SwarmOne.ai

Continuous AI inference optimization for agentic workloads.
Simulate real workloads.
Optimize every layer. Cut costs 17×

[SwarmOne.ai @swarmoneai]

DeepSeek is building a custom inference chip. OpenAI has Jalapeño. Google has TPUs. Amazon has Inferentia.
Every major AI company is designing silicon to own inference economics.
But custom chips take years to reach production scale. The inference cost problem exists TODAY. Right now. This quarter's budget.
SwarmOptimizer works on the hardware you already have. Any NVIDIA GPU, any cloud, any endpoint. Autonomous tuning, production-ready, deployed this month - not in 2028.

[SwarmOne.ai @swarmoneai]

Every inference provider publishes tokens/second, throughput and TTFT.
Almost none of them publish what happens to those numbers when KV-cache pressure builds across a real multi-turn agent session.
@baseten @digitalocean @togethercompute @novita_labs @DeepInfra @FireworksAI_HQ @SambaNovaAI @cerebras @nebiustf @wafer_ai

your benchmark pages show metrics at fixed context lengths. 1K. Maybe 10K.
Agentic coding sessions - Claude Code, Cursor, Windsurf - routinely hit 100K-400K tokens per session with cache that invalidates on every file edit.
SwarmSimulator defeats prefix cache by design. Every replay uses pre-poisoned recordings so you measure real inference, not warm-cache theater.
We built this for neo-clouds, chip manufacturers, endpoint providers and whom ever that wants to know what their stack actually does - not what it does under ideal conditions that NEVER happen in production.

[SwarmOne.ai @swarmoneai]

A chatbot API call: $0.001.
A multi-step agent that plans, retrieves, calls tools, reflects, and self-corrects: $0.10 to $1.00.
Gartner confirmed it - agentic workloads use 5-30X more tokens per task. At enterprise scale, that's the difference between a manageable line item and a board-level conversation.

[SwarmOne.ai @swarmoneai]

OpenAI engineers reportedly found a software optimization that halves inference costs.
Tested on: logged-out ChatGPT traffic. Short sessions. Single-turn. No tool calls.
Agentic workloads - multi-turn, 500K+ context, concurrent tool calls, growing KV-cache - look NOTHING like logged-out ChatGPT. SwarmOptimizer tunes specifically for YOUR workloads, not an average across someone else's traffic. A 50% improvement on the wrong workload profile is a 0% improvement on yours.

[SwarmOne.ai @swarmoneai]

NVIDIA says Vera Rubin + Groq 3 LPX delivers 35X inference throughput per megawatt and 10X more revenue opportunity.
Great hardware. Genuine question: who's tuning the software layer on top of it?
New silicon doesn't auto-optimize batching strategies, KV-cache eviction policies, or request routing under mixed agentic workloads.
SwarmOptimizer takes any hardware - H100, A100, L40S, Rubin, whatever ships next - and autonomously tunes the deployment until it's actually running at the performance the hardware promised.

[SwarmOne.ai @swarmoneai]

Some Fortune 500 companies are now reporting monthly AI inference bills in the tens of millions.
Twelve months ago, most of them hadn't forecasted this.
A single multi-step agent task - planning, retrieval, tool calls, self-correction - costs 100-1,000X a chatbot query.
Scale that across an enterprise and the math gets ugly fast.
SwarmOptimizer continuously simulates your workloads and cuts cost 17x. The CFO shouldn't be the one discovering your token budget.

[SwarmOne.ai @swarmoneai]

SwarmOptimizer tunes the full stack autonomously. A cheap model on unoptimized infrastructure is still an expensive deployment.
Now available on @awscloud at
https://t.co/SbUzvv529f

[SwarmOne.ai @swarmoneai]

Kimi K3: $3/M input tokens. Open-weight. 2.8T parameters.
Fable 5 charges roughly 3x that. Sol is in the same range.
The model layer is getting commoditized. But the model is one line item on the invoice.

[SwarmOne.ai @swarmoneai]

Serving infrastructure, request routing, cache management, hardware selection, concurrency tuning - that's where the real cost compounds at scale.

[SwarmOne.ai @swarmoneai]

@togethercompute @novita_labs @DeepInfra @FireworksAI_HQ @SambaNovaAI @cerebras @nebiustf If you're publishing TTFT measured at 10K context, you're publishing a number that doesn't exist in production.

[SwarmOne.ai @swarmoneai]

NVIDIA is splitting prefill and decode across different silicon. Rubin GPUs for prefill, Groq LPUs for decode.
Right architecture. Wrong benchmarking.

[SwarmOne.ai @swarmoneai]

@togethercompute @novita_labs @DeepInfra @FireworksAI_HQ @SambaNovaAI @cerebras @nebiustf SwarmSimulator already benchmarks disaggregated pipelines under recorded agentic workloads. Neo-clouds and chip manufacturers use it to validate architecture decisions before committing silicon.

[SwarmOne.ai @swarmoneai]

Inference costs dropped 95% in two years.
Enterprise AI spending grew 5x in the same window.
Cheaper tokens X exponentially more tokens = bigger bills. This is the agentic cost trap. Per-token pricing fell. Per-task costs exploded.
SwarmOptimizer measures what you actually spend per task, per workflow, per agent chain - then tunes across every layer to bring it down. 17X cost reduction in STAC Research-measured tests.
Cheaper tokens don't help if nobody's optimizing how many you burn.

[SwarmOne.ai @swarmoneai]

https://t.co/SbUzvv529f

[SwarmOne.ai @swarmoneai]

OpenAI just unveiled Jalapeño - their first custom inference chip with Broadcom.
"Substantially better performance per watt."
Interesting. But a chip doesn't fix a misconfigured serving stack. Custom silicon with default batching, naive routing, and untuned KV-cache management is still burning money.
SwarmOptimizer doesn't care what chip you run. It tunes the deployment layer - batching, caching, routing, concurrency - where most of the waste actually lives.
The silicon is one layer. The optimization is another. Now on @awscloud

[SwarmOne.ai @swarmoneai]

Gartner says 40% of agentic AI projects will be canceled by 2027.
The #1 killer: escalating costs.
Not model quality. Not talent gaps. The inference bill.
Agentic workloads consume 5-30x more tokens per task than chatbots.
Most teams budget for the chatbot number, then ship agents. SwarmOptimizer simulates your real traffic patterns and tunes your deployment before the bill arrives. The teams that survive the 40% will be the ones that saw production costs before production.

[SwarmOne.ai @swarmoneai]

Kimi K3 just hit #1 on Arena's frontend code leaderboard. 2.8T parameters. Open-weight.
Now every inference provider on @openrouter is going to race to deploy it.
@togethercompute @novita_labs @DeepInfra @FireworksAI_HQ @SambaNovaAI @cerebras @nebiustf
Honest question: what are your published TTFT and throughput numbers measured at?
1K tokens? 10K?

Real agentic workloads run at 500K-1M tokens with growing KV-cache, multi-turn tool calls, and hundreds of concurrent sessions.

SwarmSimulator replays recorded Claude Code and Cursor sessions against your endpoint. Not synthetic loads. Not 1K context. The actual workload your users will generate.
Your published metrics and your production metrics are two different numbers. We measure the second one.

[SwarmOne.ai @swarmoneai]

A big part of surviving the agentic era is recognizing that your cost problem is mostly a configuration problem. SwarmOptimizer finds what your stack leaves on the table.