Together AI and Fireworks AI document serverless inference and dedicated deployment options; Replicate documents model predictions, deployments and asynchronous webhooks. Compare the model, deployment mode, data terms and production behaviour for your workload rather than selecting on a headline speed claim.
First decide what you need to run
An AI document-review product may need a low-latency response for an interactive user and a longer background job for a large file. A model-serving decision should begin with the job, the data and the operating team, not the longest list of models.
We reviewed public vendor materials on October 30, 2026. The table summarizes documented approaches, not an independent performance test. Pricing, availability, data terms and supported models should be confirmed for the specific account and deployment.
| Question | Together AI | Fireworks AI | Replicate |
|---|---|---|---|
| Publicly described starting point | Managed serverless inference | Serverless model inference | Model predictions through an API |
| Other documented approach | Dedicated model inference | Dedicated deployments | Deployments for running models |
| Operational distinction | Confirm the model and endpoint available to your account | Separate serverless and deployment requirements | Prediction lifecycle can be handled asynchronously |
| Evidence to request | Model/version, capacity and data terms | Deployment configuration, limits and data terms | Prediction status, output handling and webhook behaviour |
Sources: Together AI inference, Fireworks documentation, and Replicate predictions.
Together AI: separate serverless from dedicated capacity
Together AI describes a managed serverless inference API and a dedicated model-inference option. The distinction matters when traffic is unpredictable or when a buyer needs to establish a specific capacity arrangement. Ask which model version is available in each mode and whether the move to a dedicated endpoint changes operational responsibilities.
For a document pipeline, ask how the service handles requests that exceed limits, how failures are reported and what data is retained. Do not infer a particular service-level agreement or regional availability from a generic product page.
Fireworks AI: identify the deployment being evaluated
Fireworks documentation presents serverless inference and dedicated deployments as separate choices. A demonstration on a shared endpoint does not automatically establish the behaviour of a dedicated configuration, or vice versa. Record the exact model, deployment, region and limits used in any evaluation.
Ask about cold starts, throughput, rate limits and the process for changing a model version. Request the applicable privacy and security terms for the proposed contract. Marketing comparisons of speed or cost should be treated as provider claims until tested against your own workload.
Replicate: plan for the prediction lifecycle
Replicate documents synchronous and asynchronous prediction modes. Its HTTP reference describes webhooks and notes that webhook requests may be retried and redirects are not followed. Your receiver should therefore handle repeated events safely and use a reachable endpoint.
The provider’s webhook guide discusses persisting API prediction data and files before its documented retention window ends. Confirm the current retention policy for the exact model and account; do not assume a returned output URL is permanent storage.
Test the same workload
Use a representative, consented test set and the same model version where it is available. Record input and output length, concurrency, time to first response, total completion time, failures and the cost of a completed task. Separate model-quality evaluation from infrastructure performance: a faster incorrect answer is not a successful result.
For sensitive documents, test with synthetic or approved material. Review encryption, retention, access controls, sub-processors and contractual deletion terms before production. Ask who owns retry logic, observability and incident response when a model call fails.
Explore FluidRWA’s AI vendor directory and AI infrastructure providers for related categories. These providers were selected for their documented serving workflows, not as a ranked list of the entire market.
FAQ
Does API compatibility mean models behave identically?
No. A similar request format does not establish identical model versions, limits, output behaviour or operational guarantees. Test the exact endpoint and model.
Is serverless always cheaper than a dedicated deployment?
Not necessarily. Compare your traffic, capacity needs, billing terms and the cost of idle or reserved resources using current quotations.
Can confidential investor data be sent to any inference API?
Do not assume so. Review the provider's contract, retention, sub-processors, location and your own legal and security requirements before sending sensitive data.
Choosing AI infrastructure?
Tell us about your application and constraints, or email contact@fluidrwa.com.