# PromptLayer vs PromptMage

PromptLayer records 85,899 monthly visits with -4.6% month-over-month growth and Free Trial, Contact for Pricing pricing. PromptMage records 0 monthly visits with 0% growth and Free pricing.

| Metric | [PromptLayer](https://openaitools.app/tool/promptlayer) | [PromptMage](https://openaitools.app/tool/promptmage) |
| :--- | :--- | :--- |
| Website URL | https://promptlayer.com | https://promptmage.io |
| Categories | Prompt Engineering, Large Language Models (LLMs), AI Monitor, AI Developer Tools | AI Developer Tools, Large Language Models (LLMs), Prompt Engineering, AI Workflow |
| Pricing Model | Free Trial, Contact for Pricing | Free |
| Monthly Visits | 85,899 | 0 |
| MoM Growth Rate | -4.6% | 0% |
| Primary Market | United States | Global |
| Direct Visits | 14,092 | 0 |
| Review Score | 4.5 | 4.3 |
| Saved Count | 3 | 0 |
| Supported Platforms | Website | Website |

## PromptLayer

PromptLayer targets the gap between prototyping with an LLM API and running that application reliably in production. It packages the tooling teams usually end up building themselves: a prompt registry, a visual CMS for editing and versioning prompts, an evaluation harness, and an observability stack that ties production traffic back to the prompt versions that generated it.

The core insight is that prompt quality is a product concern, not only an engineering one. Because prompts live in a dashboard rather than scattered through a codebase, domain experts — product managers, marketers, curriculum designers, support leads — can edit and test them directly, while engineers keep control over what actually ships. Version history, diffs, comments and rollbacks keep that collaboration auditable.

Evidence comes from evaluation and production. Teams define datasets, run automated and human grading, set up regression tests that fire whenever a prompt changes, and backtest new versions against historical data. In production, traces connect each request to its prompt version, so quality, cost and latency can be compared side by side. Agent workflows get the same treatment: prompts, tool calls, evaluations and logs stay in one place for multi-step runs.

**Pricing & Plans:** PromptLayer has a free tier and three paid tiers. Free is $0/month with 5 users, 2.5k requests per month, 1 workspace, 250 eval cell executions and 10MB datasets. Pro is $49/month and adds unlimited playgrounds, unlimited workspaces and 150MB datasets. Team is $500/month with 25 users, 100k+ requests, 7.5k+ eval cell executions and 1GB datasets. Enterprise offers custom limits plus RBAC, deployment approvals, HIPAA with BAA, self-hosted or EU hosting and dedicated support. Overage is billed per transaction (about $0.003 on Pro, $0.002 on Team).

- **No-code visual prompt CMS:** Edit, version, diff and roll back prompts from a visual dashboard with no coding, so product, marketing and content teams can iterate on prompts while engineers keep control of the deploy.
- **Evaluations and regression testing:** Run dataset-backed tests with human and AI graders, trigger regression evals whenever a prompt changes, backtest new versions against historical data and compare prompts across models.
- **Observability and tracing:** Connect production traces to the exact prompt version that produced them and inspect failures, cost, latency and token usage by feature, model or user.
- **Agent workflow support:** Build, trace and improve agent workflows with prompts, tool calls, evaluations and logs in one place, so multi-step runs can be tested before they ship.
- **A/B testing and usage analytics:** Release new prompt versions gradually, compare live metrics, and see exactly how the LLM application is being used, by whom and how often, without jumping to a separate analytics tool.

**Limitations:** Team plan jumps to $500/month, which is steep for small teams that outgrow Pro; Usage beyond plan limits is billed per transaction, so heavy LLM traffic needs forecasting; Role-based access controls, deployment approvals and SSO are Enterprise-only; Live in the developer ecosystem, so setup expects an LLM API and engineering involvement

[Profile (Markdown)](https://openaitools.app/tool/promptlayer.md)

## PromptMage

PromptMage is an open-source Python framework built to simplify the development of complex, multi-step applications powered by large language models. Rather than treating prompts as throwaway strings scattered through code, it makes prompts first-class citizens with built-in version control, so teams can track changes, compare iterations, and collaborate on the exact wording that drives their application.

The framework centers on a workflow that mirrors how good LLM apps actually get built. You define and iterate on prompts inside an integrated playground, evaluate them with manual and automatic testing, and then expose the resulting logic through an automatically generated FastAPI backend. That combination means the same artifact you prototype in is the one you deploy, cutting the drift between experimentation and production.

PromptMage is designed as a self-hosted solution, which suits teams that want prompt and data handling to stay within their own infrastructure. It is aimed at both small teams and larger enterprises, and the project positions itself as a pragmatic answer to the gap in LLM workflow management. Because it is self-hosted and open source, there is no per-seat fee for the framework itself, though the LLM providers your application calls are billed separately. The project documentation carries an explicit warning that PromptMage is in alpha state and under active development, so APIs and features may change at any time.

**Pricing & Plans:** Free and open source. PromptMage is a self-hosted framework with no listed subscription or per-seat pricing; you pay only for the LLM APIs and infrastructure your application uses.

