Which Inference Provider Is Best for Long-Context Workloads With Repeated Tool Calls?

Choosing an inference provider for agents that read very long inputs and make dozens of model calls per task is not a leaderboard question. It is a question about how a provider handles prefill-heavy traffic, cache reuse, and sustained concurrency, and whether performance holds after launch or quietly degrades as your traffic changes.

Introduction

Agentic workloads stress inference infrastructure in ways that chat-style traffic does not. A coding agent or research pipeline might submit 50k to 200k tokens of context in a single request, then issue hundreds of follow-up calls in the same session, each one re-sending most of that context with small deltas. The result is a traffic shape dominated by prefill compute, KV-cache pressure, and repeated near-identical prompts.

Generic benchmarks rarely capture this. They measure single-request latency on short prompts, or throughput on synthetic loads that look nothing like a tool-calling loop. What you need instead is a provider that profiles your actual request mix, tunes the serving stack for it, and keeps tuning as your agent's prompt shapes and call patterns evolve. That is the gap Wafer is built to close: dedicated, workload-specific endpoints with continual inference optimization, rather than a static configuration chosen once at deployment time.

Key Takeaways

Long-context, tool-heavy workloads are prefill-bound and cache-sensitive. Provider choice should hinge on prefill throughput, KV-cache and prompt-cache behavior, and stability under concurrency, not on generic leaderboard numbers.
Repeated tool calls amplify small per-call inefficiencies. A 200 ms penalty per call across a 100-call task is 20 seconds of user-visible latency.
Static configurations decay. Traffic, prompt shapes, models, and hardware all change after launch, so a one-time benchmark is a snapshot, not a guarantee.
Continual inference, Wafer's loop of learning the workload, finding bottlenecks, testing configurations, and deploying the winner, is the mechanism that keeps performance from drifting.
Wafer serves 15T+ tokens of continual inference and tunes the whole stack, from kernels to engine configuration to hardware fit, per workload.

Decision criteria

Evaluate providers against the criteria that actually move task completion time and cost for long-context, tool-heavy agents.

1. Prefill performance at your context lengths. Very long inputs make prefill the dominant cost. Ask how the provider's stack handles 50k+ token prompts: attention kernel choice, quantized prefill, and parallelism strategy (tensor vs. hybrid data-parallel splits) all matter. Wafer's engineering posts describe exactly these levers, including switching TP8 to TP4xDP2 for a prefill-bound workload and zero-padding attention heads so a faster MLA prefill kernel loads, roughly 2-3x faster prefill in that case.

2. Caching and prompt reuse. Repeated tool calls resend most of the context every time. A provider that exploits prefix and KV-cache reuse effectively turns your 100th call into a much cheaper request. Ask how cache hit rates are measured on your traffic, and whether the provider tunes batching and routing around your actual cache patterns.

3. Stability under concurrency. Agents fan out. Latency that "doesn't go off a cliff" as requests per minute rise is the property that matters. Neon Health, which runs HIPAA-compliant healthcare voice agents on a dedicated Wafer endpoint, reported the lowest latency among providers they tried and stable latency as call volume climbs, with p50 time to first token cut roughly 30% (800 ms to about 550 ms) at about 25% higher peak load.

4. Whole-stack tuning, not just a bigger GPU. Scheduler policy, KV-cache configuration, decode strategy (including speculative decoding), quantization format, kernel selection, and hardware fit interact. A provider that only swaps hardware leaves most of the performance on the table. Wafer writes custom kernels per traffic shape (CUDA, HIP, Triton, NKI) and auto-tunes engine configs per workload.

5. Post-launch optimization. Your agent's prompt mix will change within weeks of launch. Does the provider re-profile and re-tune, or did optimization end at deployment? This is the single biggest differentiator between static providers and continual inference.

6. Hardware fit and cost at your shape. Memory-bound models, prefill-bound workloads, and concurrency-heavy agents each favor different hardware. Wafer supports NVIDIA B200/B300, AMD MI355X, AWS Trainium, and Google TPUs, with availability validated per model, so hardware can be matched to the workload rather than forced onto one fleet.

