Ollama Cloud Review: From Local LLMs to Seamless Cloud Inference

Ollama changed how developers interact with open-source LLMs. The ollama run llama4 experience - one command, no configuration, model running in seconds - brought local LLM inference to hundreds of thousands of developers who had never compiled a model in their lives. Ollama Cloud, launched in public beta on January 15, 2026, asks the natural next question: what if that same simplicity worked in the cloud?

The pitch is ollama push to upload a model (including custom fine-tunes and quantizations) and ollama serve --cloud to get a globally distributed inference endpoint. After four weeks of testing, we found a product that nails the developer experience and stumbles on production readiness.

What Ollama Cloud Is

Ollama Cloud is a managed inference service that extends Ollama's local CLI to the cloud. You use the same ollama commands, the same Modelfile format, and the same OpenAI-compatible API - but instead of running on your laptop's GPU, models run on Ollama's cloud infrastructure across data centers in the US, Europe, and Asia-Pacific.

The service offers two modes:

Shared inference: Your API calls are routed to a shared pool of GPUs running popular models. Latency is variable but cost is low. This is analogous to a serverless function - you pay per token, and cold starts are handled transparently.

Dedicated endpoints: You reserve GPU capacity for a specific model. Latency is consistent and low, but you pay per hour regardless of usage. Pricing starts at $0.80/hour for a single A10G instance.

Ollama Cloud supports every model in the Ollama library - currently over 400 models - plus custom models you push from your local machine. This includes fine-tuned models, custom quantizations, and Modelfile-configured variants with custom system prompts and parameters.

The Developer Experience

This is where Ollama Cloud earns its score. The workflow is seamless:

ollama pull llama4-maverick
ollama push llama4-maverick
ollama serve llama4-maverick --cloud --region us-east

Three commands. You now have a globally accessible API endpoint serving Llama 4 Maverick. The endpoint is OpenAI-compatible, so any application that works with the OpenAI SDK works with Ollama Cloud by changing the base URL.

For custom models, the process is equally clean. We fine-tuned a Llama 3.3 8B model locally using Unsloth, created a Modelfile with custom parameters, and pushed it to Ollama Cloud. Total time from fine-tuning completion to live cloud endpoint: 4 minutes. The majority of that was upload time.

The dashboard at cloud.ollama.com shows real-time metrics for each deployed model: requests per second, token throughput, latency percentiles, GPU utilization, and cost accumulation. It is clean, functional, and provides exactly the information you need.

Integration with existing tools is strong. Ollama Cloud works with LangChain, LlamaIndex, Open WebUI, Continue, and any tool that supports the OpenAI API format. If your tool works with local Ollama, it works with Ollama Cloud with a URL change.

Performance Benchmarks

We compared Ollama Cloud's shared inference against Groq, Together AI, and Fireworks for popular open-source models.

On raw throughput, Ollama Cloud is the slowest option in this comparison. Groq is 13x faster, and GPU-based competitors are roughly 2x faster. The gap is partly architectural - Ollama Cloud's shared inference pool uses consumer-grade GPUs (A10G, L4) rather than the H100s and LPU chips that competitors deploy.

Dedicated endpoints tell a better story. With a reserved A100 instance, Ollama Cloud's Llama 4 Maverick throughput jumped to 210 tokens per second - competitive with Together AI and Fireworks, though still far behind Groq.

Time-to-first-token on shared inference averaged 1.2 seconds with a p95 of 3.8 seconds. The variance comes from cold starts - when your request hits a GPU that does not have the model loaded, Ollama Cloud needs to load it into VRAM. Popular models (Llama 4, Mixtral) rarely cold-start; niche models can take 10-15 seconds.

Custom Model Support

This is Ollama Cloud's genuine differentiator. No other inference provider makes it this easy to deploy custom models.

Together AI and Fireworks support custom model hosting, but the process involves converting to their specific format, submitting a deployment request, and waiting for provisioning. Ollama Cloud's approach - push your local model, get an endpoint - takes minutes instead of hours.

