Portkey vs. Liminal

LMOps is a comprehensive stack designed for launching production-ready applications that facilitate monitoring, model management, and additional features. Portkey serves as an alternative to OpenAI and similar API providers. With Portkey, you can efficiently oversee engines, parameters, and versions, enabling you to switch, upgrade, and test models with ease and assurance. You can also access aggregated metrics for your application and user activity, allowing for optimization of usage and control over API expenses. To safeguard your user data against malicious threats and accidental leaks, proactive alerts will notify you if any issues arise. You have the opportunity to evaluate your models under real-world scenarios and deploy those that exhibit the best performance. After spending more than two and a half years developing applications that utilize LLM APIs, we found that while creating a proof of concept was manageable in a weekend, the transition to production and ongoing management proved to be cumbersome. To address these challenges, we created Portkey to facilitate the effective deployment of large language model APIs in your applications. Whether or not you decide to give Portkey a try, we are committed to assisting you in your journey! Additionally, our team is here to provide support and share insights that can enhance your experience with LLM technologies.

Liminal is a secure AI enablement platform that becomes the place AI happens inside a regulated organization. Employees reach models from Anthropic, OpenAI, Google and other providers through one governed interface, and protection is applied inside the prompt before anything leaves the organization. That design answers a tradeoff most organizations are stuck in. Restrict AI access and employees route around it, taking the exposure somewhere nobody can see. Allow open access and regulated information flows into third-party systems with no record of what went where. Liminal makes the compliant path the convenient one, so there is nothing to route around. Protection happens inside the prompt. Before text leaves the organization, sensitive terms are recognized and handled according to rules the organization defines by data category: replaced with tokens, removed entirely, flagged for the user, or stopped outright. Tokens are swapped back when the response returns, so the employee reads something coherent while the provider never receives the underlying information. Nothing has to be scrubbed by hand. Model choice stays open. Policy-based routing directs traffic without anyone configuring controls provider by provider, and there are no separate contracts or security reviews for each vendor added. Everything is recorded as it happens: which person, which model, which rules fired, what was caught. Auditors and examiners get evidence produced at the moment of the interaction rather than assembled afterward, and the same records can feed existing security tooling so AI activity sits alongside everything else being reviewed. Internal systems connect through MCP, letting people bring real organizational context into their work without copying data into unmanaged tools. Existing permissions carry through. Infrastructure is single-tenant. Authentication runs through SSO with role-based authorization. Organizations in banking, insurance, healthcare, education, g