Multi-model routing and fallback gateway

For

Product and platform teams running AI features in production

Solves

Teams pick one model per feature, overpay for simple prompts, and break when a provider times out or returns bad responses.

Delivers

Reviewed routing policy with automatic fallback

Built in

about 5 weeks of creation time, MVP in 6 days

Investment

$13,000 for the MVP, $44,000 for the full product

Run it

Inside your business, or as part of your offer to clients

Reduce model spend and failed requests while keeping one integration.

1. Connect many AI models through one account.
2. Accept plain-language prompts and structured requests.
3. Return responses within the configured latency budget.
4. Provide simple input and output screens for non-specialists.
5. Handle general and specialized subject prompts.
6. Select the most appropriate model per prompt automatically.
7. Route on cost, latency and quality trade-offs.
8. Expose an OpenAI-style API endpoint.
9. Support configurable routing modes such as quality, balanced and low-impact.
10. Show token usage, performance and cost in one dashboard.
11. Let smaller models request help from larger models.
12. Include carbon-aware routing as a selectable mode.
13. Run automated model comparisons on cost, latency and JSON reliability.
14. Switch to another model on timeout, overload or bad response.
15. Run tests and switch providers through one API and MCP functions.
16. Maintain a large refreshed model pool.
17. Bill per test and API call used.
18. Compare the reviewed result with the recorded baseline and value assumptions.
19. Capture corrections and named-owner approval before consequential use.
20. Export a versioned reviewed routing policy with source references and unresolved questions.

Everything these tools do, in one app

• Multiple AI model access Lets users choose from many AI models instead of being tied to one.Found in AskYoda by Eden AI, LLMTest
• Natural language query handling Understands and responds to complex questions typed in plain language.Found in AskYoda by Eden AI
• Fast response times Returns answers quickly enough for casual and professional use.Found in AskYoda by Eden AI
• Simple user interface Provides straightforward input and output screens for users of any technical background.Found in AskYoda by Eden AI
• Broad topic coverage Handles questions across general knowledge and specialized subjects.Found in AskYoda by Eden AI
• Automatic model selection Chooses the most appropriate model for each prompt without manual tuning.Found in ModelPilot, LLMTest
• Cost-latency-quality balancing Routes requests based on trade-offs between cost, speed, and output quality.Found in ModelPilot, LLMTest
• OpenAI-style API endpoint Allows teams to integrate with minimal code changes by using a familiar API format.Found in ModelPilot
• Configurable routing modes Lets users prioritize goals such as high quality, balance, or eco-consciousness.Found in ModelPilot
• Usage analytics dashboard Shows token usage, performance, and cost monitoring in one place.Found in ModelPilot
• Small-model help requests Lets smaller models ask larger models for help when needed to improve results.Found in ModelPilot
• Carbon-aware routing Considers environmental impact when choosing which model to use.Found in ModelPilot
• Automated model comparison Tests and compares models on cost, latency, and JSON reliability for real workflows.Found in LLMTest
• Automatic fallback handling Switches to another model when a provider times out, overloads, or returns bad responses.Found in LLMTest
• Single API and MCP functions Runs tests and switches providers through one interface without deep integration work.Found in LLMTest
• Large refreshed model pool Gives access to hundreds of models that are updated daily for testing.Found in LLMTest
• Pay-per-use billing Charges only for tests and API calls used, with a low starting top-up.Found in LLMTest

What goes in, what comes out

The workflow

1. In
   Start with

   Prompts, quality rules, latency budgets and cost limits

2. 1

   Confirm the buyer's problem and scope

3. 2

   Collect their prompts

4. 3

   Quality rules

5. 4

   Latency budgets and cost limits

6. 5

   Then follow this sequence: 1

7. Out
   Finish with

   Reviewed routing policy with automatic fallback

AI does the heavy lifting, people stay in charge

Use AI to interpret permitted inputs, suggest structured mappings and generate candidate outputs for the routing, comparison and fallback modules. Use deterministic code for arithmetic, schema validation, hard constraints and reproducible tests. Review source-linked explanations and uncertainty before accepting results. One fixed provider set and approved model pool; final routing policy and quality thresholds remain under named human ownership. A model suggestion is never a verified fact, professional decision or authorization to act.

