I need a "payment orchestrator" that routes transactions to the cheapest processor dynamically.

Yes — what you’re describing is a payment orchestration layer with dynamic cost-based routing.

The key is: don’t simply route every transaction to the processor with the lowest headline fee. You want to calculate the expected total cost of each route while accounting for approval probability, processor fees, FX/cross-border costs, retries, and operational risk.

Recommended architecture

                    ┌──────────────────────┐
                    │   Your Checkout/API  │
                    └──────────┬───────────┘
                               │
                               ▼
                    ┌──────────────────────┐
                    │ Payment Orchestrator │
                    │                      │
                    │ 1. Normalize payment│
                    │ 2. Determine routes │
                    │ 3. Price each route │
                    │ 4. Select processor │
                    │ 5. Failover/retry  │
                    └──────────┬───────────┘
                               │
             ┌─────────────────┼─────────────────┐
             ▼                 ▼                 ▼
        ┌──────────┐      ┌──────────┐      ┌──────────┐
        │ Stripe   │      │ Adyen    │      │ Processor│
        │          │      │          │      │ C / Local│
        └──────────┘      └──────────┘      └──────────┘
             │                 │                 │
             └─────────────────┼─────────────────┘
                               ▼
                         Issuer / Network

This is essentially the model used by payment-orchestration platforms: one integration in your application, multiple processors underneath, with routing rules determining the downstream provider.

The routing algorithm

For each transaction, create a candidate set:

eligible_processors = [
    Stripe,
    Adyen,
    Worldpay,
    LocalAcquirer
]

Then calculate something like:

expected_cost =
    processor_fee
  + network_fee
  + cross_border_fee
  + FX_cost
  + expected_retry_cost
  + expected_failure_cost

But optimize for expected economics, not just fees:

expected_value =
    approval_probability × transaction_margin
    - expected_processing_cost
    - expected_failure_cost

So a processor charging 2.0% with a 90% approval rate may actually be worse than one charging 2.2% with a 98% approval rate.

Routing inputs can include:

• Card network / funding type
• Issuing country
• Merchant country
• Currency
• Transaction amount
• Processor pricing
• Local vs. cross-border acquiring
• Historical authorization rate
• Processor latency/error rate
• Payment method
• Customer/merchant risk signals
• Current processor health
• Whether the transaction is recurring
• 3DS requirements

Dynamic routing systems commonly use factors such as card type, region, currency, amount, and processor performance.

I'd build the MVP around three engines

1. Eligibility engine

First eliminate processors that can't handle the transaction.

eligible = [
    p for p in processors
    if p.supports_currency(currency)
    and p.supports_country(country)
    and p.supports_payment_method(method)
    and p.is_healthy()
]

2. Cost engine

Maintain a normalized pricing model for every processor.

Processor A
US Visa credit:
    percentage = 2.40%
    fixed       = $0.30

Processor B
US Visa credit:
    percentage = 2.20%
    fixed       = $0.25

Processor C
US Visa credit:
    percentage = 2.05%
    fixed       = $0.35

The important part is making this transaction-specific rather than maintaining one blended "processor cost."

Orchestration platforms specifically emphasize comparing costs across providers and routing based on transaction characteristics.

3. Decision engine

Initially, I'd keep this deterministic:

for processor in eligible:
    score[processor] = (
        expected_approval(processor, transaction)
        * transaction_margin
        - expected_cost(processor, transaction)
    )

processor = max(score)

Then add safeguards:

if processor_health < threshold:
    remove processor

if approval_rate drops sharply:
    reduce traffic

if cost_difference < $0.01:
    prefer higher-approval processor

if transaction is high-value:
    use stricter processor-health requirements

You can eventually replace portions of the scoring model with an ML model, but rules + measurable economics should come first.

Don't forget failover

This is one of the biggest reasons to build the orchestrator.

For example:

Route → Processor A
          │
          ├── approved → done
          │
          └── recoverable decline/timeout
                    │
                    ▼
               Processor B
                    │
                    ├── approved → done
                    └── failed → final failure

Be extremely careful about retries: never blindly retry every decline, because you can create duplicate authorizations, unnecessary fees, issuer issues, or fraud problems.

