Lyzr
Infrastructure for large companies to actually put AI agents into production safely — with guardrails against hallucinations, audit logs, and governance — instead of stopping at an impressive-looking prototype.
🔗 Visit Lyzr
Description
Plenty of companies can build an AI agent demo; far fewer can put one into production where it touches real customers and real compliance requirements without embarrassing hallucinations or a governance nightmare. Lyzr sells the infrastructure layer that sits between a prototype agent and a production one: a "control plane" that adds guardrails, observability, access control, and audit logging on top of agents built with any framework or LLM.
Its seven-layer control plane covers connectivity, LLM flexibility (swap models without rewriting), simulation/testing, observability, hallucination and PII guardrails, governance, and audit trails, plus a no-code Agent Studio, a multi-agent orchestrator called SuperFlow, and 200+ pre-built production-tested agents for common enterprise workflows. Pricing is usage-based per agent run — $0.08 on Lyzr's managed cloud or $0.03 for on-premise/VPC deployment, with LLM and compute costs billed separately, and no free tier. The company, led by founder and CEO Siva Surendira, raised a $100M Series B at a $500M valuation in July 2026 — notably using its own AI agent to help run part of the fundraising process — after an earlier $8M Series A backed by Accenture, and reports 500+ enterprise customers with over 1,000 agents live in production.
💬 Our review
The short version: Lyzr isn't for someone building their first AI agent — it's for an enterprise that already has agents in development and needs the compliance, observability, and governance layer to actually deploy them at scale in regulated industries like BFSI, healthcare, and insurance.
Against LangChain, CrewAI, or the cloud providers' own agent platforms (AWS Bedrock, Azure AI), Lyzr's pitch is that it works on top of agents built with any of those frameworks rather than replacing them — a governance and observability layer rather than another framework to lock into. The reported 85% completion rate from prototype to production in roughly 8 weeks is a genuinely useful benchmark if accurate, since "agent stuck in pilot purgatory" is a common enterprise complaint. The clear trade-offs: there's no free tier at all, pricing assumes you already have production-scale agent workloads to justify per-run costs, and the documentation doesn't exhaustively list every supported framework beyond the major ones named. For an enterprise with compliance requirements and existing agent development underway, it's a serious governance layer worth evaluating; for a startup or individual still prototyping their first agent, it's solving a problem you don't have yet.
💰 Pricing
📊 Global score
🤖 AI-enriched data
Cloud géré : 0,08$ par exécution d'agent. VPC/sur site : 0,03$ par exécution. Coûts LLM et calcul facturés séparément. Aucun palier gratuit.
Pros
Fonctionne par-dessus n'importe quel framework/LLM existant plutôt que d'en imposer un nouveau
Contrôle de gouvernance en 7 couches : garde-fous anti-hallucination, PII, audit
200+ agents pré-construits et testés en production, 500+ clients entreprise
Cons
Aucun palier gratuit — tarification pensée pour une échelle de production déjà établie
Documentation ne liste pas exhaustivement tous les frameworks/LLM compatibles
Pas adapté à une équipe qui prototype encore son premier agent