Not Diamond

Building with AI models often means making an uncomfortable choice: use the most capable model for everything and accept the bill, or use cheaper models and risk inconsistent results. Not Diamond takes a different approach by intelligently selecting the right model for each input, helping engineering teams maintain strong output quality without sending every task to the most expensive option.

The platform is designed around intelligent model routing, with a particular focus on coding agents and production AI workloads. Instead of forcing developers to manually decide which model should handle each request, its routing system predicts which available model is best suited to the task.

This is especially useful for teams working with multiple leading language models. A simple coding request does not necessarily require the same model as a difficult debugging problem, and treating every request identically can quickly increase inference costs. The routing approach addresses that inefficiency while keeping the existing development stack largely intact.

The company currently highlights accuracy improvements of 5% or more, cost savings of 20% or more, and faster development cycles of up to 2x. These figures are presented as platform-level claims, so actual results will naturally depend on the workload, models, and routing setup used by each team.

Key Features

• Intelligent routing that predicts the most suitable model for each input.
• Support for production-grade coding agent workloads.
• Integration through a secure API rather than requiring teams to rebuild their existing infrastructure.
• Compatibility with different model providers and development stacks.
• Custom routing options based on a team's own data and requirements.
• Prompt optimization designed to improve accuracy and adapt prompts to changing models.
• Support for Python, TypeScript, and REST API integrations.
• Designed to reduce unnecessary inference spending while preserving output quality.
• Protection against excessive dependence on a single AI model vendor.

User Interface

The experience is primarily designed for developers and engineering teams rather than casual users looking for a conventional chatbot interface. The workflow revolves around APIs, routing configuration, model selection, and integration with existing AI infrastructure.

For a technical team, this is actually an advantage. There is no need to introduce another consumer-facing chat application into the workflow. Instead, the routing layer can sit alongside the tools developers already use. The official documentation also provides quickstart paths intended to get an initial API request running within minutes.

Accuracy & Performance

The main appeal is the balance between model quality and operating cost. Rather than assuming that the strongest available model is automatically the best choice for every request, the routing system evaluates the input and selects a model according to the task.

The company reports accuracy gains of 5% or more and cost savings of 20% or more on its website. It also cites customer results, including a reported 39% improvement in average accuracy in Rootly's SRE benchmarks, with some use cases achieving more than double the previous performance.

These numbers should be viewed as reported results rather than guarantees. Still, the underlying idea is practical: different requests have different levels of difficulty, and intelligent model selection can prevent expensive models from being unnecessarily used for simpler work.

Capabilities

The platform goes beyond basic model switching. Developers can use a pre-trained router to get started quickly or build custom routers using their own data. This makes the system more interesting for teams whose workloads have specific performance requirements that generic benchmarks may not capture.

Prompt optimization is another important capability. Instead of treating prompts as fixed pieces of text, the system can optimize them to improve accuracy and help applications adapt when the underlying models change.

The API-first design also makes it suitable for teams with existing AI infrastructure. Python, TypeScript, and REST API support give developers several ways to integrate routing into applications, agents, and internal systems.

Security & Privacy

Security is an important consideration when routing AI requests through an additional infrastructure layer. The platform states that it is SOC 2 and ISO 27001 compliant and offers custom zero-data-retention policies for sophisticated AI teams.

Its integration model is also designed to work through a secure API and existing model gateways or agent harnesses. This approach can make adoption easier for organizations that already have established AI infrastructure and do not want to replace it simply to introduce model routing.

Teams handling sensitive workloads should still review the applicable data-retention, security, and contractual terms for their particular deployment before sending production data through any third-party AI infrastructure.

Use Cases

• Coding Agents: Automatically select suitable language models for different programming tasks, from straightforward code generation to more demanding reasoning and debugging.
• AI Development Platforms: Reduce the complexity of supporting multiple model providers behind a single application.
• Enterprise AI Applications: Control inference expenses while maintaining the quality expected from production systems.
• Customer Support AI: Route simple requests to efficient models while reserving stronger models for complex conversations.
• AI Research Workflows: Test different models and routing strategies against real application workloads.
• High-Volume Applications: Improve the economics of applications where inference costs become significant as usage grows.
• Multi-Model Systems: Reduce dependence on a single provider by intelligently working across different leading language models.

