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Cut your infra bill by 90%.
Then keep it there.

Our codeflash-agent finds optimizations across your codebase — memory, latency, throughput, cost. Our senior performance engineers review and ship every one. ROI guaranteed.

Book a call 90% cost cut at Unstructured · Case study →
Gartner® Cool Vendor™ 2025 · SOC 2 Type 2 · Unstructured · Roboflow · Pydantic · Hugging Face · vLLM
Proof

The outcomes we deliver.

90% infra cost cut
$10K → $1.1K/mo · 9.2× pod density · zero regressions
Read case study →
Vision models faster on GPU
YOLOv8, RF-DETR, SAM3 speedups
[CONFIRM exact numbers]
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3× speedup
OSS library powering thousands of pipelines
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13.7× faster token decoding
Merged to upstream · PR #20413
View PR →
9× faster WAN encoding
Merged to upstream · PR #11665
View PR →
34% speedup
16 PRs merged · zero regressions
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The problem

You are paying for code that isn't pulling its weight.

01

Most companies are far behind the perf engineering frontier.

Google, NVIDIA, Cloudflare and Linux have deep perf engineering expertise. Most companies struggle to prioritize it, so it shows up as a cloud bill that can be cut by 40–90%.

02

Agentic coding makes it worse, not better.

We've found 118 functions up to 446× slower than necessary in just two AI-written PRs. Read the analysis →

03

The payoff is real money.

A 90% infra cut lifts gross margin, extends runway, and frees capital for hiring and growth. This is the cleanest dollar a CFO will see this year.

Introducing codeflash-agent

An autonomous performance engineer.

codeflash-agent is an expert performance engineer that runs 24/7, in parallel, with the whole codebase in view. It finds global optimizations a human working file-by-file would miss, and superhuman optimizations humans might fail to discover. Every change is benchmarked faster, proven correct, and delivered as a reviewable PR.

"Codeflash is the team of performance engineers that we don't have."

Crag Wolfe · Chief Architect, Unstructured
Read the launch announcement →
codeflash-agent · run #284
// exploring unstructured-io/core
▸ profiling pdf_extract.py
▸ found: _patch_current_chars
  O(N²) scan per operator
▸ hypothesis 1/10: single-pass rewrite
▸ generating tests... ok (42)
▸ benchmark: 14.08× faster
▸ verifying correctness... ok
▸ opening PR #443 → review

// next bottleneck detected
▸ PIL preprocessing: 24MB/req creep
Understands abstractions
Rewrites 6-step flows as 3-step flows, not just inline tweaks.
Verifies correctness
Every change is checked against your existing tests and auto-generated regression tests.
Runs in sandbox
Isolated environment. Your code is never used to train models.
Reviewable PRs
Benchmark numbers and rationale attached to every PR.
Process

How an engagement runs.

01

Scope

Pick the objective: cost, p99 latency, cold start, GPU utilization. We reproduce your baseline on a representative workload.

02

Optimize

codeflash-agent explores autonomously in a sandbox. Humans don't drive it; they steer it.

03

Review

Our performance engineers audit every optimization. Only PRs that pass our quality bar reach your team.

04

Continuous

We setup our agent to optimize every new PR. New code starts optimal.

Continuous Optimization

The bill stays down.

Most optimization wins decay inside a year, because new code arrives faster than anyone can optimize it. Continuous Optimization closes that loop: the same agent that cut your bill stays on, reviewing every new PR and catching regressions where they're introduced. Integrates with Claude Code, Cursor and GitHub, so the code your team ships is optimal on the first commit.

See Continuous Optimization →
[ PR timeline visual: PR opened → agent benchmark → suggested patch → merge ]
Specialization
[ Stack grid: PyTorch · JAX · ONNX · vLLM · Diffusers · YOLO · RF-DETR · SAM3 · PaddleOCR · spaCy ]

Specialized in ML performance.

We've shipped wins across inference, training, and data processing, through GPU optimization, custom CUDA kernels, and algorithmic rewrites. Including state-of-the-art vision models like RF-DETR and SAM3, and inference frameworks like vLLM and Hugging Face Diffusers. If you run models in production, we've probably optimized your exact stack.

See ML case studies →
Security & privacy

Built for enterprise from day one.

SOC 2 Type 2
Annual audit, continuous controls.
Never trained on your code
Not ours, not third-party. Ever.
Your deployment, your choice
SaaS, your cloud, or on-prem. Same product.
Sandboxed execution
No production access, no exfiltration paths.
See our security posture →
"It wasn't just tweaks to existing functions. Codeflash understood the abstractions. If a process had six steps, it would recognize that two or three were unnecessary and replace the flow with a better one. That requires really deep understanding of what the code is actually doing."
Crag Wolfe · Chief Architect, Unstructured

How much is your team paying for slow code?

The first call is a 20-minute diagnostic. We'll tell you where the waste is and what we'd target.

Book a call Start free →