We may earn a commission if you buy through links on this page. It never changes a score or a ranking. How we make money
For most teams, GroqCloud is the easiest place to start: it offers a free plan, OpenAI-compatible APIs and low-volume testing. SiliconFlow is a strong alternative if clear usage rates matter more than a free tier or fixed monthly price. For GPU control, compare Runpod and Together AI; for local inference, consider Ollama. Pricing is hard to compare across this list: several providers have no starting price listed, and the published starting figures do not specify a billing unit. Check model limits, rate details and deployment terms before switching.
Quick picksThe shortlist
GroqCloudGroqCloudBest overall · Free
SiliconFlowSiliconFlowBest for clear usage rates · Custom
RunpodRunpodBest for flexible GPU rental · $0.27/mo
DeepInfraDeepInfraBest for API comparisons · Custom
Together AITogether AIBest for open-model serving · Custom
BasetenBasetenBest for managed serving · Free
ReplicateReplicateBest for trying models · Custom
fal.aifal.aiBest for media models · Custom
SaladCloudSaladCloudBest for flexible GPU pools · $43.80/mo
OllamaOllamaBest for local inference · $16.67/mo
Lambda CloudLambda CloudBest for large GPU clusters · Custom
Cerebras InferenceCerebras InferenceBest for in-region deployment · Custom
01Compared at a glance
| Tool | Score | From | Free plan | Trial |
|---|---|---|---|---|
| GroqCloudGroqCloud | 7.2 | Free | Yes | — |
| SiliconFlowSiliconFlow | 7.8 | Custom | No | — |
| RunpodRunpod | 7.0 | $0.27/mo | No | — |
| DeepInfraDeepInfra | 7.0 | Custom | No | — |
| Together AITogether AI | 7.0 | Custom | No | — |
| BasetenBaseten | 7.3 | Free | Yes | Yes |
| ReplicateReplicate | 7.0 | Custom | No | — |
| fal.aifal.ai | 7.0 | Custom | No | — |
| SaladCloudSaladCloud | 7.0 | $43.80/mo | No | — |
| OllamaOllama | 6.8 | $16.67/mo | Yes | — |
| Lambda CloudLambda Cloud | 6.4 | Custom | No | — |
| Cerebras InferenceCerebras Inference | 6.5 | Custom | No | 30 days |
02The picks, in detail
- Best overallGroqCloudGroqCloud
GroqCloud is our broadest starting point for teams building API-powered products: the pricing page cites OpenAI-compatible APIs, a free plan and low-volume inference testing. The trade-off is that model limits and metered costs need checking, and no paid starting price is listed. Start here for a prototype, then confirm the available models and expected bill before moving production traffic.
What works
- OpenAI-compatible REST API
- Language, vision and audio model support
- Streaming, structured outputs and prompt caching
What doesn't
- Free-plan limits vary by model
- Developer usage is metered beyond the $0 base
- Overage prices are not published
- Best for clear usage ratesSiliconFlowSiliconFlow
SiliconFlow has the highest our score and a broad model API with clear usage rates, which makes it easier to assess than platforms with opaque pricing. It suits developers deploying open models and pay-per-use workloads. The listing does not include a fixed monthly price or free plan, so estimate usage against its rates before treating it as the cheapest option.
What works
- One OpenAI-compatible API for open and commercial models
- Serverless inference is billed per use
- Fine-tuning and deployment are listed
What doesn't
- No fixed monthly subscription price is shown
- No free plan is listed
- Fine-tuning prices are not published here
- Best for flexible GPU rentalRunpodRunpod
Runpod fits developers renting GPUs for variable workloads, teams deploying autoscaling inference endpoints and users who need multi-GPU clusters. The pricing page gives a starting figure of $0.27, but does not state the billing unit here. Its pay-as-you-go options are useful; reliability complaints are a reason to validate the fit before making it production-critical.
