Choosing the right path to build AI capabilities in your enterprise
The way you acquire AI – whether building in-house, leveraging open source solutions, or outsourcing to a specialist AI provider – requires careful alignment with your business goals, budget, risk tolerance, technical maturity, talent acquisition, and more. It’s not a decision to be taken lightly; the approach you choose can impact everything from how fast […]
Why OpenAPI by default matters
Read on to find out and discover how you can go OAS-native for secure, interoperable and governance-first API experiences.
API governance in the age of AI: Designing for human and machine consumers
As artificial intelligence becomes part of both our development teams and our end-users, API governance is entering a new era.
Seven steps to AI readiness
Many organizations struggle to turn AI potential into a secure and well-governed reality. That’s where our seven-step AI readiness guide comes into play – helping you move from AI planning to responsible implementation with ease.
Making sense of MCP: Why standardization matters in the AI supply chain
The future of enterprise AI relies on standardization. MCP and A2A protocols are laying the foundation, and Tyk is leading the charge with tools for secure interoperability.
API governance meets sustainability: Strategies for implementers
API governance can help build a sustainable IT ecosystem. By cutting waste, reducing emissions, and building resilient systems, your APIs can make a bigger impact than you think.
How to manage Kafka streams in the hybrid cloud
Managing Kafka across hybrid cloud environments is no walk in the park. Head of Engineering at Tyk, Leonid Bugaev shows how Tyk Streams makes it all simpler.
How to implement API governance without stifling developer creativity and agility
Is API governance a creativity killer? Done right, governance fuels agility and innovation for developers and customers alike. Find out why.
API governance vs developer autonomy – how can platform engineering strike the right balance?
How can platform engineering teams strike the right balance between API governance and DevOps?
The (potentially awful) future of APIM?
LLMs and AI agents are revolutionizing coding. But what does this mean for the future of API Governance and oversight?