TL;DR

• What changed: Promptic is now open to everyone. You can create an account and start using the platform without waiting for an invite.
• What it is: Promptic is the optimization platform for GenAI applications.
• Why it matters: GenAI teams need higher-quality outputs at lower cost, measured against their own data instead of generic benchmarks.
• How it works: Benchmark models, tune prompts and agents, improve tool use, and optimize quality, cost, and latency together.
• What to ship: The prompt, model, agent architecture, extraction workflow, or tool configuration that performs best for your use case.

Today we are opening Promptic to everyone.

The first wave of GenAI products proved that teams can build useful AI features quickly. The harder part is turning those features into systems that produce better answers, fail less often, and cost less to run.

That is what Promptic is built for.

Promptic is the optimization platform for GenAI applications. It helps teams benchmark models, tune agents, optimize prompts, compare configurations, and ship the version that improves quality while reducing cost on their own data.

Why We Built Promptic

Most GenAI teams eventually run into the same problem: the product works, but nobody can say with confidence which version works best.

One model is cheaper but less reliable. One prompt looks cleaner but fails edge cases. One agent design is more capable but too slow. One extraction workflow improves recall but breaks the required schema. One tool selection strategy looks smart in a demo but wastes calls in production.

Manual prompt edits and one-off test runs are not enough for those decisions.

GenAI teams need an optimization loop:

• Define the goal in terms of quality, cost, latency, structure, or a business metric.
• Use real examples from the workflow you are trying to improve.
• Compare candidates across prompts, models, agent paths, extraction schemas, and tool choices.
• Ship the winner with the evidence to explain why it won.

Promptic gives teams that loop in one place.

What You Can Do Now

Improve quality and reduce cost

The best GenAI configuration is rarely the most expensive one. It is the configuration that reaches the quality bar with the least unnecessary work.

Promptic helps teams make that tradeoff explicit:

• Improve output quality by testing prompts and agent behavior against representative examples, expected outputs, and evaluators.
• Reduce model spend by comparing cheaper models, shorter prompts, and leaner agent paths against the same quality bar.
• Cut wasted calls by identifying tool choices, retries, and agent steps that do not improve the final result.
• Control latency by seeing which configuration gets to an acceptable answer fastest.

The goal is not to spend more tokens for a slightly nicer demo. The goal is to find the configuration that delivers production-grade quality at the lowest practical cost.

Optimize every layer of the GenAI stack

Promptic is not just a prompt editor.

The platform is designed to optimize the parts of a GenAI workflow that actually determine production performance:

• Model selection: compare providers and models for the same task.
• Prompt tuning: improve instructions against representative examples.
• Agent architecture: test how different workflows behave under realistic tasks.
• Information extraction: optimize for structure, recall, precision, and schema validity.
• Tool selection: improve when and how agents call tools, including tools discovered from an MCP server.
• Objective tradeoffs: balance quality, cost, and latency instead of optimizing one metric blindly.

That is why the homepage says "Make GenAI perform." Performance is not a single score. It is the fit between your data, your quality bar, your cost target, and the outcome your application needs.

Move from data to a shipped configuration

The workflow starts with the material your team already has: examples, expected outputs, traces, evaluation criteria, and production constraints.

From there, Promptic helps you build a repeatable path:

1. Bring in the use case data that represents the workflow.
2. Define the KPIs that matter, including quality targets and cost limits.
3. Run experiments against candidate prompts, models, or agent configurations.
4. Compare results across quality, cost, and latency.
5. Deploy the best-performing prompt and retrieve it safely from your application.

The important part is the last step. Optimization is only valuable if it leads to a decision your team can ship.

Use tracing as the vehicle, not the destination

Tracing matters because optimization needs data.

Promptic captures the behavior of LLM calls, tool calls, and agent steps so your team can understand what happened and use that evidence in experiments. But tracing is the vehicle. The destination is a higher-quality, lower-cost GenAI system.

That distinction matters. We are not building a dashboard where traces go to sit. We are building the platform that turns GenAI behavior into better configurations.

Let your coding agent help close the loop

Promptic also gives your coding agent the context it needs to participate in optimization: inspect results, compare experiments, and propose implementation changes backed by evaluation data.

That is where GenAI engineering gets more useful. The tools that help you build AI software should also help you improve the AI software you already shipped.

Who It Is For

Promptic is for teams that need GenAI to perform in production:

• Product teams shipping AI features into customer-facing workflows.
• Engineering teams optimizing quality, reliability, latency, and cost.
• Data and AI teams comparing prompts, models, evaluators, and agent designs.
• Founders and operators who want measurable improvement without building an optimization stack from scratch.

If you have moved beyond a prototype and now need a higher-quality, lower-cost version of a workflow, Promptic is built for you.

What Makes the Launch Different

Opening the platform changes the way we can work with users.

Before, we were learning from a smaller set of teams and workflows. Now, anyone can bring their own use case and start improving quality, reducing cost, and optimizing the parts of the GenAI stack that matter most to them.

The product is intentionally practical. You can start with a small dataset, one AI component, and a few quality checks. You do not need to redesign your application before Promptic is useful, and you do not need to accept "the prompt feels better" as a release process.

Start Optimizing

You can start from the dashboard or the docs:

• Create an account and set up an AI Application.
• Follow the dashboard quickstart to set up your first AI Application and AI Component.
• Run a prompt optimization experiment to improve a concrete task.
• Compare models across quality, cost, and latency.
• Optimize agents against representative datasets and evaluators.
• Improve MCP tool selection for tool-calling workflows.
• Use tracing as the data layer for optimization.
• Deploy the winner to your application.

We are opening Promptic because every GenAI team should be able to answer the same basic question:

Which configuration performs best for this workflow?

Now you can benchmark it, optimize it, and ship the version that wins.