Introduction to optiml
optiml is the control layer for production AI workflows, designed to simplify the deployment and management of multi-step AI processes. Instead of manually stitching together providers, routing logic, evaluations, and fallback infrastructure, teams can deploy complex AI workflows behind a single endpoint. With support for text, image, voice, and vision across nine major AI providers, optiml enables companies to build, test, and scale AI applications with greater speed, flexibility, and reliability.
The product provides a unified platform that allows users to version their workflows, run A/B experiments, and monitor performance in real time. It also includes built-in features such as automatic rollback, streaming, and per-step observability, making it easier for teams to manage AI pipelines in production environments. Whether you're building chatbots, image analyzers, or multi-modal applications, optiml offers the tools needed to streamline AI operations without vendor lock-in.
Takeaways
- Control layer for production AI workflows
- Deploy multi-step workflows behind one endpoint
- Supports text, image, voice, and vision across nine providers
- Built-in versioning, experiments, and automatic rollback
- Enables A/B testing and per-node observability
- Facilitates cost tracking and model comparison
- Integrates seamlessly with existing systems
How optiml Works
optiml operates by allowing users to define and deploy AI workflows through a visual canvas. Each workflow consists of multiple AI steps, which can be connected via nodes and edges. These steps can include tasks like text generation, image creation, speech processing, and more. Once deployed, each workflow is assigned a versioned API endpoint, enabling seamless updates and rollbacks without changing the integration point.
The system supports dynamic routing between AI models and providers, with rules based on error rates, latency, or quality thresholds. Users can also split traffic between different versions of a workflow for A/B testing and evaluate results using built-in metrics like latency, cost, and quality scores. Every request is traced step-by-step, providing detailed insights into performance and costs at each stage of the workflow.
Core Benefits and Applications
| Benefit | Description |
|---|---|
| Faster Development | Reduce development time by deploying workflows in minutes instead of weeks |
| Provider Flexibility | Switch between AI providers easily without rewriting code |
| Cost Efficiency | Track and optimize costs per node and per workflow |
| Improved Reliability | Use automatic rollback and error handling to ensure uptime |
| Enhanced Observability | Monitor every step of the workflow with detailed traces |
| Scalability | Scale from individual projects to enterprise-level deployments |
Key Features
- Versioned Endpoints: Every deployment creates a new version, allowing for safe rollbacks and experimentation
- A/B Experiments: Split traffic between versions to compare performance and outcomes
- Eval Gates: Set conditions for workflow promotion based on structural checks, format validation, and AI-graded rubrics
- Streaming Support: Enable real-time token streaming over SSE for interactive applications
- Conversation State Management: Maintain context across multiple interactions
- Multi-Modal Workflows: Chain text, image, voice, and vision tasks in a single pipeline
- Provider Agnosticism: Work with OpenAI, Anthropic, Gemini, Mistral, and more
- Per-Step Tracing: View detailed execution traces including latency, cost, and output per node
