Run AI in your cloud — data, cost, and control stay yours

Why self-host AI

You're an enterprise or mid-size agency with clients in healthcare, finance, or legal. Your clients won't let their data touch third-party AI SaaS platforms — compliance won't sign off, legal says no, or the contract explicitly forbids it. Cloud-hosted AI (hosted by a vendor) is a non-starter.

Self-hosting means the AI runs in your AWS, Azure, or GCP account. Your data never leaves your perimeter. You control access, audit logs, model selection, and cost. You're not locked into a vendor's roadmap or pricing changes.

StacX24 Runtime is a self-hosted AI orchestration layer. It wraps agents and RAG with monitoring, cost controls, conversation memory, request/response logging, and governance hooks — on your infrastructure. You bring the cloud account and model API keys (Claude, OpenAI, Gemini, or self-hosted Llama/Mistral). We ship the runtime as Docker containers, Kubernetes Helm charts, or Terraform modules.

Runtime capabilities

1. Agent orchestration

Run multi-step agents with tool calling, memory, and human-in-the-loop approvals. Agents execute in isolated sandboxes — a buggy agent can't take down your stack. Workflow definitions are code (TypeScript or Python) so you can version, test, and review them like any application logic.

2. RAG backend

Ingest, chunk, embed, retrieve, rerank. The runtime manages your vector DB (Postgres pgvector, Pinecone, or Weaviate), handles hybrid search, enforces citation tracking, and logs retrieval traces. You own the embeddings; they're stored in your database, not a vendor's.

3. Cost control

Set per-user, per-agent, and per-day token budgets. Track costs in real time, broken down by model, task, and client. If an agent or RAG query hits a spending threshold, it pauses and alerts instead of burning through your API budget. Cost dashboards surface spend anomalies before they escalate.

4. Observability

Trace every request: user query → agent steps → tool calls → LLM prompts → responses. Logs are structured (JSON) and exportable to your monitoring stack (Datadog, Grafana, CloudWatch). Metrics: latency, token usage, error rates, retrieval quality. Alerts fire when performance degrades.

5. Governance and audit

Every prompt, tool call, and output is logged with timestamps, user IDs, and session context. Audit logs are immutable and retained per your compliance requirements (GDPR, HIPAA, SOC 2). Role-based access control: define who can run agents, approve outputs, or access production logs. PII redaction and data retention policies are configurable.

Model flexibility

You're not locked into one LLM provider. The runtime supports:

• Hosted APIs: Claude (Anthropic), GPT-4 / GPT-4o (OpenAI), Gemini (Google)
• Self-hosted models: Llama 3, Mistral, Qwen via vLLM, TGI, or Ollama
• Model routers: Bedrock, Azure OpenAI, Vertex AI

Switch models per task: use Claude for reasoning-heavy workflows, GPT-4o for speed, and a local Llama for low-latency, on-prem requirements. The runtime abstracts the provider API so swapping models doesn't require rewriting agent code.

Deploy path

Week 1–2: Architecture review. We assess your cloud setup (VPC, networking, IAM), compliance requirements, and data residency constraints. Terraform or CloudFormation templates are adapted to your environment.

Week 3: Runtime deployment. Docker images or Helm charts deploy to your Kubernetes cluster or ECS/Fargate. Database (Postgres) and vector store (pgvector or Pinecone) are provisioned. Monitoring hooks integrate with your observability stack.

Week 4: Agent/RAG integration. Your first agent or RAG workflow goes live in a staging environment. You test with real data, validate cost and latency, and confirm logs flow to your dashboards.

Week 5–6: Production rollout. Limited user beta, monitoring tuning, runbook handoff. Your ops team can manage the runtime without us once deployed.

Post-deploy: We offer 30-day support and optional retainer for ongoing runtime updates, new agent development, and eval expansion.

Who it's for

Enterprise agencies with strict data residency or compliance requirements. Mid-size agencies building AI for clients who won't accept third-party SaaS. In-house teams at regulated companies (healthcare, finance, legal) deploying AI internally.

If you need AI that runs in your environment, with your governance, on your timeline — book a call.

Book Strategy Call →

Or email contact@stacx24.com with your deployment requirements.