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Case studySee how an automotive team did in 2 days what took them 2 months by handRead the study →
rapidfire.

Test and optimizeyour AI agents.

Run many combinations of your agentic applications at once, across data preprocessing, retrieval, workflow structure, prompts, and models, on the same resources. Spot what works early, cut what doesn't, and get to much better eval metrics, much faster.

Integrations & ecosystem

  • OpenAIModel provider
  • ClaudeModel provider
  • GeminiModel provider
  • MistralModel provider
  • QwenModel provider
  • DeepSeekModel provider
  • Hugging FaceFramework
  • PyTorchFramework
  • RayInfrastructure
  • MLflowTracking

With RapidFire, your team can

start wide,

Launch hundreds of agent versions at once, on the resources you already have.

converge on what works,

Weak runs stop early. The strongest get cloned, and new variants launch mid-flight.

and optimize.

Land on the few versions that clear your quality bar, at the cost you set.

How it works

More experiments. Less time. Lower cost.

Step 01

Go wide.

Launch hundreds of combinations of data preprocessing, retrieval schemes, prompts and models on the same resources.

Typical eval · a handful

RapidFire · hundreds

Step 02

Run them all at once.

All combinations run side by side, with results displayed early and incrementally.

Typical eval · one after another

Version 1Version 2Version 3Version 4
First result · 1 version

RapidFire · all at once, same hardware

First results · every version
Time →Early resultsMore results
Step 03

Cut the losers. Clone the winners.

Weak runs stop as soon as results turn. The strongest get cloned and ramped up, with new variants launched while the experiment runs.

Losers stopped · winners cloned

Compute spent

Run everything to the end
RapidFire

Automation without the black box.

Set hard limits on time and spend, and goals for the metrics you care about. RapidFire works inside them, and every stop and clone is explained in the audit log.

Hard limitsNever exceed the time or budget you set.
Soft goalsRank the metrics to optimize for, in order.
Audit logEvery decision and result, explained.

Trade-offs

See every trade-off. Pick your operating point.

Every version is scored on every metric, so quality, cost, and latency sit side by side. From your current setup (O), move to better quality for the same cost (A), or the same quality for less (B).

Change your priorities and pick a new winner without re-running anything.

Compare against

Evidence

Real results. Real scale.

RetrievalGlobal automotive manufacturer

2 days

vs. 2 months by hand

5 of 7

metrics beat the best existing pipeline

A factory-floor knowledge assistant across four production domains. 432 configurations in one run, on the vector database the team already used, and reliable in every domain where both existing pipelines broke down in at least one.

Read the study →
Fine-tuningFortune 500 enterprise tech company

2,000+

fine-tuning configurations

5–6 days

per sweep before, for a few dozen

A self-hosted small model, kept in-house to protect customer data. Same four GPUs, from a few dozen configurations per sweep to more than 2,000.

Read the study →
“It is not apples to apples, because we were not able to try these many configurations before.”
AI team at a Fortune 500 enterprise tech company

What you can test

Every lever. Tested at scale.

Data preprocessing and retrieval

Test variations of chunking, indexing, retrieval, reranking, etc. This is where most gains come from.

Quality score · a closed and an open model tie at the top

Models and prompts

Try any closed or open models in the same experiment, with any prompt variations.

Tools and agentic structure

Compare agentic structures and tool use, from workflow-like to fully autonomous.

Explore use cases →

Get started

See RapidFire in action.

Get a walkthrough of the workflow, results, and controls relevant to your agent.