runs on the Zap Sprint. A fixed price, a working prototype in about 14 days and a production launch in four to six weeks.
- 01
Review what you have
We audit your current AI setup, costs, risks and gaps.
- 02
Set up the platform
Cloud accounts, networking, model access and secrets, done the secure way.
- 03
Add evals and tracing
We measure quality first, so every later change is safe.
- 04
Optimize and hand over
We tune cost and speed, then train your team to run it.
FAQ
Cloud and LLMOps FAQ
Still curious? Email us at hello@zappient.com. A real human replies within a day.
Usually the cloud you already use. Each offers strong models and enterprise controls. Bedrock offers Claude and many open models. Azure offers OpenAI models. Vertex offers Gemini. We can also run more than one behind a single gateway.
Yes. We deploy models like Llama, Mistral, DeepSeek and Qwen on your own GPUs or on managed endpoints. This helps when data must never leave your account.
Evals are automated tests for AI output. They score answers for accuracy, tone, safety and format on a fixed set of cases. They tell you if a change made the system better or worse.
It depends on your setup. Common wins are routing simple requests to smaller models, caching, shorter prompts and batching. We measure your current spend first and share a plan with expected savings.
Related services
Pairs well with.
Got a messy process?
Let’s zap it.
Tell us what slows your team down. We will show you what an agent could do about it. The first call is free.
- 01Tell us about the job in two minutes
- 02A senior engineer replies within 24 hours
- 03Get a plan and a fixed quote after one call