Full-Stack AI Application Infrastructure
Everything you need to build, deploy, and operate AI-powered applications — from LLM routing and memory to email notifications and user auth.
Pain Points, Solved
Common Pain Points
AI apps need more than just an LLM
Calling an LLM is easy. Building a production app around it — with auth, notifications, persistence, and observability — requires stitching together a dozen services.
No visibility into AI costs and performance
Without centralized observability, LLM token costs spiral, latency issues go undetected, and debugging prompt failures is guesswork.
Memory and context are afterthoughts
Most AI frameworks treat conversation history and user context as something you build yourself, leading to fragile, custom persistence layers.
How Transactional Helps
AI Gateway with built-in observability
Route requests across OpenAI, Anthropic, Google, and more through a single proxy. Every call is traced with latency, token usage, and cost breakdowns.
Persistent memory for every user
The Memory module stores conversation history, user profiles, and semantic context — queryable and managed without custom infrastructure.
Full application stack in one platform
Combine AI Gateway with Auth for user management, Email for notifications, Support for human escalation, and Forms for data capture. All connected.
By the Numbers
15+
LLM providers supported
30%
Avg. cost reduction
90 days
Trace retention
1M tokens
Context window
Overview
Building an AI application means more than calling an API. You need user authentication, email notifications when async tasks complete, a chat interface for human-in-the-loop workflows, and observability to understand what your models are actually doing.
Transactional gives you all of this in a single platform. The AI Gateway handles model routing, the Memory module manages context, and every other module — email, auth, support, forms — is already integrated.
Architecture
Model routing and fallbacks
The AI Gateway supports automatic failover between providers. If OpenAI is slow, route to Anthropic. If a model is deprecated, swap it without changing application code.
Cost controls
Set per-user and per-organization token budgets. Get alerts before you overspend. View cost breakdowns by model, user, and feature.
End-to-end traces
Every LLM call is captured with input, output, latency, and token counts. Traces connect to downstream actions — the email that was sent, the form that was submitted, the support ticket that was created.
Use this when you are building
- AI chatbots with persistent memory and human escalation
- Document processing pipelines with email delivery of results
- AI agents that need auth, tool use, and auditability
- Copilot features embedded in your SaaS product