AI Backbone — LLM Provider
Core intelligence layer: model selection, API provider, deployment model
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Decision principle: Choose your orchestration framework first, LLM provider second. Most frameworks are model-agnostic. For MVP, start with hosted APIs. For production, evaluate latency, cost-per-token, data sovereignty, and fine-tuning needs.
| Tool / Model | Phase | Type | Why Choose It | Tradeoffs | Complexity | Alternatives |
|---|---|---|---|---|---|---|
GPT-4o / GPT-4.1 SaaS OpenAI — multimodal, text/image/audio |
BOTH |
SaaS API | Best-in-class reasoning, massive ecosystem, vision built-in. Go-to for complex agent tasks, code gen, document analysis. | ⚠ Data leaves your infra. Cost scales with tokens. Rate limits on free tiers. |
LOW | Claude 3.5, Gemini 1.5 Pro |
Claude 3.5 / Claude 4 SaaS Anthropic — 200k context, strong reasoning |
BOTH |
SaaS API | Best for long-document analysis, instruction following, and low hallucination. 200k context window is unmatched for RAG with large docs. | ⚠ No self-hosting. Limited fine-tuning options. |
LOW | GPT-4o, Gemini |
Azure OpenAI Service AZURE GPT-4o on Azure infra — enterprise compliance |
BOTH |
Managed | Data stays in your Azure tenant. HIPAA/SOC2 compliant. Required for enterprise/gov clients. PTU for guaranteed throughput in prod. | ⚠ Slower model updates vs OpenAI direct. Needs Azure subscription setup. |
MED | OpenAI direct API |
Llama 3 / Mistral OSS Self-hosted open-source models |
PROD |
OSS | Zero per-token cost at scale. Full data sovereignty. Fine-tunable for domain-specific tasks. Deploy on your GPU infra or AKS. | ⚠ Needs GPU infra. Ops overhead. Smaller context. Weaker reasoning than GPT-4 class. |
HIGH | Phi-3, Gemma 2 |
vLLM / Ollama OSS Self-hosted LLM inference server |
PROD |
Runtime | High-throughput batched inference for production. vLLM: best for multi-user APIs. Ollama: best for local dev and edge deployment. | ⚠ Infra management required. GPU costs. |
HIGH | TGI (HuggingFace), LMDeploy |
Orchestration Framework
The "nervous system" — agent coordination, workflow, tool use, multi-agent patterns
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Decision principle: This is the FIRST architectural decision. It shapes everything else. LangGraph = stateful/cyclical agents. CrewAI = role-based teams. AutoGen = conversation-based multi-agent. Semantic Kernel = enterprise/.NET first.
| Tool | Phase | Type | Why Choose It | Tradeoffs | Complexity | Alternatives |
|---|---|---|---|---|---|---|
LangGraph OSS LangChain — stateful graph-based agent orchestration |
BOTH | OSS | Best for complex, stateful agents with loops, branching, human-in-the-loop. Built-in checkpointing, memory, streaming. Most production-ready OSS option. | ⚠ Steep learning curve. Verbose graph definition. |
HIGH | CrewAI, AutoGen |
CrewAI OSS Role-based multi-agent crews |
MVP | OSS | Fastest path to multi-agent MVP. Intuitive agent/task/crew abstraction. Great for sequential role delegation pipelines. Client demos well. | ⚠ Less control over state. Less flexible for non-sequential flows. |
LOW | LangGraph, n8n |
AutoGen v0.4 OSS Microsoft — conversation-based multi-agent |
BOTH | OSS | Best for coding agents, autonomous problem solving, multi-agent debate patterns. AgentChat API is clean. Strong Azure/Microsoft ecosystem alignment. | ⚠ Less structured workflow control than LangGraph. |
MED | LangGraph, CrewAI |
Semantic Kernel AZURE Microsoft — enterprise SDK for AI orchestration |
PROD | SDK | Best choice if client is .NET/C# shop or deep Azure tenant. Native Azure AI Foundry integration, enterprise memory patterns, plugin architecture. | ⚠ Python support lags .NET. Smaller community than LangChain. |
MED | LangGraph + Azure OpenAI |
n8n OSS Low-code workflow automation with AI nodes |
MVP | OSS | Ideal for deterministic, operational automation (CRM sync, email triage, data pipelines). Non-dev stakeholders can edit. Fast POC delivery. | ⚠ Not suited for complex reasoning or autonomous agents. Not production-grade AI logic. |
LOW | Make.com, Zapier AI |
Vector Store / Semantic Search
Embedding storage, ANN search, retrieval backbone for RAG systems
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Decision principle: Vector DB handles semantic/fuzzy search. SQL handles exact/structured retrieval. Best RAG architectures use BOTH: vector search returns candidate IDs → SQL resolves to full structured records. Choose vector DB based on data volume, filter needs, and managed vs self-hosted preference.