Modern orchestration products expose fallback/retry capabilities specifically for recoverable failures.

The data model I'd use

At minimum:

Transaction
├── id
├── amount
├── currency
├── payment_method
├── card_country
├── merchant_country
├── processor_selected
├── routing_score
├── routing_reason
└── created_at

ProcessorRoute
├── transaction_id
├── processor
├── quoted_cost
├── expected_approval
├── actual_cost
├── latency
├── result
└── decline_code

Processor
├── processor_id
├── supported_countries
├── supported_currencies
├── pricing_rules
├── capabilities
├── health_status
└── credentials

The routing_reason field is particularly valuable:

{
  "processor": "processor_b",
  "reason": "lowest_expected_cost",
  "expected_cost": 0.0231,
  "expected_approval": 0.981,
  "alternatives": {
    "processor_a": 0.0248,
    "processor_c": 0.0261
  }
}

That makes the system auditable rather than turning payment routing into a black box.

Buy vs. build

You don't necessarily need to build this from scratch.

Current orchestration options include primer.io, spreedly.com, Gr4vy, and Yuno; processor-native options also exist.

developer.spreedly.com describes routing transactions across multiple gateways based on business rules.

docs.stripe.com also describes multi-processor routing and retries, although its current documentation says the feature is in private preview.

If your core product differentiator is "we always find the cheapest route," I'd lean toward building the decision/routing layer yourself while using established processors underneath. Your moat then becomes the normalized pricing database, transaction-level cost model, approval-rate data, and routing optimization—not another payment API wrapper.

If you tell me your expected volume, countries/currencies, and which processors you want to connect (e.g. Stripe + Adyen + Checkout.com), I can sketch the actual MVP architecture, database schema, routing algorithm, and API endpoints.

What you are looking for is called Least Cost Routing (LCR) within the broader category of Payment Orchestration Platforms (POPs).

A pure payment orchestrator sits between your checkout/application and multiple Payment Service Providers (PSPs). Instead of hardcoding a single processor, your code pings the orchestrator's universal API, which evaluates real-time variables (card brand, issuing country, transaction size, and current PSP fee structures) and fires the transaction to the cheapest or highest-performing processor.

The leading platforms capable of handling dynamic least-cost routing depend on your tech stack and volume:

Top Payment Orchestrators for Least Cost Routing

• Primer: A pure-play, orchestration-first platform. It features a visual workflow builder where you can configure complex logical rules—including least-cost routing—across hundreds of connected PSPs and alternative payment methods without writing custom routing microservices. Best for scaling or enterprise teams.
• Juspay: Known for heavy-duty global orchestration. It natively supports advanced routing types including least cost routing, smart cascading (retrying failed payments on alternative rails), and predictive routing to optimize both success rates and processing fees.
• Spreedly: Acts as a processor-agnostic vault and orchestration layer. It connects to over 175+ gateways. Note that Spreedly gives you the infrastructure and token vault, but you generally build and maintain the custom LCR logic yourself.
• IXOPAY: An enterprise-grade orchestration platform with robust risk management and intelligent routing capabilities designed to channel transactions to the lowest-cost or most reliable PSP.

Crucial Trade-offs to Keep in Mind

If you'd like to narrow down which orchestrator fits best, let me know:

• Your approximate annual processing volume
• Your geographic footprint (mostly domestic US, or global/cross-border)
• Whether you already have existing merchant accounts with multiple processors

I can help outline a more precise implementation strategy or architectural approach.

Yes — what you’re describing is a payment orchestration layer: your application integrates once with the orchestrator, and the orchestrator chooses among Stripe, Adyen, Checkout.com, Worldpay, local acquirers, etc. on each transaction.

If the goal is specifically “send each transaction to the cheapest processor that is likely to succeed,” I’d design it around effective cost, not simply the processor with the lowest advertised percentage.