Pros and Cons

• Pros: Intelligent model selection, potential inference cost reduction, production-focused architecture, custom routing options, prompt optimization, multiple API integration options, and strong enterprise security credentials.
• Cons: The product is primarily aimed at developers and engineering teams, so it may feel unnecessarily technical for casual AI users. Pricing is not presented as a simple public self-service plan, and the actual savings or accuracy improvement will depend heavily on the workload.

Pricing Plans

Public pricing is not presented as a conventional fixed subscription table on the current website. The service is positioned toward engineering organizations and production AI teams, with prospective customers encouraged to discuss their requirements directly with the company.

This model makes sense for organizations with substantial inference workloads because the value of intelligent routing can vary considerably from one deployment to another. A company processing millions of requests will have very different requirements from a small development team experimenting with a few models.

Teams interested in using the platform should contact the provider for pricing and deployment details based on their expected usage, model providers, workload, and support requirements.

How to Use the Intelligent Model Router

1. Create an account through the provider's application.
2. Generate an API key.
3. Choose the routing quickstart or prompt optimization workflow.
4. Connect the API to the existing application, model gateway, or coding-agent environment.
5. Send requests through the routing layer rather than manually selecting a model for every input.
6. Evaluate the resulting quality, latency, and inference costs against the existing setup.
7. For specialized workloads, consider training a custom router using relevant application data.

Comparison with Similar Tools

Traditional multi-model systems often leave model selection to developers. A team might manually decide that one provider is best for coding, another for reasoning, and a third for lower-cost requests. That can work, but the rules eventually become difficult to maintain as new models appear.

A conventional single-model integration is simpler, but it can also create unnecessary costs and increase dependence on one provider. The routing approach sits between these two extremes: applications can work with multiple models while an intelligent layer decides which one should handle an individual request.

Another distinction is the focus on real production workloads. Rather than presenting model routing simply as a way to compare AI models, the platform is built around integrating recommendations into the model gateway or harness that an engineering team already uses.

For a developer building a small personal application, manually selecting a model may still be perfectly reasonable. For a larger AI product where inference spending and output quality directly affect the business, automated routing becomes considerably more compelling.

Conclusion

Choosing an AI model for every request is becoming increasingly difficult as the number of capable models continues to grow. The strongest model is not always necessary, and the cheapest model is not always sufficient. Intelligent routing offers a practical way to make that decision dynamically.

For engineering teams running coding agents or other production AI workloads, this approach can provide a useful layer between applications and model providers. The combination of model routing, custom optimization, API-based integration, and enterprise-oriented security makes the platform particularly relevant to teams that care about both performance and infrastructure costs.

The biggest benefit is not simply having access to more models. It is being able to use those models more intelligently. When implemented against the right workload, that can turn model selection from a manual engineering decision into an automated part of the AI stack.

Frequently Asked Questions (FAQ)

What does the platform do?

It intelligently routes individual AI inputs to the model it predicts will provide the best balance of quality and cost. It is particularly focused on coding agents and production AI workloads.

Can developers build custom routers?

Yes. In addition to its pre-trained router, the platform provides the ability to train custom routers using a team's own data.

Does it support APIs?

Yes. The platform supports Python, TypeScript, and REST API integrations, making it possible to connect the routing layer to different development environments.

Can it help reduce AI costs?

Cost reduction is one of its primary goals. The company reports savings of 20% or more, although actual savings depend on factors such as the models being used, request volume, and workload characteristics.

Is it suitable for enterprise AI applications?

It is designed with production-grade workloads in mind and states that it is SOC 2 and ISO 27001 compliant. Custom zero-data-retention policies and 24/7 support are also offered for sophisticated AI teams.