Fly.io deployment hurdles for LLM inference

Fly.io runs application containers as micro-virtual machines across 18 global regions. Developers often select this platform for multi-region deployments and low-latency needs without managing raw cloud infrastructure. However, Fly.io imposes a CLI-first workflow with a minimal dashboard, which frustrates teams preferring visual interfaces for monitoring or access control. The platform also lacks a deep managed services ecosystem for queues, object storage, and background workers when compared to established PaaS providers.

The pricing model at Fly.io relies on usage-based billing without fixed monthly caps, making cost forecasting difficult for production workloads. You should note that Fly.io bills compute, bandwidth, storage, and multi-region replicas separately.

Teams moving away from AWS Lambda or Google Cloud Run for machine-learning inference workloads encounter specific hardware constraints. AI-driven applications often require high memory bandwidth to handle the iterative nature of LLM decoding. While Fly.io requires Docker-based workflows, which creates a steep learning curve for non-Docker teams, it provides the compute necessary for distributed applications.

Scaling LLM workloads

Scaling LLM serving functions like a distributed operating system because demand grows with test-time compute and compound LLM systems. Charlotte Qi from Meta notes that LLM inference is expensive because the process involves a prefill stage and a decode stage. The prefill stage is compute heavy, while the decode stage depends heavily on memory bandwidth. To manage these resources, engineers use tensor parallelism to partition weights across multiple GPUs on one host.

A 70B model cannot fit in one GPU and requires at least two GPUs to avoid running out of memory. For a 405B model, the weights exceed 800GB under bf16, necessitating two nodes and pipeline parallelism to reduce communication overhead. Users might find that running a 405B model remains slower than a 70B model unless they modify the hardware.

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Alternatives for AI-native teams

Render targets developers building AI-native, multi-service applications. Render raised $100M in Series C financing in February 2026, bringing total funding to $257M. It provides Git-based deployment, automatic SSL, and a visual dashboard. Render serves as an alternative for small teams needing fast deployments, though it lacks multi-cloud or BYOC options.

Upsun serves enterprises requiring multi-cloud deployment across AWS, GCP, Azure, OVHcloud, and IBM Cloud. Upsun allows teams to define code and infrastructure in a single YAML file and clones full environments, including live data, in under one minute. It provides a managed services catalog including PostgreSQL, MySQL, Redis, and Kafka.

Koyeb also competes for AI workloads by offering Tenstorrent AI accelerators. Koyeb provides two instance types:

• TT-N300S: 24GB GDDR6 memory, 192MB SRAM, and 466 FP8 TFLOPS.
• TT-LoudBox: 96GB GDDR6, 768MB SRAM, and 1,864 FP8 TFLOPS.

Koyeb scales applications to hundreds of servers and provides a low-latency experience for AI workloads through its serverless nature. Does the move to specialized AI accelerators solve the cost unpredictability of existing PaaS providers?