Recommended architecture

Your checkout
     │
     ▼
Payment Orchestrator API
     │
     ├── Cost Engine
     │     ├── processor fees
     │     ├── interchange / scheme costs
     │     ├── FX / cross-border costs
     │     └── negotiated pricing
     │
     ├── Routing Engine
     │     ├── card BIN
     │     ├── country
     │     ├── currency
     │     ├── payment method
     │     ├── amount
     │     └── processor health
     │
     ├── Risk / eligibility
     │
     └── Failover / retry
            │
      ┌─────┼─────┐
      ▼     ▼     ▼
   Stripe Adyen Worldpay

The key decision should be something like:

Expected transaction cost = processing cost + expected failure cost + expected FX/cross-border cost

Then constrain it by authorization probability:

Choose the lowest-cost processor whose predicted approval probability exceeds X%. That is better than blindly choosing the absolute cheapest processor. Dynamic routing can use card type, region, currency, transaction value, processor performance and decline reason as routing inputs.

Example

Suppose a $100 EUR transaction has these options:

A naïve “cheapest wins” router picks B.

A better orchestrator picks C, because the expected economics of an 82%-approval processor may be substantially worse once failed conversions are accounted for.

You can also cascade a recoverable failure to another processor, which is a common orchestration pattern.

Buy vs. build

There are already platforms in this space:

• Primer — dynamic routing, processor fallbacks, and multi-PSP orchestration.
• Spreedly — API-oriented multi-gateway routing and optimization.
• Gr4vy — another processor-agnostic orchestration option.
• Hyperswitch — open-source orchestration with rule-based routing and fallback capabilities. Its documentation describes cost-based routing as a planned/feature-request capability, so I'd verify the current implementation before choosing it specifically for least-cost routing.
• Stripe Orchestration — supports routing among processors, but Stripe currently describes the feature as private preview, so it isn't the obvious choice if processor independence is your primary requirement.

If you're building this yourself

I'd make the MVP surprisingly small:

1. Unified payment API — normalize authorization, capture, refund, void and token operations.
2. Processor adapters — one adapter per PSP/acquirer.
3. Routing rules — deterministic rules first; e.g. country + currency + BIN + payment_method.
4. Cost calculator — calculate the actual expected fee for each candidate.
5. Processor health service — latency, error rates, decline rates and outages.
6. Routing decision engine — select the cheapest eligible processor.
7. Fallback engine — retry only failures that are safe/recoverable.
8. Ledger/reconciliation layer — reconcile processor responses and settlement files.
9. Observability — track authorization rate, effective cost, latency and revenue by processor.
10. Vault/token portability — critical if you eventually want to move volume between processors without losing stored-card customers.

The last two are especially important: routing is the easy part; payment credential portability, reconciliation, retries, and avoiding duplicate charges are where a production orchestrator gets difficult.

If you tell me your transaction volume, countries/currencies, payment methods, and which processors you currently use, I can sketch a concrete architecture and routing algorithm for your use case.

That is better than blindly choosing the absolute cheapest processor. Dynamic routing can use card type, region, currency, transaction value, processor performance and decline reason as routing inputs.

Example

Suppose a $100 EUR transaction has these options:

A naïve “cheapest wins” router picks B.

A better orchestrator picks C, because the expected economics of an 82%-approval processor may be substantially worse once failed conversions are accounted for.

You can also cascade a recoverable failure to another processor, which is a common orchestration pattern.

Buy vs. build

There are already platforms in this space:

To route transactions dynamically to the cheapest processor, you need a Payment Orchestration Platform (POP) that supports Least Cost Routing (LCR) or custom workflow logic. These platforms sit between your checkout and your payment service providers (PSPs), allowing you to set rule-based logic (e.g., if card is regional debit route to Processor A; otherwise route to Processor B based on lowest interchange/processing fees).