| Tool | Phase | Type | Why Choose It | Tradeoffs | Scale | Alternatives |
|---|---|---|---|---|---|---|
ChromaDB OSS In-process or client-server vector DB |
MVP | OSS | Zero infrastructure to set up. Perfect for POC and MVP. Runs in-process in Python. Easy LangChain/LlamaIndex integration. | ⚠ Not production-grade at scale. Limited metadata filtering. No managed cloud offering. |
LOW — <1M vecs | FAISS, Qdrant |
Qdrant OSS Rust-based, high-performance vector DB |
BOTH | OSS | Best OSS option for production. Rich payload filtering, sparse+dense hybrid search, cloud and self-hosted. Docker-ready, fast. | ⚠ Smaller ecosystem than Pinecone. Needs infra management if self-hosted. |
MED — millions | Weaviate, Pinecone |
Pinecone SaaS Fully managed, serverless vector DB |
PROD | Managed | Zero ops. Serverless pricing model. Strong enterprise support. Best for teams without MLOps capacity who need reliable prod vector search. | ⚠ Vendor lock-in. Data leaves infra. Cost at scale. No SQL-style joins. |
HIGH — billions | Weaviate Cloud, Qdrant Cloud |
Azure AI Search AZURE Cognitive Search + vector indexing on Azure |
PROD | Managed | Best choice for Azure-native stack. Hybrid search (keyword + vector), integrated with Azure OpenAI, Cosmos DB, Blob Storage. Enterprise SLA. | ⚠ Azure lock-in. Higher cost than OSS. Slower feature velocity. |
HIGH — enterprise | Qdrant + AKS |
Weaviate OSS GraphQL API, multi-modal, hybrid search |
PROD | OSS | Best for multi-modal (text + image) retrieval. Built-in vectorizer modules, GraphQL API, object-level permissions. Strong enterprise roadmap. | ⚠ Higher resource usage. GraphQL adds learning curve. |
HIGH | Qdrant, Pinecone |
pgvector OSS Postgres extension for vector search |
MVP | Extension | Keep everything in one DB (Postgres). No extra infra. Perfect when data volume is modest and you want SQL + vector in one query. Supabase includes it. | ⚠ Slower ANN at large scale vs dedicated vector DBs. Not purpose-built. |
LOW — <500k vecs | ChromaDB, Qdrant |
Database — Structured Storage
Relational, NoSQL, graph, and time-series storage for operational data
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| Tool | Phase | Type | Why Choose It | Tradeoffs | Use Case Fit | Alternatives |
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SQLite OSS Embedded, serverless relational DB |
MVP | Embedded | Zero setup. File-based. Perfect for agent memory, chat history, structured episodic memory stores in MVP. Pairs well with ChromaDB. | ⚠ No concurrent writes. Not for multi-user prod. |
Agent memory, local dev | PostgreSQL |
PostgreSQL OSS Gold standard relational DB + pgvector |
BOTH | OSS | Best all-round choice. ACID, JSON support, pgvector extension, mature ecosystem. If in doubt, choose Postgres. Scales to most production workloads. | ⚠ Needs ops at scale. Not globally distributed natively. |
Everything structured | MySQL, SQLite |
Azure Cosmos DB AZURE Globally distributed NoSQL + vector preview |
PROD | Managed | Best for globally distributed, multi-region Azure deployments. Multiple APIs (SQL, MongoDB, Cassandra). NoSQL for flexible schemas + now has vector search. | ⚠ Expensive. RU model confusing. Azure lock-in. |
Global chatbots, IoT, sessions | MongoDB Atlas, DynamoDB |
MongoDB Atlas SaaS Managed document DB with vector search |
BOTH | Managed | Great for flexible schemas (chat history, agent state, unstructured docs). Atlas Vector Search = no separate vector DB needed for moderate scale. | ⚠ Not relational — joins are painful. Cost at scale. |
Chat history, flexible records | Firestore, Cosmos DB |
Neo4j OSS Graph DB — knowledge graphs, entity relations |
PROD | OSS | Best for relationship-heavy data: knowledge graphs, ontologies, recommendation engines. GraphRAG uses Neo4j as graph memory store. Cypher query language. | ⚠ Niche use case. Steep Cypher learning curve. Higher ops overhead. |
GraphRAG, knowledge base | Amazon Neptune, TigerGraph |
Redis / Upstash OSS In-memory key-value + vector store |
PROD | Cache | Semantic cache for LLM responses (major cost saver). Session storage, rate limiting, real-time pub/sub. Redis Stack adds vector search. | ⚠ Not a primary DB. Memory-bound. Persistence requires config. |
Caching, sessions, rate limits | Memcached, DynamoDB |
Agent Memory Architecture
Short-term, long-term, episodic, semantic, procedural memory for AI agents
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Decision principle: Memory evolves: V1 = static lookup → V2 = agentic retrieval → V3 = multi-source integration → V4 = background self-updating memory. Match complexity to actual need. Most MVPs need only V1-V2.