We tested with five custom models: a fine-tuned Llama 3.3 8B for code review, a quantized Qwen 3 32B (4-bit GPTQ), a Modelfile-configured Gemma 3 9B with a custom system prompt, a LoRA adapter on Llama 4 Scout, and a full fine-tune of Mistral 7B. All five deployed successfully. The LoRA adapter deployment was particularly impressive - Ollama Cloud merged the adapter with the base model during push and served the merged model without any manual steps.

For teams iterating on fine-tuned models, this workflow is a substantial productivity improvement. Push a new version, test against your evaluation suite, roll back if needed - all through familiar Ollama commands.

Pricing

Ollama Cloud's pricing sits between the cheapest options (Groq, shared endpoints) and the most expensive (dedicated GPU instances on cloud providers):

Shared inference: $0.15-0.60 per million input tokens, $0.30-1.20 per million output tokens, depending on model size. Roughly 50% more expensive than Groq for the same models, and 10-20% more than Together AI.

Dedicated endpoints: $0.80/hour (A10G), $2.40/hour (A100 40GB), $4.80/hour (A100 80GB). Competitive with equivalent GPU instances on AWS or GCP, with the added benefit of Ollama's management layer.

The free tier is generous: $10 of shared inference credits per month, enough for approximately 30,000 Llama 4 Maverick requests. For development and testing, this is sufficient.

What Holds It Back

Shared inference performance is not competitive. At 95 tokens per second for Llama 4 Maverick, Ollama Cloud is 2x slower than GPU-based competitors and 13x slower than Groq. For production applications where latency matters, shared inference is not viable.

Cold starts on niche models are painful. The 10-15 second cold start for unpopular models makes them impractical for interactive use. Dedicated endpoints solve this, but at $0.80+/hour.

No SLA on shared inference. Ollama Cloud's terms of service explicitly state that shared inference has no uptime or latency guarantees. For production workloads, this is a problem. Dedicated endpoints come with a 99.9% SLA.

Model size limits. Shared inference supports models up to 70B parameters. Larger models (Llama 4 Maverick at 400B+ total parameters works because of MoE routing only 17B active) require dedicated endpoints. The largest supported model on a single dedicated endpoint is constrained by A100 80GB VRAM.

Beta rough edges. We encountered intermittent 502 errors during our testing period (roughly 0.5% of requests), and the billing dashboard had a bug that double-counted tokens for streaming requests (acknowledged and fixed during our test period).

Strengths and Weaknesses

Strengths:

• Seamless local-to-cloud workflow with familiar Ollama commands
• Best-in-class custom model deployment (push and serve in minutes)
• OpenAI-compatible API works with existing tools
• 400+ models available out of the box
• Generous free tier ($10/month credits)
• LoRA adapter merging and deployment
• Clean dashboard with real-time metrics
• Three global regions (US, EU, APAC)

Weaknesses:

• Shared inference 2-13x slower than competitors
• 10-15 second cold starts on niche models
• No SLA on shared inference tier
• 50% more expensive than Groq for equivalent models
• Beta stability issues (0.5% error rate)
• Model size limits on shared infrastructure
• No fine-tuning in the cloud (local only, then push)
• Dedicated endpoints start at $0.80/hour with hourly billing

Verdict: 7.5/10

Ollama Cloud is the easiest way to get a custom model into production. The push and serve workflow is unmatched, and for teams iterating on fine-tuned models, the time savings compared to any alternative are substantial. If you are already using Ollama locally - and millions of developers are - the cloud extension feels natural and requires almost zero learning.

The performance gap is the problem. For production applications using standard models, Groq, Together AI, and Fireworks all deliver better throughput at lower or comparable prices. Ollama Cloud's shared inference is adequate for development, testing, and low-traffic applications, but not for latency-sensitive production workloads.

The ideal Ollama Cloud user is a developer or small team that wants to deploy custom or fine-tuned models to the cloud with minimal DevOps overhead and does not need the absolute fastest inference speeds. For that specific use case - and it is a large one - Ollama Cloud is the best option available. For everything else, the dedicated inference providers remain the better choice.

Sources:

• Ollama Official Site - Ollama
• Ollama Cloud Documentation - Ollama
• Ollama Cloud Launch Announcement - Ollama Blog
• From Local to Cloud: Ollama's Infrastructure Play - The New Stack