Deploy Where
Your Data Lives
Cloud, private instance, or on-premise. Your choice. Primary SaaS on AWS, Azure for secure client deployments, self-hosted on your tenant when needed. No architectural changes between deployment modes. Same platform, same capabilities, different locations. Start with cloud for speed, move to private when required. European hosting, sovereign clouds, air-gapped environments. Deploy flexibly, migrate easily.
Flexible deployment options
for every requirement
Cloud for speed. Private instance for isolation. On-premise for control. Same platform, different locations. Start where it makes sense, migrate as needs evolve. No rework, no lock-in.
Cloud deployment for immediate start
AWS, Azure, or GCP hosting. No infrastructure setup. Start in minutes, scale automatically. Cloud provider credits available (AWS). Best for fast deployment, minimal ops overhead.
Private instance with dedicated infrastructure
$7,500/month. Your own dedicated environment. BICEP template for Azure one-click deployment. Private Azure environment with full control. Best for data isolation requirements.
Private Managed Instance
On-Premise Deployment
Standard SaaS (Multi-tenant cloud)
On-premise for full control
Deploy in your data center. Docker-based deployment package. You manage hardware and GPUs. We provide configuration files. Best for air-gapped or regulatory requirements.
Private Managed Instance
Multi-cloud support
Deploy on AWS, Azure, or Google Cloud. Cloud-agnostic architecture. Use your existing cloud provider credits. No lock-in to specific infrastructure.
Multi-Cloud Deployment
Local (European) data residency
Hosted in Europe for GDPR compliance. Data stays in EU. Same capabilities as US hosting. Address sovereignty requirements without compromise.
Compliance built in
HIPAA and SOC-II compliant in every deployment mode—cloud, private instance, or on-premise.
Same platform everywhere
No feature differences between deployments. Cloud, private, on-premise—same capabilities. Build in cloud, deploy on-premise if needed. No rework for deployment changes.
Enterprise support included
Solution engineers help with deployment. Integration support for enterprise systems. Migration assistance between deployment modes. Not self-service only—guided implementation available.
Cloud deployment: Fastest path to production
Our primary SaaS runs on AWS with Azure available for secure client deployments. No infrastructure setup required. Sign up and start building agents immediately. Automatic scaling handles workload variations. Updates and maintenance are managed by us. AWS marketplace credits can be applied. SOC 2 compliant shared infrastructure with tenant isolation. Best for organizations that want speed without ops overhead.
Private instance: Dedicated environment
Private instance deployment provides a dedicated environment running on your cloud infrastructure. Priced at $7,500/month. For Azure, a BICEP template enables one-click deployment. For customers with Azure subscriptions, we deploy our services as self-hosted on their tenant. You control the infrastructure location and network configuration. Data stays in your environment, not shared infrastructure. All platform features available. Updates managed by us with your approval schedule.
On-Premise, Full control
On-premise deployment runs entirely within your data center. Docker-based deployment with configuration files provided. Customer manages hardware, GPUs, and infrastructure orchestration. We provide the deployment package and configuration support. For LLM integration, you provide model endpoints—we route requests. GPU distribution managed by your orchestration layer. Best for air-gapped environments or strict regulatory requirements.
LLM integration: Any model, any location
Model-agnostic and deployment-agnostic for LLMs. Use OpenAI, Anthropic, Google, or any provider via their APIs. Use self-hosted models (Llama variants, fine-tuned models) via custom endpoints. Specify endpoint, API key, and model version. Requests map accordingly. Round-robin load balancing across multiple deployments available. On-premise deployments can use local models exclusively. Data sent to LLMs is not used for training.
Middleware requirements: What on-premise needs
On-premise deployment requires a middleware layer between our infrastructure and your systems. We provide SaaS + Agent OS layer. Customer provides infrastructure layer and orchestration. GPU distribution is customer-managed. We don't directly integrate with Nvidia AI Enterprise or similar. If you have 5 GPT deployments, we route between them. GPU allocation within each deployment is your orchestration. This architecture provides flexibility while maintaining clear responsibility boundaries.
Migration paths: Change deployments as needs evolve
ReinforcedX's architecture supports migration between deployment modes. Start with cloud SaaS for rapid proof of value. Move to private instance when data isolation requirements emerge. Move to on-premise when regulatory requirements demand it. Agents, configurations, and integrations transfer between environments. Solution engineers assist with migrations. No platform lock-in to a deployment mode.
Regional compliance: Data where it belongs
ReinforcedX offers regional deployment options for data residency requirements. European hosting ensures data stays in EU for GDPR compliance. Other regions available based on requirements. Private instance and on-premise deployments give complete control over data location. Sovereign cloud support (government cloud regions) available. All deployment options provide the same platform capabilities. No feature differences by region.
Support and implementation: Not just software
Enterprise deployment includes solution engineer support. Help with initial deployment and configuration. Integration assistance for enterprise systems (SAP, Oracle, Workday). Optimization guidance for best practices. Training for your team on platform capabilities. Migration assistance when changing deployment modes. Not just software delivery. Guided implementation to production.
Start building custom AI agents to automate processes
Join our platform and start building AI agents for various types of automations.
Questions teams ask about deployment
What does deployment actually involve?
We design the workflow, build the agent and the evaluation around it, run it in shadow mode against real traffic, then hand it over with a runbook. You end up owning a running system for deployment plus the evidence it works, not a proof of concept that needs rebuilding.
How long before deployment is live?
Four weeks is the standard implementation: discovery in week one, environments in week two, a shadow-mode pilot in week three, handover in week four. Most teams see their first agent running against real data by week three.
Do we need an ML team to run this?
No. Most clients start with strong software engineers and no ML specialists. The engagement is built so your existing team owns the system afterwards — we train them while we build rather than handing over documentation at the end.
How do you know it is working?
Every system ships with an evaluation suite: golden datasets built from your real cases, rubric-driven scoring, and regression gates in CI. Quality becomes a number you track per release rather than an opinion, and drift pages you the way a failing test would.
What happens when the agent gets it wrong?
Low-confidence and high-stakes cases route to a human queue by design. Every failure is captured with full trace context, and those traces become new evaluation cases, so the same mistake is caught automatically next time rather than recurring.
Does this run in our environment or yours?
Yours. Deployment happens inside your cloud perimeter, against your data stores and your identity provider. We integrate with the stack you already run rather than asking you to move anything into ours.
Who owns what we build?
You do. Fine-tuned weights, datasets, evaluation suites and runbooks are yours, handed over at the end of the engagement. There is no lock-in that requires us to keep the system running.
How is this priced?
A platform subscription plus a fixed-scope implementation fee, quoted in writing before work starts. Implementation is priced by engagement rather than by the hour, so a slower week costs you nothing extra.
We tried something like this before and it failed. Why would this be different?
Most failures are not model failures — they are missing evaluation, no human fallback, and no way to tell whether a change made things better. Those are the parts we build first. If we cannot define how success is measured for deployment, we say so before taking the work.
What do you need from our team?
One process owner who knows the workflow end to end, one engineer with access to the systems being integrated, and a weekly 45-minute review. That is genuinely it — no standing project committee.