| Tool / Pattern | Phase | Memory Type | Why Choose It | Tradeoffs | Complexity | Alternatives |
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In-context Window OSS Pass full history in system prompt |
MVP | Working Memory | Zero implementation. Works for short sessions. Sufficient for most chatbot MVPs. Claude/GPT-4o 128k+ context makes this viable longer. | ⚠ Context saturation. Token cost scales linearly. No persistence. |
MINIMAL | Summarization buffer |
LangChain Memory Buffers OSS ConversationBufferMemory, SummaryMemory |
MVP | Short-term + Summary | Easy plug-in memory for LangChain chains. SummaryMemory compresses old turns, solving token budget problem. Backed by any DB. | ⚠ LangChain v1 memory deprecated in v0.3+. Moving to LangGraph preferred. |
LOW | LangGraph checkpointer |
LangGraph Checkpointer OSS Built-in state persistence for LangGraph agents |
BOTH | Working + Episodic | Native state snapshot per turn. Supports resume, rollback, human-in-the-loop. Backends: SQLite (dev), PostgreSQL/Redis (prod). Most production-ready pattern. | ⚠ LangGraph specific. Adds graph definition overhead. |
MED | AutoGen state, custom DB |
LangMem / LangGraph Store OSS Long-term semantic memory SDK for LangGraph |
BOTH | Semantic + Episodic | Cognitive memory model (semantic, episodic, procedural) baked into LangGraph. Cross-thread memory persistence. Best structured approach to agent long-term memory. | ⚠ Relatively new. Docs still maturing. LangGraph dependency. |
MED | Letta/MemGPT, mem0 |
Letta / MemGPT OSS Paged memory OS for LLM agents |
PROD | Full cognitive model | Most advanced open-source agent memory system. Paged memory (core/archival/recall), self-editing memory, multi-agent support. Ideal for long-running personal AI agents. | ⚠ Complex setup. Opinionated architecture. Smaller community. |
HIGH | LangMem, mem0 |
mem0 SaaS Managed memory layer for AI apps |
PROD | Semantic LTM | Managed service — no infra. Automatically extracts, stores, retrieves memories across conversations. Good for SaaS AI products needing user-level personalization. | ⚠ SaaS cost. Data leaves infra. Early stage product. |
LOW | Letta, LangMem |
Data Strategy — Ingestion & ETL
Document parsing, chunking, embedding, pipeline, and data connectors for RAG
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| Tool | Phase | Role | Why Choose It | Tradeoffs | Complexity | Alternatives |
|---|---|---|---|---|---|---|
LlamaIndex OSS Data framework for LLM — parsing, indexing, querying |
BOTH | RAG Framework | Best dedicated RAG toolkit. 160+ data connectors, advanced chunking strategies, query engines, reranking. Complements LangGraph for data-heavy RAG apps. | ⚠ Can be complex to configure. Some overlap with LangChain. |
MED | LangChain loaders |
Unstructured.io OSS Document parsing: PDF, Word, HTML, images |
BOTH | Doc Parser | Best-in-class for extracting clean text from messy documents. Handles tables, headers, images in PDFs. Open-source core + managed API for scale. | ⚠ Managed API costs. Complex docs need tuning. |
LOW | Azure Document Intelligence, Docling |
Azure Document Intelligence AZURE OCR + layout analysis + form extraction |
PROD | Doc Parser | Enterprise-grade for structured doc extraction (invoices, forms, contracts). Prebuilt models for common doc types. Tight Azure ecosystem integration. | ⚠ Pay-per-page pricing. Azure lock-in. |
MED | Unstructured, Textract |
OpenAI / Azure Embeddings SaaS text-embedding-3-small/large |
BOTH | Embeddings | State-of-the-art embedding quality. text-embedding-3-small = best cost/quality tradeoff. Critical: embed query and docs with SAME model. | ⚠ Per-token cost. Data leaves infra. Changing models requires re-embedding. |
LOW | Cohere, BGE, E5 |
Apache Airflow / Prefect OSS Workflow orchestration for data pipelines |
PROD | Pipeline Orchestration | Schedule and monitor embedding refresh pipelines. Airflow for complex DAGs. Prefect for simpler Python-native flows. Essential for keeping vector store fresh. | ⚠ Infra overhead. Overkill for simple scheduled jobs. |
