Current compute models no longer meet the needs of highly dynamic applications. While dedicated servers provide efficiency and always-on availability, they often lead to over-provisioning, scaling challenges, and operational overhead. Serverless computing improves this with auto-scaling and pay-as-you-go pricing, but can suffer from cold starts and inefficient use of idle time. It’s time for a new, balanced approach.
Fluid compute evolves beyond serverless, trading single-invocation functions for high-performance mini-servers. This model has helped thousands of early adopters maximize resource efficiency, minimize cold starts, and reduce compute costs.
Fluid compute is a new model for web application infrastructure. At its core, Fluid embraces a set of principles that optimize performance and cost while establishing a vision for meeting the demands of today’s dynamic web
Compute triggers only when needed
Real-time scaling from zero to peak traffic
Supports advanced tasks like streaming and post-response processing
Billing based on actual compute usage, minimizing waste
Existing resources are used before scaling new ones
Pre-warmed instances reduce latency and prevent cold-starts
All with zero configuration and zero maintenance overhead.
Fluid delivers measurable improvements across a variety of use cases, from ecommerce to AI applications. Its unique execution model combines serverless efficiency with server-like flexibility, providing real benefits for modern web applications.
Vercel Functions with Fluid compute prioritize existing resources before creating new instances, eliminating hard scaling limits and leveraging warm compute for faster, more efficient scaling. By scaling functions before instances, Fluid shifts to a many-to-one model that can handle tens of thousands of concurrent invocations.
At the same time, Fluid mitigates the risks of uncontrolled execution that can drive up costs. Functions waiting on backend responses can process additional requests instead of sitting idle, reducing wasted compute. Built-in recursion protection prevents infinite loops before they spiral into excessive usage.
Fluid minimizes the effects of cold starts by greatly reducing their frequency and softening their impact. When cold starts do happen, a Rust-based runtime with full Node.js and Python support accelerates initialization. Bytecode caching further speeds up invocation by pre-compiling function code, reducing startup overhead.
Vercel Functions with Fluid compute extend the lifecycle of an invocation, enabling function executions to extend beyond when the final response is sent back to a client.
With waitUntil, tasks like logging, analytics, and database updates can continue to run in the background of a compute function to reduce time to response. For AI workloads, this means managing post-response tasks like model training updates without impacting real-time performance.
Vercel Functions with Fluid compute support a dense global compute model, running compute closer to where your data already lives instead of attempting unrealistic replication across every edge location. Rather than forcing widespread data distribution, this approach ensures your compute is placed in regions that align with your data, optimizing for both performance and consistency.
Dynamic requests are routed to the nearest healthy compute region—among your designated locations—ensuring efficient and reliable execution. In addition to standard multi-availability zone failover, for enterprise customers, multi-region failover is now the default when activating Fluid.
Vercel Functions run without proprietary code, ensuring full portability across any provider that supports standard function execution. Developers don’t need to write functions explicitly for the infrastructure—workloads are inferred and automatically provisioned.
With full Node.js and Python runtime support, including native modules and the standard library, Fluid ensures seamless compatibility with existing applications and frameworks—without runtime constraints.
Many of our API endpoints were lightweight and involved external requests, resulting in idle compute time. By leveraging in-function concurrency, we were able to share compute resources between requests, cutting costs by over 50% with zero code changes.
Modern applications require compute that is low-latency, efficient, and scalable. Fluid compute meets these demands by dynamically allocating resources and optimizing performance for AI, real-time personalization, and API workloads.
AI workloads require fast, scalable compute. Traditional serverless struggles with model inferences, leading to cost inefficiencies.
Fluid compute advantages:
Concurrency: Handles multiple AI inference requests per function
Background tasks: Return user results quickly while running post-response tasks
Pre-warmed instances: Reduces latency for real-time responses
Dynamic scaling: Accommodates traffic surges without cost spikes
Authentication, authorization, and personalization must happen instantly. Traditional serverless struggles with latency and cost inefficiencies for frequent middleware execution.
Fluid compute advantages:
Bytecode caching: Reduces latency for dynamic rendering
Authentication, authorization, and personalization must happen instantly. Traditional serverless struggles with latency and cost inefficiencies for frequent middleware execution.
Fluid compute advantages:
Understanding how Fluid compute compares to existing serverless solutions helps highlight its advantages in efficiency, scalability, and cost optimization.
Fluid compute is more than an optimization—it’s a fundamental shift in how modern applications scale and perform. By combining the flexibility of serverless with the efficiency of persistent compute, it sets a new path forward for web application compute.
As applications become more complex and traffic more unpredictable, the need for an intelligent, more efficient compute model has emerged.
Fluid compute is that model.
