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If you’re leaving DeepInfra, first decide what you’re switching away from: API-based inference, a particular model experience, or uncertainty about data handling. GroqCloud, Replicate, Together AI and SiliconFlow keep you in the model API category; Ollama shifts some inference local, while Runpod and Lambda Cloud put more infrastructure in your hands. The listings don’t provide a like-for-like price comparison, so check each provider’s rates, limits and privacy terms against your own workload before moving traffic.
01Why people leave DeepInfra
Privacy is a trust decision
A Reddit thread questions whether zero-data-retention policies are enough. A commenter notes that ZDR is a policy promise, not a technical guarantee.
Provider behavior can differ
In one Reddit thread, a user said DeepSeek’s first-party API felt different from third-party endpoints, including DeepInfra; the cause was not established.
Costs need workload checks
DeepInfra is described as usage-based. The published material does not give its rates, so compare per-model costs before routing production traffic elsewhere.
02The alternatives, ranked by fit
- API product builders
GroqCloudGroqCloudGroqCloud is the closest listed fit if you want to keep an API-based product workflow while comparing another provider. Its listing calls out OpenAI-compatible APIs and a free plan, making it an option for low-volume testing. The catch is explicit: check model limits and metered costs before moving production traffic.
Trade-off: Model limits and metered costs need checking.
7.2/10from
Freefree plan - Usage-based API inference
SiliconFlowSiliconFlowSiliconFlow suits teams that want to keep using a model API and pay for inference as they go. Its listing highlights a broad model API and clear usage rates, useful when you’re comparing per-model costs against DeepInfra. It lists no fixed monthly price or free plan, so budget from your expected usage rather than a subscription figure.
Trade-off: No fixed monthly price or free plan is listed.
7.8/10from
Custom - Multi-model API access
ReplicateReplicateReplicate is worth comparing if you want one API route for testing multiple models rather than tying a workload to one model choice. Its listing also includes custom model deployment and fine-tuning. That flexibility may fit teams evaluating alternatives, but the published assessment flags costs and support risk, so price the workload and review support needs first.
Trade-off: Costs and support risk need scrutiny.
7.0/10from
Custom - Open-model production
Together AITogether AITogether AI fits teams moving from DeepInfra but still looking to serve open models in production. Its listing also covers reserved inference capacity and GPU compute, giving teams more than one infrastructure option to evaluate. The published material does not list a price, and it advises estimating workload costs before committing to usage-based billing.
Trade-off: Estimate usage costs; pricing is not listed.
7.0/10from
Custom - Local model inference
OllamaOllamaOllama is the clearest change of direction if you want to run open-weight models locally instead of sending every request through a hosted inference API. Its listing includes compatible APIs and Modelfiles for customization, plus a free local inference option. The listing also flags cloud costs and user trust as caveats, so check which setup you actually need.
Trade-off: Cloud costs and user trust remain caveats.
6.8/10from
$16.67/mofree plan - Flexible GPU workloads
RunpodRunpodRunpod is a fit if you want to rent GPUs and manage more of the deployment rather than switch to another model API. Its listing covers variable workloads, autoscaling inference endpoints and multi-GPU clusters, with a displayed starting price of $0.27. The listing also notes reliability complaints, so weigh those against the flexibility before moving a production workload.
Trade-off: Reliability complaints temper the value.
7.0/10from
$0.27/mo - Dedicated GPU clusters
Lambda CloudLambda CloudLambda Cloud is a bigger infrastructure shift for teams whose reason to leave is a need for GPU clusters or single-tenant infrastructure, rather than a different API endpoint. Its listing targets production workloads and large-model training. It may not suit teams seeking a simple inference API replacement; check hourly costs and cluster requirements before committing.
Trade-off: Check hourly costs and cluster needs.
6.4/10from
Custom
03Side by side
| Tool | Score | From | Free plan | Best for |
|---|---|---|---|---|
 DeepInfra DeepInfra (current) | 7.0 | Custom | No | Teams integrating inference through APIs |
| GroqCloud GroqCloud | 7.2 | Free | Yes | Developers building API-powered products |
| SiliconFlow SiliconFlow | 7.8 | Custom | No | Teams building on one model API |
| Replicate Replicate | 7.0 | Custom | No | Teams integrating multiple AI models |
| Together AI Together AI | 7.0 | Custom | No | Teams serving open models in production |
| Ollama Ollama | 6.8 | $16.67/mo | Yes | Running open-weight models locally |
| Runpod Runpod | 7.0 | $0.27/mo | No | Developers renting GPUs for variable workloads |
| Lambda Cloud Lambda Cloud | 6.4 | Custom | No | Teams training large AI models |
Questions people ask
Which DeepInfra alternative is cheapest?
The published material does not support a like-for-like cheapest pick. GroqCloud has a free plan, Ollama lists free local inference, and Runpod shows a starting price of $0.27, but the listings don’t give comparable workload costs. Compare the rates and limits for the models and usage you expect.
Does DeepInfra offer zero data retention?
The published DeepInfra information does not state its data-retention terms. A Reddit thread discusses zero-data-retention policies, and one commenter cautions that ZDR is a policy promise rather than a technical guarantee. Check DeepInfra’s current terms directly before sending sensitive prompts, and make the same check with any replacement.
Will the same model behave the same through another provider?
Don’t assume so. In one Reddit thread, a user reported a different experience with DeepSeek’s first-party API than with third-party providers, including DeepInfra. The thread did not establish the cause; commenters raised harness, context length and possible quantization as factors. Compare your own model and workflow before switching traffic.