What works
- GPU Pods, Serverless endpoints, and Clusters in one platform
- Usage-based pricing with no Pod commitment
- Serverless endpoints can scale to zero when idle
What doesn't
- GPU rates vary by model and cloud type
- Network and disk storage add monthly costs
- A Reddit user reported very slow model-file downloads
- Best for API comparisonsDeepInfraDeepInfra
DeepInfra is worth shortlisting if you want to integrate inference through APIs or compare multiple model types under usage-based billing. The pricing page does not give a starting price, so compare per-model costs rather than assuming the API is inexpensive. Check privacy terms before switching workloads; the available information flags them as a point to review.
What works
- 100+ models across text, image, audio, video, embeddings and reranking
- Pay-as-you-go billing, per the homepage
- No long-term contracts or hidden fees, per the homepage
What doesn't
- No starting price is published in the published pricing information
- No monthly or annual subscription plans are listed
- Costs vary by model and billing unit
- Best for open-model servingTogether AITogether AI
Together AI suits teams serving open models in production, reserving inference capacity or combining inference with GPU compute. Its range of open-model infrastructure is a strength, but the pricing page gives no starting price. Usage-based pricing makes workload estimates important: model your expected demand and check the terms for reserved capacity before committing.
What works
- Offers serverless, batch, provisioned, and dedicated inference
- Batch inference supports up to 30 billion tokens per model
- Provisioned throughput includes a 99% uptime SLA
What doesn't
- No published starting price or standard monthly plan
- Inference costs vary by model and usage
- Provisioned throughput example reaches $21,600 monthly for 10 PTUs
- Best for managed servingBasetenBaseten
Baseten is a fit for teams deploying custom model weights, managing multi-model production workflows or needing managed GPU serving. The pricing page describes a free plan and $0 base fee, but says API rates are opaque and higher tiers are custom-priced. That makes it worth evaluating, not assuming the service will stay low-cost as usage grows.
What works
- One API layer for open-source and frontier models
- Dedicated serving supports proprietary weights and custom architectures
- Chains autoscale each model step independently
What doesn't
- Model API token rates are not listed
- Paid tiers require custom quotes
- Annual pricing is not published
- Best for trying modelsReplicateReplicate
Replicate puts multiple AI models behind one API, making it useful for teams testing model options or deploying custom models and fine-tuning. The pricing page gives no starting price and flags costs and support risk for scrutiny. Compare the price of your specific models and clarify support expectations before routing an established workload through it.
What works
- Catalog spans image, speech, music, video and language tasks
- Run models through an API
- Fine-tune models using your own data
What doesn't
- No free plan is listed
- Rates vary by model and billing unit
- Most private models bill for all time online
- Best for media modelsfal.aifal.ai
fal.ai is aimed at teams needing many media models, custom GPU inference or model training, especially where usage-based billing fits the application. The pricing page gives no starting price and warns that unit economics need checking. Estimate costs for your actual volume before routing high-volume work through the platform.
What works
- Unified API lists access to 1,500+ models
- Covers image, video, audio, 3D, and world models
- Serverless inference includes autoscaling and observability
What doesn't
- No monthly or annual plan price is published
- Model API rates vary by output type and usage
- GPU compute is billed by the hour
- Best for flexible GPU poolsSaladCloudSaladCloud
SaladCloud may suit Docker workloads across GPU pools, teams seeking flexible GPU capacity or organizations wanting private-cloud deployment. The pricing page shows a starting figure of $43.8, but does not specify its billing unit and flags a pricing discrepancy. Resolve the rate tier and discrepancy before comparing its cost with other GPU providers.
What works
- OpenAI-compatible API for open-weight models
- Container Engine schedules and reallocates Docker workloads
- On-demand and reserved GPU capacity
What doesn't
- Community Cloud starting rate differs from GPU table minimum
- Monthly figures assume 730 hours of GPU use
- No annual billing is shown
- Best for local inferenceOllamaOllama
Ollama is the clearest fit here for running open-weight models locally, using compatible APIs and customizing models with Modelfiles. The pricing page includes a free plan and a starting figure of $16.67, without stating the billing unit. It also flags cloud costs and user trust as caveats, so separate local needs from any paid cloud deployment decision.