What your team sees

Primary screens: Routing policy setup, Live request and fallback monitor, Usage and cost dashboard. Use a project list, a central policy editor with per-route rules, and a right-hand panel for model pool, limits and test results. Let users compare models side by side on the same prompt set. Display draft, active and paused policy states. Provide a request log with the chosen model, fallback chain and reason. Make the task-specific outcome reviewed routing policy with automatic fallback visible beside its evidence, review state and value baseline.

Accounts and administration

Project ownership, provider credentials, model pool versions, policy states, usage allowances, request limits, export history and a rights record for supplied material. Add organization access boundaries, named reviewers, usage caps, data retention controls, export logs and explicit approval for external actions.

Integrations and data access

Customer-owned prompts, provider accounts and permitted evaluation data. Cloud logging, alerting, CI pipelines and existing API gateways. Start with file exchange and validate destination specifications before promising direct production cutover. Start with authorized file exchange. Validate current provider access, usage rights and schema behavior before promising a connector.

We build with our own AI software development factory, so most implementations take days to a few weeks of creation time, not months. You see working software at every step, and exact timing depends on availability.

1. 1

   Scoping call

   Day 1

   Thirty minutes on your process, your data and how you want to run it: for your own team, or for your clients. You get a fixed scope and price for the MVP.

2. 2

   MVP

   6 days

   One buyer segment, one recurring use case; first modules: connect many AI models through one account; select the most appropriate model per prompt automatically. Manual review in the loop. Built by our AI software factory.

3. 3

   Paid pilot

   7 days

   Accounts, roles, review states, audit trail and the first integration, hardened for two to three paying pilot customers.

4. 4

   Full product

   3 weeks

   Self-serve onboarding, billing, monitoring and the wider integration set.

5. 5

   Run and improve

   Monthly

   We host, monitor and improve it for a fixed monthly fee, or hand it over to your team. How the retainer works.

Why we start with an MVP

An MVP, or minimum viable product, is the smallest version that your users can actually work with. It is not a cheap version of the full solution. It is a test, built to answer the questions that decide whether the rest is worth building.

1. Pick the riskiest assumption. Here: will product and platform teams running AI features in production use it to solve "teams pick one model per feature, overpay for simple prompts, and break when a provider times out or returns bad responses"?
2. Build only what tests it. One team, one use case, a few core modules. People do the rest by hand for now.
3. Run a paid pilot. Agree quality and outcome thresholds before the pilot using this measure: Cost per accepted response, fallback success rate and quality pass rate.
4. Measure, then decide. Track cost per accepted response and fallback success rate and quality pass rate; accepted-output rate; material error rate; reviewer correction time; actual repeat purchase. Then expand, change course or stop, with evidence instead of opinions.

MVP scope for this solution. Pilot scope: One fixed provider set and approved model pool; final routing policy and quality thresholds remain under named human ownership. Implement one approved input format, a bounded representative prompt set and the first two task modules: connect many AI models through one account; select the most appropriate model per prompt automatically. Support the third module with operator review: switch to another model on timeout, overload or bad response. Include source references, corrections, basic organization access, approval states, export and value measurement. Use managed operator assistance for unresolved exceptions. The cost estimate covers this narrow prototype, not unrestricted multi-tenant scale, complex production integrations, specialist certification or physical operations.

After the MVP. Once paid pilots prove usefulness, automate repeatable reviewed steps and add one verified provider integration. Expand supported inputs and request volume only after new evaluation cases pass. Build reusable customer configurations and recurring value reports around the reviewed routing policy with automatic fallback. Retain the explicit scope boundary: One fixed provider set and approved model pool; final routing policy and quality thresholds remain under named human ownership.

What the build depends on. Prompt upload and preview, asynchronous test jobs, editable policy history, reviewer access and tested export formats. High-fidelity production requires specialist engineering QA. Obtain representative authorized cases, baseline measurements, qualified reviewers and a buyer-side decision owner. Specific limitation: One fixed provider set and approved model pool; final routing policy and quality thresholds remain under named human ownership.

