Prompt optimization
Optimize prompts against labeled examples, evaluators, and business metrics. Promptic supports multi-variable prompts and structured chat-message templates so experiments can match the format used in an application.
Promptic
Freemium
Promptic is a GenAI optimization platform for evaluating models, improving prompts and agents, and selecting configurations against business metrics. It combines OpenTelemetry-based tracing with datasets, benchmarks, and optimization workflows for teams building AI applications.
What is Promptic?
Promptic helps teams evaluate and improve AI applications using representative data, expected outputs, and business metrics. It covers model comparison, prompt tuning, agent evaluation, information extraction, and tool-selection workflows.
Users can benchmark candidate configurations for quality, cost, and latency, inspect production behavior through OpenTelemetry traces, and retrieve or deploy a validated configuration from application workflows.
What can Promptic do?
Model comparison
Benchmark different foundation models on the same task and compare quality, inference cost, and latency to identify a configuration that fits the project’s priorities.
Agent evaluation
Evaluate complete agents and compare their outputs, artifacts, trajectories, and architectures, including workflows that use routing, retrieval, or tools.
OpenTelemetry tracing
Capture LLM calls, tool calls, and workflow steps with provider-agnostic spans. Traces can include request and response payloads, token counts, cost, and latency for debugging and optimization.
Structured-output evaluation
Score only the output fields that matter, with per-field inclusion, weighting, required-field handling, strategy overrides, and configurable LLM-judge behavior.
Developer integration
Use the Python SDK, REST API, CLI, or documented dashboard workflows to instrument applications, manage experiments, and retrieve optimized prompts or configurations. The SDK supports first-class tracing for OpenAI, Anthropic, Google, LangChain, and Cohere.
Use Cases
“Selecting a model for a production task”
Run the same evaluation dataset across candidate models and weigh output quality against inference cost and latency before choosing what to deploy.
“Improving a support or workflow agent”
Compare complete agent architectures, inspect failing traces and spans, and validate a revised implementation against fixed cases before shipping it.
“Tuning extraction and structured responses”
Optimize prompts for documents or other structured tasks, then evaluate important output fields individually so optional or noisy fields do not obscure meaningful regressions.
“Improving tool use in an agent”
Evaluate how reliably a model selects tools from an MCP server and use trace evidence to investigate incorrect routing, retries, or other tool-use failures.
Frequently Asked Questions
What can Promptic optimize?
Promptic can optimize prompts, compare models, evaluate agent architectures, improve information-extraction workflows, and optimize model tool selection. The documentation also describes support for multi-variable prompts, chat-message prompts, and structured-output evaluation.
How does an optimization workflow work?
Users provide representative inputs and expected outputs, define the metrics that matter, and benchmark candidate prompts, models, or architectures against the same dataset. Promptic surfaces differences in the results and helps select a configuration based on the chosen trade-offs.
How does tracing work?
Promptic uses OpenTelemetry-based instrumentation to capture LLM calls, tool calls, and workflow steps. Trace data can include payloads, token counts, cost, and latency, and can then be used for debugging, dataset creation, and optimization.
Can Promptic be integrated into an existing application?
Yes. Promptic documents a Python SDK, REST API, CLI workflows, and dashboard-based workflows. The Python SDK provides drop-in initialization for tracing and supports OpenAI, Anthropic, Google, LangChain, and Cohere integrations.
Is model usage included in the subscription?
The pricing page distinguishes Promptic Credits from model or token usage. Users can bring their own model keys, while managed model usage is optional on applicable plans and billed separately; enterprise terms are customized.
Quick Facts
- Category
- AI developer and evaluation platform
- Primary workflow
- Benchmark, optimize, validate, and ship GenAI configurations
- Observability
- OpenTelemetry-based LLM and agent tracing
- Developer access
- Python SDK, REST API, CLI, and dashboard
- Model access
- Bring your own model keys or use optional managed model billing on applicable plans
- Pricing
- Free tier, paid Team and Business plans, and custom Enterprise terms
Promptic Traffic Analysis
Traffic data is for reference only.
- Domain Rating
- 3
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