HIGH | Azure Data Factory, Dagster |
UI / Frontend
Chat interfaces, dashboards, admin panels, streaming UX
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| Tool | Phase | Type | Why Choose It | Tradeoffs | Complexity | Alternatives |
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Streamlit OSS Python-native rapid UI for data apps |
MVP | Python UI | Fastest time-to-demo for Python AI apps. Built-in chat components, file upload, streaming support. Perfect for internal tools and client POCs. | ⚠ Not production-grade. Limited customization. Not for customer-facing apps. |
LOW | Gradio, Chainlit |
Chainlit OSS Chat UI framework built for LLM apps |
MVP | Chat UI | Purpose-built for chatbot UIs. Step-by-step agent reasoning display, file attachments, streaming, auth. Best for quickly deploying a polished chat interface. | ⚠ Less flexible than full React app. Python-only backend. |
LOW | Streamlit, Open WebUI |
Next.js + Vercel AI SDK OSS React framework with streaming AI hooks |
BOTH | Full-Stack | Best for customer-facing AI products. Vercel AI SDK handles streaming SSE, useChat/useCompletion hooks, model switching. Production-ready, beautiful UX possible. | ⚠ Requires frontend dev skills. More setup than Streamlit/Chainlit. |
MED | Remix, SvelteKit |
React + FastAPI OSS Decoupled frontend + AI backend |
PROD | Custom Stack | Most flexible production architecture. FastAPI serves WebSocket / SSE streaming from Python agent. React consumes it. Full control, full customization. | ⚠ Most engineering effort. Need both Python and JS/TS skills. |
HIGH | Next.js + FastAPI, Django |
Open WebUI OSS Self-hosted ChatGPT-like interface |
MVP | Pre-built | Instant ChatGPT-like UI for any OpenAI-compatible API. Docker deploy. Supports multiple models, RAG, web browsing. Zero UI development needed. | ⚠ Hard to customize deeply. More suited for internal tools. |
MINIMAL | Chatbot UI, LibreChat |
Backend / API Layer
REST/WebSocket API servers, streaming, auth, rate limiting
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| Tool | Phase | Type | Why Choose It | Tradeoffs | Complexity | Alternatives |
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FastAPI OSS Modern async Python REST + WebSocket server |
BOTH | Framework | Default choice for Python AI backends. Async-native for streaming LLM responses. Auto OpenAPI docs. SSE support built-in. Uvicorn/Gunicorn for prod. | ⚠ Python GIL limits true parallelism. Need separate worker scaling strategy. |
LOW | Django REST, Flask |
Azure Functions AZURE Serverless compute for event-driven AI logic |
PROD | Serverless | Best for event-driven tasks: webhook handlers, real-time voice pipeline steps, scheduled embedding refresh. Pay-per-execution. Tight Azure integration. | ⚠ Cold start latency. Execution time limits. Azure vendor lock-in. |
MED | AWS Lambda, Google Cloud Run |
Azure API Management AZURE API gateway with rate limiting, auth, throttling |
PROD | Gateway | Enterprise API gateway: rate limiting per user/key, token quota management, load balancing across Azure OpenAI PTU pools, auth, analytics. Essential for multi-tenant AI APIs. | ⚠ Complex setup. Azure-only. Licensing cost. |
HIGH | Kong, AWS API Gateway |
Containerization & Deployment
Docker, orchestration, CI/CD, cloud deployment targets
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| Tool | Phase | Layer | Why Choose It | Tradeoffs | Complexity | Alternatives |
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Docker + Compose OSS Container runtime + local multi-service orchestration |
BOTH | Runtime | Universal standard. Compose for local dev with multiple services (API + vector DB + Redis). Same image promotes from dev to prod. No team should ship without this. | ⚠ Not for production orchestration at scale — use K8s or managed containers. |
LOW | Podman |
Azure Container Apps AZURE Serverless K8s-based container hosting |