A planning range to start the conversation, not a quote. You pay per phase, so you can stop after the MVP.

Running costs per month

A rough indication of monthly hosting and AI model costs once it is live, not tested. Real costs depend on usage, file sizes and the models chosen.

Your brand, or this one

Run it under your own brand, or start from this concept style.

Selling it to your own clients: the go-to-market playbook

Pricing to test

Test a USD 300-1,500 fixed pilot for one defined routing policy. Offer a monthly request allowance after repeat demand. Quote complex multi-region or regulated deployments separately. These are test prices, not market benchmarks. Package the initial sale as one bounded reviewed routing policy with automatic fallback. Recurring fees must specify request volume, review depth and integration support. For exchanges, test a disclosed coordination or successful-service fee rather than holding customer funds. Reprice only after measuring real delivery labor; platform-build cost is separate from a commercial pilot fee.

Message to test

Reduce model spend and failed requests while keeping one integration. Demonstrate a concrete reviewed routing policy with automatic fallback using the buyer's approved prompt set and show the baseline, corrections and actual delivery effort.

Where to find buyers

Product and platform teams running AI features in production professional communities; specialist consultants serving this buyer; permissioned partner introductions; practical demonstrations at relevant trade or practitioner events.

Lead magnet

A reviewed sample routing policy with automatic fallback from a small authorized prompt set, with a transparent calculation of cost per accepted response, fallback success rate and quality pass rate and no promised savings.

The first 30 days

1. Week 1: interview five product and platform teams running AI features in production and inspect a recent example of teams pick one model per feature, overpay for simple prompts, and break when a provider times out or returns bad responses.
2. Week 2: prepare a consented or synthetic demonstration of the three task modules.
3. Week 3: seek one bounded paid pilot with agreed baseline and acceptance criteria.
4. Week 4: measure cost per accepted response, fallback success rate and quality pass rate, reviewer effort and repeat-purchase interest. This is a demand-validation plan, not a thirty-day full-product delivery promise.

Paid pilot

Agree quality and outcome thresholds before the pilot using this measure: Cost per accepted response, fallback success rate and quality pass rate. Continue only if the buyer accepts the actual output, the intended job outcome improves without unacceptable errors, and measured delivery cost fits willingness to pay. Revise or stop if access is unavailable, qualified review cannot be provided, or apparent savings disappear after corrections and support. Use held-out cases when comparing model quality; use a properly reviewed comparison design before making causal claims. Record missing cases and negative results alongside successful outputs.

Success metrics

Cost per accepted response, fallback success rate and quality pass rate; accepted-output rate; material error rate; reviewer correction time; actual repeat purchase.

Retention and expansion

Repeat the workflow when the buyer again needs a reviewed routing policy with automatic fallback. Retain permissioned settings and reviewed examples, report realized value honestly, and sell increased volume or adjacent approved workflows only after contribution margin and quality remain acceptable.

Why clients would pick it

A reusable library of approved routing rules, fallback chains and review examples, together with reliable delivery for a narrow engineering niche. Build a permissioned library of representative task cases, reviewer corrections and verified operating constraints for product and platform teams running AI features in production. Repeatable delivery and useful integrations matter more than access to a base model.

Alternatives and positioning

AskYoda by Eden AI, ModelPilot, LLMTest, direct provider SDKs and in-house routing scripts. Compare this product with the buyer's present method on cost per accepted response, fallback success rate and quality pass rate. Offer a bounded paid workflow instead of claiming broad autonomous expertise. Market uniqueness and competitor coverage are not verified.

Main delivery costs

Model calls, fallback retries, storage, reviewer hours, client revision rounds and licensed source material. Additional initial validation requires representative authorized prompt preparation, buyer interviews, buyer-side evaluation and bounded validation of the reviewed routing policy with automatic fallback. Track cost per accepted response, including correction work, unsuccessful cases and support.

Preserve prompt confidentiality, source attribution, data residency and usage permissions. Named owners approve routing changes and production scope. One fixed provider set and approved model pool; final routing policy and quality thresholds remain under named human ownership. Keep all consequential actions under authorized human control and do not fabricate missing inputs, permissions, professional judgments or market evidence.