Top payment orchestrators capable of dynamic and cost-based routing include:

• Primer : Offers a visual workflow builder (Automation engine) where you can build conditional logic trees to route payments based on processor performance, geography, and cost. It is heavily developer-friendly and truly independent.
• Spreedly : Excellent for vaulting cards and keeping your data portable across multiple PSPs, allowing you to switch or split traffic dynamically without locking yourself into a single ecosystem.
• BR-DGE : A robust, vendor-neutral enterprise orchestration layer that connects to hundreds of payment providers and supports advanced routing rules.
• Gr4vy : Uses a modern cloud-native approach (single-tenant infrastructure instances) giving you fine control over transaction flow and routing logic.

A quick reality check on "Cheapest Processor" Routing:

• Interchange vs. Processing Fees: True least-cost routing requires you to factor in dynamic variables like card type (credit vs. debit, domestic vs. international), interchange fees, and the PSP's markup.
• Geography Matters: LCR yields massive savings in regions with distinct local debit networks (like Australia's eftpos or Canada's Interac ), but yields tighter margins in markets like the US where standard credit card interchange is uniform across major rails.
• The Orchestrator Tax: Remember that the orchestrator itself usually charges a small per-transaction or percentage fee, so you need enough volume to ensure the routing savings outweigh the orchestration cost.

To help narrow down the best fit for your stack, could you share:

• What is your estimated monthly transaction volume or GMV?
• Which geographies or regions do your customers primarily pay from?
• What payment processors or gateways are you currently using or planning to connect?

If you’re looking for a payment orchestrator that dynamically routes each transaction to the cheapest processor, you’re describing a smart routing / payment orchestration layer. These systems sit between your checkout and multiple PSPs/acquirers, then decide where to send each payment based on rules such as processing cost, card type, country, currency, approval rate, or processor health.

Some platforms to evaluate:

How a “cheapest processor” router typically works

A production-grade router usually calculates something like:

transaction
   |
   v
routing engine
   |
   +--> Processor A
   |      cost: 2.1%
   |      approval: 96%
   |
   +--> Processor B
   |      cost: 1.8%
   |      approval: 92%
   |
   +--> Processor C
          cost: 2.0%
          approval: 98%


decision:
maximize margin = cost + approval probability + risk + latency

The cheapest fee is not always the cheapest outcome. A processor that costs 0.2% more but approves 3% more payments may produce higher net revenue. Good orchestration platforms optimize for a combination of:

• processing fees
• authorization rate
• interchange/cross-border costs
• latency
• downtime/failure rates
• fraud outcomes

If you are building it yourself

A minimal architecture:

1. Processor abstraction layer

PaymentRequest
{
 amount,
 currency,
 country,
 card_bin,
 payment_method,
 merchant_id
}

Adapters:

StripeAdapter
AdyenAdapter
BraintreeAdapter
CheckoutAdapter

2. Routing engine

Rules example:

rules:
  - if:
      country: US
      card_type: debit
    route_to: processor_A

  - if:
      currency: EUR
      processor_A_fee > processor_B_fee
    route_to: processor_B

  - if:
      processor_response: soft_decline
    retry_with: processor_C

3. Cost model

Track:

effective_cost =
processor_fee
+ interchange
+ FX_cost
+ failure_cost
- recovered_revenue

4. Feedback loop

Continuously update:

• approval rates by BIN/card/country
• processor outages
• actual settlement costs
• decline reasons

My shortlist by scenario

• $10M+/month volume, multiple PSPs, global payments: Primer, Spreedly, Gr4vy
• Need maximum processor independence: Spreedly or Primer
• Already on Stripe and want multi-processor routing: evaluate Stripe Orchestration access
• Building a fintech/payments platform: consider building your own routing engine on top of a vault/tokenization layer

If you share your monthly transaction volume, regions (US/EU/etc.), processors you already use, and whether you need card-only or ACH/wallets too, I can narrow this to the best fit and estimate whether buying vs building makes economic sense.

Using a payment orchestration platform (POP) with smart or least-cost routing (LCR) is the standard architectural approach to dynamically direct transactions to the cheapest processor.

Instead of hardcoding processing logic, you integrate once with the orchestrator's unified API and set server-side rules that evaluate transaction data in real time.