PROD | Hosting | Best managed container platform on Azure. Auto-scaling to zero, KEDA-based event scaling, Dapr integration. Much simpler than AKS for most AI app deployments. | ⚠ Less control than AKS. Not for stateful workloads or GPU inference. |
MED | Azure App Service, AKS |
AKS (Azure Kubernetes) AZURE Managed Kubernetes — full control |
PROD | Orchestration | Required for GPU workloads (self-hosted LLMs), complex microservice AI architectures, custom networking/security requirements, or high-throughput production AI APIs. | ⚠ High ops complexity. Requires K8s expertise. Cost. |
HIGH | GKE, EKS |
Railway / Render SaaS Simple PaaS for containerized apps |
MVP | PaaS Hosting | Deploy FastAPI + Postgres + Redis in minutes. No Kubernetes. Git push deploys. Best for rapid MVP delivery when infra is not the focus. | ⚠ Not enterprise-grade. Limited compliance controls. Vendor dependency. |
LOW | Fly.io, Heroku |
GitHub Actions SaaS CI/CD pipeline automation |
BOTH | CI/CD | Standard CI/CD for most projects. Build → test → push Docker image → deploy to container platform. Free for public repos, generous free tier for private. | ⚠ Complex pipelines become unwieldy. Azure DevOps better for deep Azure integration. |
LOW | Azure DevOps, GitLab CI |
Logging, Monitoring & Observability
LLM tracing, cost tracking, latency monitoring, error alerting
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Decision principle: AI observability ≠ traditional APM. You need LLM-specific tracing (prompt/response capture, token cost per run, chain step visibility). Add LangSmith or LangFuse early — you'll regret not having it in production debugging sessions.
| Tool | Phase | Focus | Why Choose It | Tradeoffs | Complexity | Alternatives |
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LangSmith SaaS LangChain's LLM observability platform |
BOTH | LLM Tracing | Best-in-class for LangChain/LangGraph apps. Auto-traces every chain/agent step, shows prompt/response, token cost, latency per node. Essential for debugging agent loops. | ⚠ SaaS cost at scale. LangChain ecosystem only (though SDK is broader). |
LOW | LangFuse, Arize |
LangFuse OSS Open-source LLM observability — self-hostable |
BOTH | LLM Tracing | Framework-agnostic LLM observability. Self-hostable (data sovereignty). Covers traces, evals, datasets, prompt management. Best OSS alternative to LangSmith. | ⚠ Self-hosting adds ops. Smaller community than LangSmith. |
MED | LangSmith, Helicone |
Azure Monitor + App Insights AZURE Full-stack Azure observability platform |
PROD | Platform Monitoring | Unified logs, metrics, traces for Azure-hosted apps. KQL queries for log analysis. Custom dashboards, alerting, distributed tracing. Required for enterprise Azure deployments. | ⚠ Not LLM-specific. KQL learning curve. Cost by data volume. |
MED | Datadog, Grafana Stack |
Prometheus + Grafana OSS Metrics collection + visualization stack |
PROD | Infra Metrics | Standard OSS metrics stack. Instrument FastAPI/vLLM with Prometheus exporters. Grafana dashboards for throughput, latency, token rates, queue depths, GPU utilization. | ⚠ Infra overhead. Not LLM-aware out of the box — requires custom metrics. |
HIGH | Datadog, Azure Monitor |
Helicone SaaS LLM cost + usage analytics proxy |
PROD | Cost Tracking | Drop-in proxy for OpenAI/Anthropic APIs. Tracks cost per user/session, caches responses, rate limits. Excellent for multi-tenant SaaS AI products where per-user cost matters. | ⚠ Sits in request path — adds latency. SaaS dependency. |
LOW | LangFuse, OpenLLMetry |
Voice AI Stack
STT, TTS, real-time voice pipeline, telephony integration
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Decision principle: Voice latency perception <500ms end-to-end is the target. Real-time voice = WebSocket/WebRTC throughout (no HTTP polling). STT → LLM → TTS pipeline must all support streaming. For telephony: ACS Call Automation on Azure, Twilio elsewhere.
