Service · AI Models on Cloud
Generative AI on AWS Bedrock, Microsoft Foundry & Vertex AI
Enterprise AI, inside the cloud you already trust.
Octobit8 designs, deploys and runs generative AI on AWS Bedrock, Microsoft Foundry (formerly Azure AI Foundry) and Google Vertex AI, with the security, governance, scalability and cost control your IT and compliance teams require.
We're cloud-neutral. We recommend the platform that fits your existing stack, data location, model needs and budget — not the one we'd rather sell.
Who is this for
Common profiles that get the most out of this service.
Data must stay in your account
Prompts and outputs need to stay within your cloud tenancy and chosen region, including in-country regions where available.
One bill, existing contracts
You want to use committed cloud spend and existing enterprise agreements instead of new vendor contracts.
Choice of models matters
You want access to leading models from Anthropic, Meta, Mistral, Cohere, Amazon, OpenAI and Google through one platform.
Enterprise controls are non-negotiable
Identity and access management, private networking, encryption, logging and compliance certifications you already rely on.
Scale without managing servers
Managed, serverless inference that scales with demand.
What sets it apart
Principles we apply on every engagement—so results are measurable, not just delivered.
Cloud AI architecture
Reference architectures for RAG, agents and chat, with private networking (VPC / VNet endpoints), identity and encryption.
Model selection and benchmarking
We test candidate models on your own tasks for quality, latency and cost per request.
Application build
Agents, RAG pipelines and assistants built on native services, with code you own.
Guardrails and responsible AI
Content filtering, PII redaction, grounding checks and topic restrictions using platform-native tools plus custom checks.
LLMOps
Infrastructure as code (Terraform, CloudFormation, Bicep), CI/CD for prompts and models, evaluation pipelines and observability.
Cost optimisation
Right-sized models, prompt caching, batch inference, provisioned throughput where it pays, model routing, and cost dashboards per team or feature.
Capabilities & deliverables
Concrete workstreams we plan, execute, and hand over with documentation and dashboards.
Amazon Bedrock & SageMaker AI
Managed access to Claude, Llama, Mistral, Amazon Nova and more on AWS.
- ▸Knowledge Bases for RAG
- ▸Bedrock Agents and AgentCore, Guardrails
- ▸SageMaker for hosting fine-tuned models
Microsoft Foundry
Azure OpenAI and a large model catalogue.
- ▸Agent Service
- ▸Azure AI Search for RAG
- ▸Content safety and fine-tuning
Google Vertex AI
Gemini and the Model Garden, including Claude.
- ▸Agent Builder
- ▸Vertex AI Search
- ▸Model tuning
Self-hosted / hybrid
For full control and data sovereignty.
- ▸vLLM or Triton on GPU instances or Kubernetes
- ▸Any cloud or on-premise
What you get
Concrete deliverables from this engagement.
Architecture document and model benchmark report
Infrastructure-as-code repository
Deployed application in your cloud account
Security and compliance documentation pack
Cost dashboard and optimisation recommendations
Additional benefits
How we work with you
A phased approach with clear artifacts—so stakeholders see progress weekly, not only at launch.
Readiness review
Your cloud landing zone, security policies, data locations, quotas and target use cases.
- Readiness report
- Use-case shortlist
Architecture and model shortlist
Design document and benchmark results for 2–4 candidate models.
- Architecture doc
- Benchmark report
Build
Infrastructure as code plus the application, in your account.
- IaC repository
- Deployed application
Security and compliance review
Support for your InfoSec, DPO and audit teams with documentation.
- Compliance documentation pack
Go-live and run
Monitoring, alerting, cost reports and optional managed operations.
- Live application
- Cost dashboard
Use cases by industry
Where teams in our focus industries are already applying this service.
Travel & Hospitality
- ▸Bedrock-powered booking and guest-service agents integrated with PMS and GDS systems
- ▸Multilingual review summarisation at scale using batch inference
Healthcare
- ▸Clinical documentation assistants hosted in-region with private networking and audit logs
- ▸Medical document RAG on Microsoft Foundry and Azure AI Search, aligned with hospital IT standards
EdTech
- ▸Vertex AI tutors grounded in course content stored in Google Cloud
- ▸Scalable content and assessment generation with cost caps per course
Stack & integrations
Representative tools—we meet you where your stack already lives and document every handoff.
AWS
- —Amazon Bedrock
- —Amazon SageMaker AI
Azure
- —Microsoft Foundry
- —Azure AI Search
Google Cloud
- —Google Vertex AI
- —Vertex AI Search
Self-hosted & IaC
- —vLLM, Triton, Kubernetes
- —Terraform, CloudFormation, Bicep
Good to know
Choosing a platform
- ✓Usually the one you already use — the major platforms now offer strong model choice
- ✓The bigger factors are where your data lives and what your team knows
Data & model terms
- ✓The major cloud AI platforms state that customer prompts and outputs are not used to train their base models
- ✓We confirm current terms for your chosen platform during the readiness review
- ✓We build with a model abstraction layer, so switching models later is a configuration change plus a test run
Engagement models
Cloud AI Quick-Start
Fixed price, 3 weeks — readiness review, model benchmark and a deployed reference application.
Fixed-scope Project
Milestone-based delivery of a production cloud AI application.
Managed AI Operations
Monthly fee for monitoring, cost optimisation and platform upgrades.
Starter offer
Cloud AI Quick-Start
3 weeks · [₹ / $ amount — confirm before publishing]
We review your cloud readiness, benchmark models for one use case, and deploy a secure reference application (RAG or chat) in your AWS, Azure or Google Cloud account, fully in infrastructure as code.
Frequently asked questions
Which cloud should we choose?▼
Can we switch models later?▼
Is our data used to train the models?▼
Can you help with cloud credits or partner programmes?▼
Ready to talk specifics?
Share your goals, timelines, and stack—we will propose a scoped next step.