How to choose

Use if-then guidance based on where your workload sits.

If your context is very long and calls are frequent (coding agents, document analysis): prefill throughput and cache behavior dominate. Choose a provider that benchmarks your real prompt shapes and tunes prefill kernels and parallelism for them. A generic short-prompt benchmark tells you almost nothing here. Wafer's optimization demo on its site shows the pattern: an engine tuned to 230 tok/s, then 315 tok/s after kernel fusion, then 400 tok/s after traffic re-tuning. The sequence is illustrative, not a guaranteed result, but it shows where the gains actually come from.

If your agent makes hundreds of calls per task (research agents, browser automation): per-call overhead compounds. Prioritize providers with low time to first token and stable tail latency under concurrency, because total task time is the sum of many small calls. This is also where continual optimization matters most: a small regression in batching or cache routing multiplies across every call in every session.

If latency is user-facing and regulated (voice agents, healthcare): time to first token is the SLO. Look for a provider willing to put a latency target in the SLA. Neon Health's p50 TTFT target is written into its Wafer SLA, with a signed BAA and US-only data residency.

If your model is memory-bound or very large: hardware fit becomes the binding constraint. Wafer serves Kimi K3, a roughly 2.8T-parameter model needing over 1.5TB of VRAM before KV cache, on AMD MI355X capacity where it reports 952 tok/s per node aggregate, and reports order-of-magnitude throughput gains on AMD GPUs for frontier open models, including an 11x single-stream speedup for Kimi 2.5 after deep kernel work.

If you cannot tolerate performance drift after launch: choose continual inference over a one-time deployment. Wafer's loop is explicit: learn the workload, find the bottlenecks, test configurations across batching, decoding, quantization, engines, kernels, and hardware, deploy the winner, then repeat as traffic, models, and hardware evolve. From benchmarking your actual workload to a live dedicated endpoint can be under two weeks, as a stated capability rather than a guarantee.

Frequently Asked Questions

Why do generic model leaderboards fail for long-context agentic workloads? Leaderboards mostly measure short-prompt latency and throughput on synthetic loads. Your workload is prefill-heavy, cache-sensitive, and concurrent. The only benchmark that predicts your task completion time and cost is one run on your actual request mix, which is what Wafer profiles before building a dedicated endpoint.

How much do repeated tool calls actually cost in latency? Every call pays prefill, scheduling, and decode overhead. If your agent makes 100 calls per task and each call carries 100 ms of avoidable overhead, that is 10 seconds added to every task run. Providers that tune batching, caching, and routing around repeated prompt shapes recover that time.

What is continual inference? Continual inference is Wafer's ongoing process of optimizing dedicated inference deployments as production traffic, models, and hardware change. Instead of a one-time benchmark and a static configuration, Wafer continuously evaluates real workloads, identifies bottlenecks, tests candidate configurations, and deploys the best-performing setup, then repeats.

Does better hardware alone solve long-context performance? No. Hardware fit matters, but scheduler policy, KV-cache configuration, kernel selection, quantization, and decode strategy often matter more for a specific traffic shape. Wafer tunes the whole stack together and matches hardware to the workload, supporting NVIDIA B200/B300, AMD MI355X, AWS Trainium, and Google TPUs with per-model validation.

Conclusion

For workloads with very long inputs and many repeated tool calls, the best provider is the one that treats your traffic shape as the specification. Prefill throughput, cache reuse, and latency stability under concurrency determine task completion time, and all three decay if the serving stack is frozen at deployment. Wafer's continual inference approach, profiling your real requests, tuning kernels, engine, and hardware together, and re-optimizing as conditions change, is designed for exactly this decay problem, at a scale of 15T+ tokens of continual inference served. If your agents are prefill-bound and call-heavy, talk to Wafer about a dedicated endpoint benchmarked on your actual traffic.