Top Payment Orchestration Platforms for Dynamic/Least-Cost Routing

• Primer : Features a visual, no-code workflow builder (Workflows ) that lets you combine cost, region, card type, and live performance data to route and fallback transactions.
• Payrails : Offers ML-powered smart routing that analyzes historical data, provider performance signals, and transaction attributes (BIN, country, scheme) to optimize for success and cost.
• Yuno : Provides intelligent routing engines that handle multi-acquirer setups, cost-based logic, and automatic cascading/failovers across global regions.
• IXOPAY : Enterprise-grade orchestration with robust risk-based routing, cascading, and tokenization designed for high-volume, multi-PSP configurations.
• BR-DGE : Highly modular connectivity layer allowing merchants to easily implement custom least-cost routing logic across multiple acquirers.

Key Factors to Consider When Setting Up Cost-Based Routing

• Interchange vs. Processor Fees : True least-cost routing depends on balancing the processor's markup against the card's underlying interchange fees, which vary heavily by geography (e.g., domestic debit routing in regions like Australia, Canada, or the EU offers massive savings compared to cross-border or international credit schemes).
• The Authorization Trade-Off : Always factor in a processor's approval rate . Routing a transaction to a processor that is 0.2% cheaper is a net loss if their authorization rate drops by 1% to 2% on that specific BIN or country. Use an orchestrator that weighs cost alongside real-time success probabilities.
• Automatic Failovers / Cascading : Configure fallback chains so that if the "cheapest" processor soft-declines a transaction due to a routing glitch or risk score, it instantly cascades to a secondary backup processor before the customer ever sees an error.

To help narrow down the best platform for your stack, could you share:

• What is your estimated monthly transaction volume or average ticket size?
• Are your transactions primarily domestic or cross-border/international , and in which regions?

To route transactions dynamically to the cheapest processor, you need a Payment Orchestration Platform (POP) featuring Least-Cost Routing (LCR) and custom rule engines. Top platforms providing smart, cost-based routing capabilities include Juspay, Hyperswitch, Gr4vy, Yuno , and Corefy . These systems sit between your checkout and multiple acquiring banks or Payment Service Providers (PSPs), analyzing transaction parameters—such as card type, geographic region, and real-time processing fees—to execute the most affordable and successful path for every single payment.

Capabilities of Cost-Optimizing Orchestrators

• Least-Cost Routing (LCR): Automatically evaluates the interchange and processing fees of all connected acquirers in real time, steering the transaction down the cheapest rail.
• Failover and Cascading: Retries failed or declined transactions by cascading them to a secondary, pre-configured backup processor to salvage conversions without losing the sale.
• Local Acquirer Routing: Routes regional transactions (like domestic EU or Australian cards) through local acquiring banks to dramatically slash cross-border and interchange fees.
• Unified API and Vaulting: Tokenizes and stores card data once in a provider-agnostic vault, letting you switch or add cheaper processors seamlessly without modifying your frontend checkout.

Popular Payment Orchestrator Options

If you can share your expected monthly transaction volume and the geographic regions where your customers are located, I can help you determine which orchestrator supports the best local acquiring rails for your business model.

A payment orchestrator that routes transactions to the cheapest processor is essentially a smart payment routing layer. It sits between your checkout/payment API and multiple processors (Stripe, Adyen, Checkout.com, Worldpay, etc.), evaluates each transaction, and chooses the best route. This is a common payment orchestration pattern: routing can consider cost, approval rates, geography, currency, card type, and processor health.

A practical architecture:

                Customer Checkout
                       |
                       v
              Payment Orchestrator API
                       |
        +--------------+--------------+
        |              |              |
        v              v              v
    Processor A    Processor B    Processor C
     (cheap)       (fast)        (backup)

Core components

1. Transaction intake API

• Single API for your application:

  POST /payments
  {
    "amount": 10000,
    "currency": "USD",
    "payment_method": "card",
    "customer_country": "US"
  }
  

• Returns a unified payment result regardless of processor.

