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Together AI gives teams hosted access to open models and GPU capacity, including batch inference. Developers weighing a switch have different needs: some want more provider choice, a local runtime or control over containers; others want to compare per-model costs and capacity terms. One Reddit thread questions the economics of reselling compute, while commenters argue that managed infrastructure can save engineering time. These alternatives cover hosted APIs and infrastructure, but their pricing and migration effort depend on the workload.
01Why people leave Together AI
Questions about provider economics
One Reddit thread questions the compute-broker model; commenters counter that managed infrastructure can justify its cost by saving teams operational work.
A need for provider choice
A Reddit commenter describes Together as one provider. Teams seeking other serving paths can compare APIs, local runtimes and GPU platforms.
Unclear production costs or capacity
Model pricing and capacity terms affect production fit. Compare workload-specific costs and confirm capacity commitments before moving traffic.
02The alternatives, ranked by fit
- OpenAI-compatible inference
GroqCloudGroqCloudGroqCloud is a hosted inference API listed as OpenAI-compatible, making it a candidate when an application already uses that integration pattern. The free plan gives teams a way to start, but the listed information does not establish production costs or model limits. Confirm supported models, limits and usage pricing, then test the model-name and response changes required by your client.
Trade-off: You still depend on a hosted provider; verify model availability, limits and pricing for your workload.
7.5/10from
Freefree plan - Custom GPU workloads
RunpodRunpodRunpod suits teams that want on-demand GPU capacity and custom containers rather than only a hosted model endpoint. Its listed starting price is $0.27, but confirm the billing unit and total cost for your deployment. Moving a Together workload means owning more of the serving setup, and the platform’s production reliability needs close attention.
Trade-off: You take on more deployment and reliability work than with a hosted inference API.
7.2/10from
$0.27/mo - Local open models
OllamaOllamaOllama is an open-source, self-hostable local model runtime with an API, suited to offline, privacy-sensitive or prototype workloads. It changes the deployment boundary: run inference in your own environment instead of relying on a hosted provider. Check model fit and resource needs, then adapt your application to Ollama’s API; its listed plan includes a free option.
Trade-off: Local deployment shifts hardware, scaling and operations to your team.
7.6/10from
$16.67/mofree plan - Containerized batch inference
SaladCloudSaladCloudSaladCloud targets cost-sensitive GPU inference, containerized batch workloads and teams with idle GPU fleets. It is listed as self-hostable, offering a different infrastructure path from a hosted model API. The listed starting price is $43.8; confirm the billing basis, available capacity and production terms. You will also need to deploy and operate your inference containers.
Trade-off: Capacity and pricing need careful review, and your team owns more of the serving stack.
7.3/10from
$43.80/mo - Hosted model comparison
DeepInfraDeepInfraDeepInfra offers broad hosted model access and usage-based rates, so it gives teams another provider to evaluate without first managing GPU infrastructure. Compare the exact models and expected usage against your Together workloads: rates and model limits can change the cost at production volume. Confirm operational details and adapt model identifiers and any provider-specific request handling.
Trade-off: It remains a hosted service, and model limits and operational terms need checking.
7.4/10from
Custom - Multimodal API workloads
SiliconFlowSiliconFlowSiliconFlow is a hosted API for model choice and pay-per-use multimodal inference, and it is listed as OpenAI-compatible. That makes it worth evaluating when you want another API provider without taking on GPU operations. Confirm model coverage, operational limits and usage costs for your traffic, then check how much of your existing client integration carries over.
Trade-off: Compatibility does not guarantee identical models, limits or production behavior.
7.4/10from
Custom - Latency-sensitive inference
Cerebras InferenceCerebras InferenceCerebras Inference is listed as an OpenAI-compatible API for low-latency workloads, with in-region deployment among its target uses. It is a candidate when those deployment needs matter more than keeping one serving provider. Confirm production cost and limits with the vendor, and validate request compatibility and model availability before routing traffic from Together.
Trade-off: Production pricing and limits need vendor confirmation.
6.4/10from
Custom
03Side by side
| Tool | Score | From | Free plan | Best for |
|---|---|---|---|---|
 Together AI Together AI (current) | 7.4 | Free | Yes | Teams serving open models |
| GroqCloud GroqCloud | 7.5 | Free | Yes | OpenAI-compatible inference integrations |
| Runpod Runpod | 7.2 | $0.27/mo | No | Teams needing on-demand GPU capacity |
| Ollama Ollama | 7.6 | $16.67/mo | Yes | Developers building local inference into apps |
| SaladCloud SaladCloud | 7.3 | $43.80/mo | No | Cost-sensitive GPU inference |
| DeepInfra DeepInfra | 7.4 | Custom | No | Teams comparing hosted models |
| SiliconFlow SiliconFlow | 7.4 | Custom | No | Teams comparing hosted model providers |
| Cerebras Inference Cerebras Inference | 6.4 | Custom | No | Teams prioritizing low-latency inference |
Questions people ask
Which Together AI alternative is easiest to migrate to?
GroqCloud, SiliconFlow and Cerebras Inference are listed as OpenAI-compatible, which may reduce client changes if your current integration uses that pattern. Compatibility does not guarantee identical model names or behavior. Check the target model and limits, then validate requests and responses before moving production traffic.
Which Together AI alternative is cheapest?
There is no reliable winner from starting prices alone: the listed offers use different models and do not establish a comparable cost for your workload. Compare the models, request volume and billing units you expect to use. Ask vendors about capacity and production pricing before committing.
Which alternatives can I self-host?
Ollama is listed as open source and self-hostable, and SaladCloud is listed as self-hostable. Ollama is a local model runtime; SaladCloud targets GPU infrastructure and containerized workloads. In either case, plan for your own deployment and operations, and confirm the terms and requirements for your setup.