| Tool | Phase | Layer | Why Choose It | Tradeoffs | Complexity | Alternatives |
|---|---|---|---|---|---|---|
Whisper / Azure Speech STT OSS Speech-to-text transcription |
BOTH | STT | Whisper (OSS): best accuracy, 100+ languages, self-hostable. Azure Speech: managed, streaming, real-time, enterprise SLA. Use Azure for production voice pipelines on Azure stack. | ⚠ Whisper: not real-time by default. Azure: cost per audio hour. |
MED | Deepgram, AssemblyAI |
Deepgram SaaS Real-time STT with ultra-low latency |
BOTH | STT (Real-time) | Best-in-class for real-time streaming STT. ~300ms latency. WebSocket API. Significantly faster than Azure Speech for live voice agent use cases. | ⚠ SaaS cost. Data leaves infra. Per-minute pricing. |
LOW | Azure Speech, AssemblyAI |
ElevenLabs / Azure TTS SaaS Text-to-speech synthesis |
BOTH | TTS | ElevenLabs: most natural voice quality, streaming TTS, voice cloning. Azure TTS: enterprise-grade, 400+ voices, Azure integration, Neural TTS. Choose based on naturalness vs compliance needs. | ⚠ Per-character cost. Voice cloning raises ethical/legal issues. |
LOW | OpenAI TTS, PlayHT |
Azure ACS + Call Automation AZURE Telephony + real-time voice pipeline on Azure |
PROD | Telephony | Enterprise telephony integration (PSTN, SIP). Call Automation API for programmatic call control, real-time transcription, media streaming to Azure Functions. ART Accelerator pattern. | ⚠ Complex setup. ACS + Functions + Event Grid architecture. Azure-only. |
HIGH | Twilio, Vonage |
OpenAI Realtime API SaaS End-to-end real-time voice (GPT-4o) |
BOTH | Full Voice Pipeline | Single WebSocket API for STT + LLM + TTS in one round-trip. Dramatically simplifies voice architecture. Voice activity detection included. Best for MVP voice agents. | ⚠ Expensive. Less control over individual pipeline stages. OpenAI lock-in. |
LOW | LiveKit, Daily.co + custom |
Security, Auth & Guardrails
Identity, access control, prompt injection protection, output guardrails
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| Tool | Phase | Layer | Why Choose It | Tradeoffs | Complexity | Alternatives |
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Auth0 / Azure AD B2C SaaS Identity and access management |
BOTH | Auth/Identity | Auth0: fastest MVP auth, any stack, social logins, MFA. Azure AD B2C: enterprise identity for Azure-native apps, SAML/OIDC, conditional access. Don't build auth from scratch. | ⚠ Auth0 cost at scale. B2C complex config. Vendor dependency. |
LOW | Clerk, Supabase Auth |
Azure Key Vault AZURE Secrets, keys, certificates management |
BOTH | Secrets Mgmt | Never store API keys in code or env files for client deployments. Key Vault + Managed Identity = zero-credential access pattern. Required for enterprise Azure deployments. | ⚠ Azure-specific. Adds latency if not cached. |
LOW | HashiCorp Vault, AWS Secrets Manager |
Guardrails AI / NeMo Guardrails OSS Output validation and prompt safety rails |
PROD | LLM Safety | Validate LLM outputs against schemas, PII detection, topic restrictions, hallucination checks. NeMo: NVIDIA's rail framework with Colang language for policy definition. | ⚠ Adds latency per call. Config overhead. False positives on edge cases. |
MED | Azure Content Safety, Rebuff |
Azure Content Safety AZURE Harmful content detection API |
PROD | Content Moderation | Managed API for hate speech, violence, sexual content detection in both inputs and outputs. Promptshield for jailbreak/prompt injection detection. Required for public-facing Azure AI apps. | ⚠ Per-call cost. Azure lock-in. Latency addition. |
LOW | Guardrails AI, OpenAI moderation |