vLLM and SGLang reduce vendor moat

The core issue is that performance tricks are turning into commodity software faster than managed inference vendors can monetize them. Techniques that once required a specialized serving team, like smarter KV cache handling, continuous batching, prefix reuse, and speculative decoding, now ship inside open engines such as vLLM and SGLang, and even inside packaged enterprise runtimes like NVIDIA NIM. That lifts the default speed and lowers the amount of clear technical alpha a provider like Wafer can claim.

• vLLM moved the baseline early with PagedAttention, which lets operators pack more requests onto the same GPU by reducing KV cache waste. Its docs now expose continuous batching, prefix caching, chunked prefill, and speculative decoding as standard features rather than custom optimization work.
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• SGLang pushed the same baseline higher from another angle. It centers on RadixAttention for automatic prefix sharing, and its docs also cover speculative decoding and attention backend tuning. That means an engineering team can get many of yesterday’s premium optimizations from open source before buying a managed layer.
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• NVIDIA NIM compresses the gap further by packaging optimized runtimes into containers that run across cloud, data center, and edge environments, including engines built on TensorRT, vLLM, and SGLang. At that point, a customer is often buying deployment convenience, not rare serving technology, unless workload specific gains are obvious and repeatable.
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The market is heading toward a thinner standalone inference layer. Durable winners will be the providers that keep finding gains in real customer traffic after open frameworks and vendor containers absorb the obvious tricks, then turn those gains into automation, routing, and procurement advantages that are harder to upstream.