Route each request to the model most likely to deliver the right quality, latency, and cost
Who should use this?
High-volume multimodel applications and Teams with evaluation datasets.
Who should avoid it?
Low-volume single-model applications, Teams without representative quality labels
What problem does it solve?
Not Diamond combines pretrained and custom model routing with cross-model prompt optimization, but its value depends on representative evaluation data and measured end-to-end savings.
Would I recommend it?
Not Diamond earns a pilot for teams with meaningful inference volume, varied requests, and enough evaluation discipline to train and monitor routing. Keep a deterministic fallback, test on held-out traffic, separate provider savings from router charges, and refuse deployment if a cheaper average hides unacceptable failures in a critical segment.
Advisor score
Premium review framework
Not Diamond combines pretrained and custom model routing with cross-model prompt optimization, but its value depends on representative evaluation data and measured end-to-end savings.
Not Diamond earns a pilot for teams with meaningful inference volume, varied requests, and enough evaluation discipline to train and monitor routing. Keep a deterministic fallback, test on held-out traffic, separate provider savings from router charges, and refuse deployment if a cheaper average hides unacceptable failures in a critical segment.
Build a stratified set of at least 500 recent production requests with blinded human scores and hard constraints for safety, format, tool use, latency, and cost. Compare a fixed strong model, a fixed economical model, pretrained routing, and a custom router on a held-out set. Measure quality by segment, catastrophic misses, router overhead, provider failures, fallback behavior, total spend, drift after model updates, and the staff effort needed to maintain labels and policies.
Personal Recommendation
Not Diamond earns a pilot for teams with meaningful inference volume, varied requests, and enough evaluation discipline to train and monitor routing. Keep a deterministic fallback, test on held-out traffic, separate provider savings from router charges, and refuse deployment if a cheaper average hides unacceptable failures in a critical segment.
Try the recommendation
Pretrained and custom routing paths
Overall Score
Editorial Review Framework
Who should use this?
High-volume multimodel applications, Teams with evaluation datasets, Cost and latency optimization.
Who should avoid it?
Low-volume single-model applications, Teams without representative quality labels
What problem does it solve?
Not Diamond combines pretrained and custom model routing with cross-model prompt optimization, but its value depends on representative evaluation data and measured end-to-end savings.
Would I recommend it?
Not Diamond earns a pilot for teams with meaningful inference volume, varied requests, and enough evaluation discipline to train and monitor routing. Keep a deterministic fallback, test on held-out traffic, separate provider savings from router charges, and refuse deployment if a cheaper average hides unacceptable failures in a critical segment.
Overall Score
8.0
Ease of Use
8.0
AI Quality
8.0
Features
8.4
Speed
8.0
Integrations
8.0
Value for Money
7.8
Customer Support
7.6
Learning Curve
7.6
Recommended Because…
Pretrained and custom routing paths
Scores use a 0-10 editorial scale. The source data is maintained as 5-point review dimensions, then normalized for reader-friendly comparison.
Reusable trial worksheet
Turn this review’s buyer test into evidence. Your entries autosave only in this browser and are never added to shared shortlist links.
Confirm the tool meets every must-have workflow and stakeholder requirement.
Review starting point: High-volume multimodel applications; Teams with evaluation datasets; Cost and latency optimization
Run the same representative work you would use in production; do not score a polished demo.
Review starting point: Complete three to five representative tasks with known acceptable outcomes and compare them with your current process.
Calculate the effective cost per accepted result, including usage, review, corrections, and required add-ons.
Review starting point: Not Diamond's live pricing page describes a free developer starting tier and usage-based access, with enterprise options for larger or controlled deployments. Routing does not erase the underlying model-provider bill, and prompt optimization incurs separate model work. Because included volume and unit rates can change, model the router, optimization, and…
Define an acceptance threshold, test known answers and edge cases, and record every correction.
Review starting point: Editorial quality signals: features 4.2/5; AI quality 4.0/5. Validate these signals in your own work.
Verify what data enters the product, who can access it, how long it is retained, and whether it trains models.
Review starting point: Use approved low-risk data first. Check roles, consent, deletion, subprocessors, model-training settings, and the contract—not only the marketing page.
Test the real handoffs, permissions, failure states, and export path your team depends on.
Review starting point: OpenAI-compatible messages, OpenRouter, Python, TypeScript, REST API, Custom models
Record training, governance, reliability, accessibility, ownership, and change-management risks before rollout.
Review starting point: Adds a production dependency; Savings depend on evaluation quality; Routing and provider costs must be modeled together
Loading saved worksheet… · private to this device or your optional account
Evaluation
Research-based
Price posture
From $0/month
Reviewed
2026-09-06
No authentic product screenshot is published for this review. DiscoverAI does not use generated interface images as product evidence.
Not Diamond's live pricing page describes a free developer starting tier and usage-based access, with enterprise options for larger or controlled deployments. Routing does not erase the underlying model-provider bill, and prompt optimization incurs separate model work. Because included volume and unit rates can change, model the router, optimization, and provider costs together from the live pricing page. Reviewed September 6, 2026.
Free plan: Yes. A free developer path is available for bounded routing and optimization experiments; confirm current included requests and provider charges.
Editorial freshness
Pricing and material product claims were checked September 6, 2026.
Community evidence
Structured, editor-moderated experience—not star ratings. This complements our independent review and never changes its score.
No approved community evidence yet. Be the first verified user to contribute.
Not Diamond selects models for individual requests and can optimize prompts for different target models using evaluation examples.
It is primarily a routing and optimization layer. Buyers still need to account for the models and provider paths used to generate responses.
Yes. Its API documents custom-router training from prompts plus comparable response and score columns for candidate models.
Compare held-out quality, latency, failures, fallback costs, router fees, and provider spend against a fixed-model baseline by workload segment.
Keep Deciding
Best AI Coding Tools
AI coding assistants ranked by real developer workflow fit, not demo magic.
Best AI Tools for Code & Development
A practical shortlist of AI tools for ai coding assistants, code generators, and developer tools.
Best AI Tools for Data & Analytics
A practical shortlist of AI tools for ai-powered data analysis, visualization, and business intelligence tools.
Code & Development
AI coding assistants, code generators, and developer tools.
Material changes only
Get an occasional email when something decision-relevant changes. This is separate from the weekly newsletter.
See how similar tools stack up
An open-source and managed gateway for model routing, reliability, observability, and governance
Portkey centralizes model access, fallbacks, caching, guardrails, keys, budgets, and traces, but a gateway becomes a critical data and availability boundary that needs failure testing.
Route models and MCP tools through one observable, policy-controlled gateway
Orq.ai combines model and MCP routing, observability, budgets, redaction, governance, and managed agents, with unusually explicit usage pricing but several independent meters to model.
An open-source AI gateway with request monitoring, cost tracking, caching, fallbacks, prompts, and evaluations
Helicone combines multi-provider routing and observability behind a familiar API, but proxy trust, logged payloads, retention, usage-based costs, and gateway dependency need careful architecture review.