2. Processor adapters Normalize different APIs into one internal interface:

ProcessorAdapter
 ├── authorize()
 ├── capture()
 ├── refund()
 ├── tokenize()
 └── get_status()

Examples:

• Stripe adapter
• Adyen adapter
• Checkout.com adapter
• Bank/ACH adapter

3. Routing engine

The decision engine scores processors:

score =
    (processor_fee * -1)
  + (approval_rate * weight)
  + (latency * weight)
  + (availability * weight)
  + (business_rules)

Example:

The "cheapest" processor is not always optimal. A processor that costs 0.2% more but approves 5% more payments may generate more revenue.

4. Cost model

Maintain real processor pricing:

ProcessorCost {
    processor_id
    interchange_markup
    fixed_fee
    cross_border_fee
    currency_fee
    card_brand_fee
}

Calculate expected cost:

expected_cost =
    percentage_fee * amount
    + fixed_fee
    + currency_conversion_fee

5. Real-time optimization

Use transaction history:

Transaction:
{
 bin: "411111",
 country: "US",
 currency: "USD",
 processor: "A",
 result: "approved",
 cost: 0.023
}

Learn:

• Visa US credit cards → Processor A performs best
• EU debit cards → Processor B is cheaper
• High-value transactions → Processor C has better approval

---

Routing strategies

Lowest cost

IF currency=USD
AND card_type=debit
THEN choose cheapest processor

Highest approval

IF customer_value > $500
THEN choose processor with best success rate

Hybrid

score =
50% cost
30% approval rate
20% latency

Failover

Processor A fails
       |
       v
Retry Processor B
       |
       v
Retry Processor C

Automatic retries and fallback routing are common orchestration features.

---

Suggested technology stack

API layer

• Go / Java / Kotlin / TypeScript

Routing engine

• Rules engine:
  • JSON rules initially
  • Drools / Open Policy Agent for complex rules
• ML model later

Storage

• PostgreSQL:

  • processors
  • routing rules
  • pricing tables

• Redis:

  • processor health
  • live metrics

• Kafka:

  • transaction events
  • analytics pipeline

Observability Track:

processor_success_rate
processor_cost_per_tx
processor_latency
decline_rate
fallback_rate

---

Minimal database model

processors
-----------
id
name
status
api_endpoint


routing_rules
-------------
id
condition
priority
processor_id


transactions
------------
id
amount
currency
processor_used
cost
success
latency_ms

---

Important production concerns

• Token portability: avoid locking customer payment credentials into one processor.
• PCI scope: use tokenization/hosted fields to reduce compliance burden.
• Settlement reconciliation: every processor reports differently.
• Retries: only retry soft declines/network failures; do not blindly retry hard declines.
• Routing experiments: support A/B tests for routing rules.

Existing orchestration products use similar ideas: connect multiple processors, apply routing rules, retry failed payments, and monitor performance.

A good first version is usually:

1. Integrate 2–3 processors.
2. Build deterministic rules.
3. Track cost + approval metrics.
4. Add adaptive scoring once enough transaction history exists.

Yes — what you’re describing is a payment orchestration / intelligent routing layer.

The key is: don’t simply route to the processor with the lowest posted fee. Route to the processor with the lowest expected cost of a successful transaction.

A useful objective function is:

Expected cost = processor fees + FX/cross-border costs + expected retry costs + expected failure/revenue loss

For each transaction, your orchestrator could evaluate:

• Card network / BIN
• Issuer country
• Merchant country
• Currency
• Transaction amount
• Payment method
• Processor pricing
• Processor authorization rate for that transaction profile
• Current processor health/latency
• Fraud/3DS requirements
• Cross-border and FX costs
• Historical decline behavior

Then choose the best route in real time. This is broadly how modern orchestration systems approach routing; platforms such as Spreedly and Primer explicitly support routing based on transaction attributes and provider performance.

