Platform
Every module. Every engine.
The home highlights the four modules that carry the ROI. Here is the full runtime — seven governance modules and the five proprietary engines that decide earlier, refuse harder, and prove further.
Runtime governance for AI applications
Seven enforcement modules. One SDK. Zero pipeline rewrites.
Automatic model choice
Every request scored across cost, quality, and latency. Azoth routes to the cheapest model that still meets your SLA — in real time, at every call.
- Cost-optimal model selection
- Latency p50/p99 enforcement
- Quality drift protection
- Fallback chain management
- Multi-provider model choice
Margin Engine
Hard budget caps per team, agent, and billing period — enforced at the runtime layer. When spend approaches limits, Azoth acts. Not alerts. Action.
- Hard limits per team and agent
- Real-time consumption tracking
- Automatic cost-mode throttling
- Slack + PagerDuty alerting
- Period-end forecasting
Loop Termination Engine
Recursive agent loops are invisible until they appear on your bill. We monitor execution graphs in real time and terminate runaway calls before the cost compounds.
- Real-time graph monitoring
- Consecutive failure detection
- Context window overflow alerts
- Auto-pause on threshold breach
- Full incident audit trail
Prompt Intelligence Engine
Your prompts carry token weight you're paying for twice. Our engine compresses, restructures, and optimizes every prompt — 40% avg reduction, zero quality loss.
- Automated compression
- A/B quality testing
- Version management
- Semantic diff tracking
- Per-agent optimization
Reused responses (cache)
Identical questions rarely arrive identically phrased. We match semantically equivalent queries to cached responses — eliminating redundant model calls.
- Intent-level matching
- 30% of answers reused on average
- TTL + invalidation control
- Per-agent cache policies
- Vector similarity scoring
AI FinOps Command Center
Full cost attribution by agent, model, team, and workflow. Real-time spend, anomaly detection, and 90-day economic forecasting. Finance and engineering, unified.
- Real-time execution stream
- Attribution by team and agent
- Economic forecasting engine
- 90-day cost modeling
- Warehouse export (S3, Parquet)
Connector Ecosystem
Connect Azoth to your existing infrastructure. GitHub App integration triggers regression tests on every PR. Native integrations for Slack, PagerDuty, and custom webhooks keep your team informed.
- GitHub App + Marketplace
- PR regression check runs
- Vision extraction pipeline
- Async enrichment workers
- Webhook event streaming
Infrastructure that refuses before it spends.
Beyond routing and caching. Azoth embeds five proprietary systems that decide earlier, refuse harder, and prove further than any AI infrastructure layer available today.
Pre-Inference Intelligence
Requests are evaluated before reaching a model. The majority that don't require generative reasoning are resolved instantly — at a fraction of the cost.
Reasoning Memory
Prior reasoning is captured and reused across related requests. Teams stop paying to re-derive conclusions their system has already reached.
Cognitive Context Compression
Long-session context is compressed without losing decision integrity or compliance constraints. Conversations and agent memory become dramatically cheaper to maintain.
Predictive Cost Orchestration
Every request is cost-modeled before execution. Model selection and resource allocation adapt dynamically as budget pressure changes — service never degrades abruptly.
Workflow Pre-Simulation
Complex AI workflows are simulated against real historical data before a single token is spent. Teams see cost, risk, and quality projections — and act on them before deployment.