- **Integrated version control for prompts:** Treat prompts as first-class artifacts with built-in versioning, so you can track how each prompt evolves and collaborate with teammates without losing earlier iterations.
- **Interactive prompt playground:** Test, compare, and refine prompts in an intuitive playground built into your workflow, enabling rapid iteration rather than ad-hoc copy-paste testing.
- **Auto-generated FastAPI API:** Prompts and workflows defined in PromptMage are exposed through an automatically generated FastAPI backend for straightforward integration and deployment.
- **Evaluation and testing mode:** Assess prompt quality with manual and automatic testing before shipping, so you know how a change affects output reliability across scenarios.
- **Self-hosted deployment:** Run the whole framework on your own laptop or server, keeping prompts, workflows, and data inside infrastructure you control.
- **Typed, Pythonic building:** Type hints are used throughout for automatic inference and validation, reducing boilerplate when composing multi-step LLM logic.

**Strengths:** Free and open source, with no vendor lock-in; Self-hosted, so prompts and data stay in your own infrastructure; Bundles version control, a playground, testing, and API generation in one framework; FastAPI auto-generation removes boilerplate between prototype and deployment

**Limitations:** Explicitly labeled alpha, so the API and features may change without notice; Self-hosted only — you need to run and maintain the server yourself; Requires Python and LLM API experience to get real value; Documentation, while solid, is still maturing alongside the framework

[Profile (Markdown)](https://openaitools.app/tool/promptmage.md)

## Frequently Asked Questions

### How do PromptLayer and PromptMage compare in monthly traffic?

PromptLayer has 85,899 monthly visits, while PromptMage has 0. Traffic estimates describe audience size, not product quality.

### How do their growth rates compare?

PromptLayer has a month-over-month growth rate of -4.6%; PromptMage has 0%.

### How does pricing compare?

PromptLayer: Free Trial, Contact for Pricing. PromptLayer has a free tier and three paid tiers. Free is $0/month with 5 users, 2.5k requests per month, 1 workspace, 250 eval cell executions and 10MB datasets. Pro is $49/month and adds unlimited playgrounds, unlimited workspaces and 150MB datasets. Team is $500/month with 25 users, 100k+ requests, 7.5k+ eval cell executions and 1GB datasets. Enterprise offers custom limits plus RBAC, deployment approvals, HIPAA with BAA, self-hosted or EU hosting and dedicated support. Overage is billed per transaction (about $0.003 on Pro, $0.002 on Team). PromptMage: Free. Free and open source. PromptMage is a self-hosted framework with no listed subscription or per-seat pricing; you pay only for the LLM APIs and infrastructure your application uses.

## Alternatives

| # | Tool Name | Monthly Visits | MoM Growth Rate | Pricing Model | Summary | Profile (Markdown) |
| :--- | :--- | :--- | :--- | :--- | :--- | :--- |
| 1 | [DeepSeek](https://openaitools.app/tool/deepseek) | 372,761,591 | +4.3% | Freemium | High-efficiency open-weight and hosted reasoning AI powered by DeepSeek-V4 and R1 at ultra-low token costs. | [.md](https://openaitools.app/tool/deepseek.md) |
| 2 | [Ollama](https://openaitools.app/tool/ollama) | 9,421,973 | +4.3% | Free, Freemium, Paid | Run and build with open AI models locally or in the cloud | [.md](https://openaitools.app/tool/ollama.md) |
| 3 | [Dify](https://openaitools.app/tool/dify-ai) | 819,691 | -15% | Freemium | Build, deploy, and govern production-ready agentic AI workflows | [.md](https://openaitools.app/tool/dify-ai.md) |
| 4 | [Open WebUI](https://openaitools.app/tool/open-webui) | 804,534 | -2.8% | Freemium | Self-hosted AI workspace for local and cloud models | [.md](https://openaitools.app/tool/open-webui.md) |
| 5 | [Weights & Biases (CoreWeave Forge)](https://openaitools.app/tool/prompts) | 315,624 | -80.2% | Freemium, Contact for Pricing, Free | Track, evaluate, and ship AI models and agents in one connected environment. | [.md](https://openaitools.app/tool/prompts.md) |
| 6 | [Arize AI](https://openaitools.app/tool/arize-com) | 249,028 | +3.2% | Paid, Contact for Pricing | Agent observability, evaluation, and continuous improvement for production AI | [.md](https://openaitools.app/tool/arize-com.md) |
| 7 | [Agno](https://openaitools.app/tool/agno) | 140,302 | -3.3% | Contact for Pricing, Free | Self-driving agent platform you run in your own cloud | [.md](https://openaitools.app/tool/agno.md) |
| 8 | [Future AGI](https://openaitools.app/tool/future-agi) | 121,741 | -7.8% | Freemium, Usage-based | Build, simulate, evaluate and monitor self-improving AI agents | [.md](https://openaitools.app/tool/future-agi.md) |
| 9 | [Maxim AI](https://openaitools.app/tool/maxim-ai) | 121,351 | +10% | Contact for Pricing, Free Open Source | Evaluate, monitor, and govern your AI applications in production | [.md](https://openaitools.app/tool/maxim-ai.md) |
| 10 | [Confident AI](https://openaitools.app/tool/confident-ai) | 83,400 | -3.3% | Freemium, Paid, Contact for Pricing | Standardize LLM evaluation, observability, and red teaming across your whole org | [.md](https://openaitools.app/tool/confident-ai.md) |

---
Source: https://openaitools.app/compare/promptlayer-vs-promptmage

More Markdown: [Categories](https://openaitools.app/categories.md) · [Traffic rankings](https://openaitools.app/rankings.md) · [llms.txt](https://openaitools.app/llms.txt)