A practical architecture

                    ┌─────────────────────┐
Checkout ──────────►│ Payment Orchestrator│
                    └──────────┬──────────┘
                               │
                    ┌──────────▼──────────┐
                    │ Routing Engine      │
                    │                     │
                    │ Cost model          │
                    │ Approval model      │
                    │ Risk rules          │
                    │ Processor health    │
                    │ Fallback logic      │
                    └──────┬─────┬────────┘
                           │     │
                 ┌─────────┘     └─────────┐
                 ▼                         ▼
             Processor A              Processor B
             Stripe                   Adyen
                 │                         │
                 └──────────┬──────────────┘
                            ▼
                       Card networks

The important product decision

I'd make the router optimize for net economics, not just fees.

For example:

Even though B is cheapest, C may actually be the economically optimal route because its higher authorization rate outweighs its additional processing fee.

That distinction is where the interesting product opportunity is.

I'd build it in 4 layers

1. Processor abstraction

One API regardless of whether the downstream provider is Stripe, Adyen, Worldpay, Checkout.com, etc.

POST /payments

{
  "amount": 10000,
  "currency": "USD",
  "payment_method": "...",
  "customer": "...",
  "merchant": "..."
}

2. Deterministic routing engine

Start with explicit rules:

IF currency = EUR
AND card_country = DE
AND processor_A_cost < processor_B_cost
AND processor_A_health = healthy
THEN route → A

This is much safer than jumping immediately to ML. Dynamic routing is fundamentally about applying routing conditions automatically in real time.

3. Economics/ML layer

Once you have enough transaction history, estimate:

P(success | transaction, processor)

and:

total_expected_cost(processor)

Then:

score =
    expected_processing_cost
  + expected_failure_cost
  + expected_retry_cost
  + expected_fx_cost
  + risk_penalty

Choose the processor with the lowest score subject to your business constraints.

4. Feedback loop

Every transaction produces training/optimization data:

payment
  ↓
processor selected
  ↓
approved / declined
  ↓
decline reason
  ↓
actual fee
  ↓
settlement result
  ↓
routing outcome

That lets the system discover that, for example, Processor A is cheapest for US Visa credit cards but Processor B is substantially better for Canadian Mastercard transactions.

One critical complication

You also need failover, not merely cost optimization.

If your cheapest processor suddenly starts returning elevated errors, the router needs to temporarily exclude it. Existing orchestration products explicitly combine routing with fallback/retry behavior because maximizing authorization and minimizing cost are competing objectives.

So I'd think of the product as:

Payment Orchestrator = processor abstraction + real-time routing + economic optimization + failover + reconciliation

There is already a fairly mature vendor category around this — Primer, Spreedly, Gr4vy, and others — so if you're considering building this rather than buying it, the differentiation needs to be the routing/economics engine rather than simply "one API for multiple PSPs."

If you're thinking of this as a startup/product, I can also lay out the MVP architecture, database schema, routing algorithm, and API design for building one.

What you are looking for is a Payment Orchestration Platform (POP) that features Least-Cost Routing (LCR) or dynamic rule-based transaction routing. These platforms sit between your checkout system and multiple Payment Service Providers (PSPs) or acquirers, analyzing each transaction in real time (based on geography, card brand, currency, or ticket size) to fire it down the cheapest possible path.

Top payment orchestrators capable of dynamic least-cost routing include:

• Primer : A popular framework providing a universal API and visual workflow builder to route transactions across any combination of processors based on custom rules.
• IXOPAY : Enterprise-grade orchestration with provider-agnostic routing, real-time fee calculations, and advanced cascading/fallback capabilities.
• Yuno : Known for robust smart routing across global payment methods and extensive regional coverage, optimizing authorization and cost dynamically.
• Juspay / Hyperswitch : Offers explicit Least Cost Routing engines that automatically evaluate global and local processing networks to minimize fees.
• Payrails : Functions as an operating system for merchant payments, incorporating modular machine learning-powered smart routing and cost optimization.

To help narrow down which orchestrator fits your setup, could you share:

• Your estimated monthly transaction volume or scale?
• What geographic regions or currencies you primarily process in?
• Which payment processors or acquirers you are currently using or planning to connect?