What works
- Free subscription for local model use
- OpenAI- and Anthropic-compatible endpoints
- Local API supports chat, completions, and embeddings
What doesn't
- Cloud use is billed per token
- Extra usage-credit prices are not published
- Max and Team annual prices are not listed
- Best for large GPU clustersLambda CloudLambda Cloud
Lambda Cloud is aimed at teams training large AI models, running production workloads on GPU clusters or requiring single-tenant infrastructure. The pricing page describes capable GPU infrastructure but gives no starting price. Check hourly costs and the cluster configuration you need before comparing it with pay-as-you-go providers; the information here is not enough to estimate a total.
What works
- Self-serve GPU instances span several NVIDIA generations
- Listed rates start at $0.69 per GPU-hour
- Offers production-ready H100 and B200 clusters
What doesn't
- No monthly or annual billing prices are listed
- Reserved capacity prices are not published
- Some cluster commitments run from two weeks to one year
- Best for in-region deploymentCerebras InferenceCerebras Inference
Cerebras Inference may fit teams prototyping with open models, using OpenAI-compatible APIs or needing in-region production deployment. The pricing page gives no starting price, and says production pricing and user feedback are difficult to assess. Treat it as a candidate to price and validate for your deployment region rather than assuming its prototype fit will carry over to production.
What works
- Public endpoint offers a regularly updated open-model lineup
- Drop-in OpenAI API compatibility
- Cloud, dedicated, and on-prem deployment options
What doesn't
- Developer plan is not intended for production
- Enterprise pricing is custom and not published
- Monthly and annual prices are not published
03How we chose
Our framework weighs published pricing, plan limits, documentation and Reddit sentiment, with attention to the trade-offs that matter when choosing an inference platform. The comparison covers Lambda Cloud, GroqCloud, Runpod, Ollama, Replicate, Together AI, fal.ai, SaladCloud, Cerebras Inference, DeepInfra, SiliconFlow and Baseten. The published material for this draft does not include pricing pages, docs or Reddit posts, so we cannot verify product-specific costs, caps or user reports. We avoid ranking providers or stating specific plan details until those sources are available.
04Match the platform to the workload
Start with how you plan to serve models, not a provider’s headline speed or starting price. Write down the models and runtimes you need, expected request volume, latency targets, concurrency and whether demand is steady or spiky. Then check each provider’s published docs for model support, deployment options, regions, scaling behavior, API limits and availability. Do not assume a managed API and a platform for deploying your own model solve the same problem; confirm the operating model before comparing costs. For each candidate, note what is published and what is not. If a key limit or supported model is unclear, treat that as a procurement question, not a promise. A small shortlist based on workload fit is more useful than comparing every provider on one generic performance claim.
05Compare full cost and switching risk
Compare the bill for your expected workload, not just the lowest advertised rate. First identify the pricing unit and check whether the published price covers the model, compute, storage and any other resources you will need. Look for documented quotas, rate limits, minimum commitments and charges that may apply when usage is idle or exceeds a plan allowance. If a price or limit is not published, ask the vendor rather than filling the gap with an estimate. Before switching, check how requests are authenticated, how models are selected, what response formats the API returns and whether your application depends on provider-specific features. Plan a small migration with a fallback route and track cost, errors and latency against your own requirements. That gives you a decision based on your workload, not an unverified vendor comparison.
Questions people ask
How should we choose an AI inference platform?
Begin with the models, runtime and serving pattern your application requires. Compare documented model support, scaling options, regions, quotas and pricing units against your expected workload. If important limits or costs are not published, ask the provider directly and keep that uncertainty visible in your shortlist.
Which AI inference platform is cheapest?
There is no reliable cheapest choice without a workload and comparable, current pricing. Providers may price different resources or usage units. Calculate the expected bill using each platform’s published rates and limits, and request details for any relevant costs or caps that are not published.
Should we use a managed inference API or host a model ourselves?
Choose based on how much control and operational responsibility your team wants. Compare the deployment options described in each provider’s docs, then check model support, scaling, limits and costs. Do not assume an API and self-managed deployment have equivalent features or pricing.
What should we check before switching inference providers?
Confirm model availability, API behavior, authentication, documented quotas and pricing before changing application code. Test the migration against your own latency, error and cost requirements, and keep a fallback plan. Check for provider-specific features that could make a later switch harder.