Improve LLM performance and reliability through real-time feedback loops and user observability, helping product teams monitor AI interactions in production.
LangWatch
Stress-test and monitor AI agents in production with automated evaluations, real-time observability, and agent simulations to ensure reliable system performance.
Free plan available
€59/mo
About LangWatch
LangWatch is a comprehensive AI engineering platform designed to help teams bridge the gap between building AI prototypes and deploying reliable production-grade agents. It functions as a reliability layer that provides end-to-end visibility into large language model (LLM) interactions. The platform allows developers to move beyond manual testing by implementing a continuous quality loop that encompasses experimentation, agent simulations, and real-time monitoring. By centralizing these processes, it aims to prevent hallucinations and ensure that complex AI systems behave predictably in diverse real-world scenarios. The platform operates through several core modules including prompt management, automated evaluations, and observability traces. Users can version and compare prompt changes with full traceability, utilizing feature-flag-style controls for safe rollouts. For agentic AI, LangWatch offers sophisticated agent simulations that run thousands of synthetic conversations to uncover edge cases and failures. It also integrates a "Human-in-the-loop" workflow, allowing domain experts and users to annotate data and provide feedback, which can then be converted into "golden datasets" for future regression testing and fine-tuning. LangWatch is tailored for AI engineering teams, data scientists, and product managers who are developing complex agentic systems. It is particularly valuable for industries where reliability and security are paramount, such as enterprise software, healthcare, or financial services. Because it supports both cloud-hosted and self-hosted deployments, it caters to highly regulated organizations that require strict data privacy and sovereignty. The platform is designed to be accessible to both technical engineers using Python/TypeScript SDKs and non-technical domain experts who can interact via a user-friendly web interface. What distinguishes LangWatch from standard observability tools is its deep focus on agentic behavior and its optimization layer. It doesn't just track metrics; it utilizes DSPy for systematic prompt and pipeline optimization. Its ability to handle multimodal voice agents and multi-turn conversations makes it more robust than traditional LLM monitoring solutions. Furthermore, it is native to OpenTelemetry and offers flexible deployment options including VPC and air-gapped environments, ensuring teams maintain control over their data while adhering to SOC2 and ISO 27001 standards.
LangWatch pros & cons
Pros
- Supports a wide range of frameworks including LangChain, CrewAI, and DSPy.
- Offers self-hosted and air-gapped deployment options for high-security enterprise needs.
- Provides automated LLM-as-judge evaluations alongside collaborative human labeling.
- Integrates natively with OpenTelemetry to prevent vendor data lock-in.
- Features a free Developer tier that includes up to 50,000 monthly logs.
Cons
- Data retention on the free Developer plan is limited to only 14 days.
- Standard Launch plan limits data access to a 30-day or 180-day window depending on the contract.
- Access to enterprise features like SSO and audit logs requires a custom high-tier plan.
LangWatch use cases
- AI Engineers can run batch experiments and use the Optimization Studio with DSPy to systematically improve prompt performance.
- Product Managers can use the analytics dashboard and topic detection to monitor user feedback and identify common failure modes.
- Security Officers at regulated firms can deploy the platform on-premises to ensure sensitive data never leaves their private VPC.
- Domain Experts can participate in the quality loop by labeling production traces and verifying the accuracy of RAG-based responses.
LangWatch features
- prompt management
- pii redaction
- llm observability
- automated evaluations
- human-in-the-loop labeling
- multi-agent graph tracing
- dspy optimization
- agent simulations
LangWatch pricing
Is LangWatch free? Yes, LangWatch has a free plan, and paid plans start at €59 / month.
Developer
Free
- 50,000 logs per month
- 14 days data access
- 2 users
- 3 Scenarios and Simulations
- 3 custom evaluations
- Community Support
- All platform features
Launch
€59 / month
- 200,000 logs per month
- 180 days data access
- Up to 3 users
- Unlimited evaluations
- Unlimited optimizations
- Private Slack / Teams support
- Pay-as-you-go additional usage
Enterprise
Price varies
- Self-hosted or hybrid deployment
- Custom data retention
- Custom SSO and RBAC
- Audit logs
- Uptime & Support SLA
- ISO27001 reports
- Dedicated Support Engineer
LangWatch FAQs
Can I host LangWatch on my own infrastructure?
Yes, LangWatch offers self-hosted, VPC, and air-gapped deployment options for enterprise customers who need full control over their data and privacy.
Which AI frameworks does LangWatch support?
It integrates with major frameworks including LangChain, LangGraph, CrewAI, Agno, Mastra, and Pydantic AI, as well as any LLM via Python and TypeScript SDKs.
How does LangWatch help with AI hallucinations?
The platform provides automated evaluations and agent simulations to stress-test responses and uses 'LLM-as-judge' metrics to identify and prevent inaccuracies before they reach production.
What security certifications does LangWatch have?
LangWatch is ISO 27001 and SOC2 certified, and it is fully GDPR compliant, offering enterprise-grade controls like role-based access and audit logs.
Does LangWatch offer a free tier?
Yes, the Developer plan is free and includes 50,000 logs per month, basic evaluations, and community support without requiring a credit card.
Ratings & reviews
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