Runware vs Together AI: compare the route you’ll operate

The APIs expose the same task through different contracts

Runware’s REST endpoint accepts a JSON array of tasks. An imageInference task includes a UUID, prompt, dimensions, model ID, and result count. Together exposes a conventional /v1/images/generations body with prompt, model, dimensions, steps, response format, and safety control.

RenderRoute translates both into one public request. The response identifies the provider actually used, so abstraction does not erase accountability. Provider-specific tests remain essential because normalized JSON cannot make model behavior identical.

Reproduce the internal cost gap before making it a sales claim

The operator’s 960×1472 measurement was $0.0006 on Runware and approximately $0.0037 on Together, a ratio near 6.2. That is compelling routing evidence for this configuration, not a permanent universal comparison. Official pricing units and account terms may not map directly to one measured call.

Run at least a meaningful sample across the same prompts, dimensions, output count, and success criteria. Include retries, failures, and output acceptance. Publish the date and raw methodology. If the result changes, update the page rather than preserving a stale headline.

• Same prompt set and safety mode
• Same dimensions and image count
• Comparable model and steps
• Actual billed cost where available
• Cost per accepted output

Retain a fallback because cheap primary inference is not an availability plan

A primary provider can rate-limit, time out, change capacity, or encounter a regional incident. Together remains useful when it improves the probability of completing a standard fast request inside the product’s latency budget.

The router retries only transient Runware errors before moving to Together. A prompt or safety rejection stops. Circuit breakers reduce repeated exposure to an active incident, and idempotency protects against duplicate work when the client retries the whole operation.

1. Runware / runware:100@1
2. Together / black-forest-labs/FLUX.1-schnell
3. Leonardo / Flux Schnell
4. Local OpenAI-compatible route

Choose the primary from your workload, then keep the router reversible

Measure p50 and p95 latency, error rate, moderation behavior, prompt adherence, artifact rate, provider cost, and acceptance. Weight them by your product: an editor values latency differently from an overnight catalog job.

Keep model IDs and provider order in environment configuration. A comparison page should document why the current order exists, while the code should allow a measured change without rewriting clients.