This blog was co-authored by Dave Stone, Jack Christie, and Kirk Crenshaw.
When it comes to hype, few innovations in tech history rival the frenzy surrounding generative AI (GenAI). Companies are scrambling to harness its potential, with buzz fueled by giants like OpenAI and Meta and hyperscalers like AWS and Azure. Large language models (LLMs) are constantly in and out of the spotlight, with OpenAI’s GPT-4o getting praise one day, then Meta’s Llama 3.2 edging it out the next, and then challengers like DeepSeek’s R1 suddenly changing the game—and organizations feel pressure to keep up with all of it. However, in doing this, they risk fixating on tools that solve immediate needs instead of building a future-ready AI foundation.
This hyper-focus on model performance often leaves businesses with overwhelming choices. Gartner predicts that 30% of GenAI projects will be abandoned after proof-of-concept as teams struggle to move from pilot to production. The real challenge isn't finding the “best” model—it's creating a flexible GenAI foundation that can keep pace with the relentless progression.
Flexibility is the key to avoiding dead-end investments and ensuring your organization can pivot as technologies and priorities shift. In the sections ahead, we'll explore how a flexible GenAI foundation can empower businesses to navigate the complexities of inferencing, from managing diverse use cases to scaling solutions seamlessly.
Failing to Adapt to Evolving Models
The GenAI landscape constantly changes, with new capabilities and proficiencies emerging rapidly. Yet many organizations need help implementing models that best fit their unique constraints, such as budget, security, and control requirements. The focus shouldn't be on universally choosing “the perfect model” but on ensuring your organization can seamlessly deploy the right model for the job.
Key pitfalls to address include:
- Rigid Infrastructure: Systems that are hard to modify or expand hinder the ability to leverage new AI advancements.
- Budgetary Constraints: Cost-driven solutions often sacrifice flexibility, leaving businesses unprepared for rapid innovation.
- Security and Compliance Challenges: Deploying models without robust safeguards increases risk, especially in heavily regulated industries.
- Vendor Lock-in: Over-reliance on a single provider can stifle agility and make it harder to adopt better options as they emerge.
By avoiding these traps and prioritizing a flexible architecture, businesses can rapidly integrate the latest advancements while staying aligned with their unique requirements.
Building a Strong Foundation for GenAI
So, how can businesses create a foundation that supports flexibility and growth? The answer lies in data. High-quality, well-organized data is the cornerstone of any successful AI strategy. Effective AI applications, especially those involving inferencing, are built on a robust and scalable data infrastructure. Organizations can unlock GenAI by prioritizing clean, structured, and secure data while ensuring their systems remain adaptable to future advancements.
A robust, data-driven foundation positions businesses to evolve with technology's relentless pace. To future-proof AI efforts, organizations should focus on three core elements:
Center Your Strategy Around Open-Source Data Management:
An open-source data foundation enables flexible workflows and adaptable data pipelines, allowing organizations to leverage the full breadth of their data for GenAI applications. By avoiding vendor-specific constraints, companies can seamlessly integrate new tools and models to respond quickly to shifting market demands.
Build Systems with Interchangeable Components for Agentic AI:
Swappable AI components—from data sources to pipelines to embedding models—ensure adaptability. This modularity creates more flexible systems and better prepares them for future advancements (e.g. the sudden emergence of DeepSeek’s R1, which provides better performance than GPT-4o for a fraction of the cost). This modular approach is also the key to unlocking the dynamic model swapping required for Agentic AI, where autonomous, purpose-built GenAI agents reason, plan, and adapt their behavior in real time based on feedback and objectives.
Treat Observability and Compliance as Design Tenets:
As AI regulations evolve, embedding traceability, data masking, and observability into AI systems is no longer optional—it’s a strategic necessity. These features ensure accountability, enhance confidence in outcomes, and streamline troubleshooting. Additionally, organizations prioritizing visibility gain a competitive edge through faster innovation, more informed decision-making, and the agility to pivot in response to new challenges or opportunities.
EDB Postgres AI and Griptape: A future-proof solution for GenAI
Together, EDB Postgres AI and Griptape deliver a comprehensive solution for enterprise-scale GenAI applications. Using an intuitive point-and-click interface, teams can quickly deploy intelligent agents—from Slack chatbots to custom applications that transform how work gets done. Here's how this partnership addresses the critical needs of adaptable AI infrastructure:
- Controlled, enterprise-scale GenAI: With a strong data foundation in Postgres, EDB Postgres AI and Griptape provide a robust environment for managing large datasets and complex workflows while ensuring compliance and governance. Retain complete control of sensitive data with Griptape's Off-Prompt™ capabilities, task memory, comprehensive observability features, role-based access control, and flexible deployment options—including private cloud and on-premises.
- Streamlined infrastructure: Griptape simplifies AI system management with a modular, open-source framework, enabling companies to integrate seamlessly with existing processes and reduce resource requirements for GenAI deployment.
- Stronger GenAI value: Open source flexibility across EDB Postgres and Griptape supports model and component swaps, empowering businesses to take technological shifts in stride. And this doesn’t just mean an organization can optimize their single GenAI chatbot agent with a better model. It means they can deploy a whole workforce of autonomous AI agents that can handle text, images, video, and other data types while dynamically scaling and adapting to business needs.
- Faster time to market: Griptape’s intuitive framework and EDB Postgres AI’s SQL-based data orchestration enable developers to deploy and scale GenAI applications without extensive retraining or upskilling. Plus, once the foundation is in place, a point-and-click interface for AI agent deployment enables even non-technical team members to build GenAI applications, accelerating development and powering repeatable innovation.
This joint solution gives organizations the tools to stay at the cutting edge of an AI-driven world. Key features include:
- Swappable configurations: Easily switch between models, integrate multiple data modalities, and select from various storage locations—in the cloud or on-prem—enabling tailored performance and efficient data management that adapts to any need across your business.
- Painless data prep: Griptape's open-source framework integrates directly with robust vector storage in EDB Postgres AI. Connect to any data source and use automated AI data processing pipelines to extract, transform—including cleaning, chunking, embedding, and adding metadata—and load it into a vector database index.
- Automated pipelines: Use Griptape's ready-made retrieval patterns, customize them, or compose your own from scratch for modular retrieval-augmented generation (RAG). Use EDB Postgres AI to automatically fetch data from Postgres or object storage, generate vector embeddings as new data is ingested, and trigger updates to embeddings when source data changes.
- Turnkey AI agents: Griptape provides clean abstractions for building GenAI agentic jobs, systems of agents, pipelines, workflows, and RAG implementations without having to learn GenAI or prompt engineering. Make these applications even more potent with semantic search across text and images from EDB Postgres AI — 4.22x faster than purpose-built vector databases.
- Comprehensive security and observability: Monitor AI systems directly in Griptape Cloud or integrate with the EDB Postgres AI Hybrid Control Plane for a unified view of all operational and AI data in Postgres. Gain insights into performance, reliability, and costs while ensuring robust security, access controls, monitoring, data masking, and alerting to meet the demands of AI and traditional workloads alike.
- Sovereign AI deployments: Deploy scalable Sovereign AI applications as microservices through Griptape Cloud, across major cloud platforms like AWS, GCP, and Azure, and even on-premises or hybrid. Flexible deployment maximizes operational resilience, cost savings, and scale.
Enter the AI-driven future with confidence.
In the rapidly changing AI landscape, businesses need more than single-use GenAI solutions; they need a flexible, secure, and scalable foundation that supports evolving needs. With Griptape and EDB Postgres AI, organizations can stay ahead of the curve, adapt quickly, and ensure sustainable growth through adaptable AI architecture.