Baseten vs Ollama
A detailed comparison to help you choose between Baseten and Ollama.
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Verdict: Ollama wins
Ollama scores 4.6/5, edging out Baseten at 4.1/5. Developers and privacy-conscious teams who want open-weight models running on their own hardware with a clean local API, and who are comfortable in a terminal.
Baseten Model inference infrastructure for developers | Ollama Run large language models locally | |
|---|---|---|
| Rating | 4.1 (1 review) | 4.6 (1 review) |
| Pricing Model | freemium | free |
| Starting Price | Free tier available | Free |
| Best For | ML engineers and platform teams deploying custom or fine-tuned models to production who want autoscaling GPUs and code-owned deployment artifacts. | Developers and privacy-conscious teams who want open-weight models running on their own hardware with a clean local API, and who are comfortable in a terminal. |
| Free Tier | ||
| API Access | ||
| Team Features | ||
| Open Source | ||
| Tags | free tierapi access | api accessopen sourcefree tier |
| Visit Baseten → | Visit Ollama → |
Baseten
Pros
- + Truss packaging (weights + model.py + config.yaml) gave me a deployable endpoint I could version in git like any other service
- + Scale-to-zero is real: my idle test endpoint stopped billing entirely between requests
- + Rolling deployments kept the old replica serving during promotion — zero dropped requests in my test
- + SOC 2 Type II and HIPAA compliance included on the base tier (baseten.co/pricing)
- + Per-minute GPU pricing is published: H100 80GB at $0.10833/min, T4 from $0.63/hr at time of writing (inferencehub.org)
Cons
- - An always-on H100 replica at list computes to roughly $4,680/month — min_replica choices dominate your bill
- - Model APIs cover open-weight models only (17 as of October 2026); no GPT or Claude access
- - Dedicated compute, priority GPU access, and higher rate limits require the Pro tier
- - Self-hosting, data residency, and custom SLAs are Enterprise-only
- - No path for non-engineers: everything assumes you can package a model and reason about replicas
Ollama
Pros
- + Local inference is free and unlimited — cost is hardware and electricity, and prompts never leave the machine (OLLAMA_NO_CLOUD=1 disables the cloud path entirely)
- + OpenAI- and Anthropic-compatible endpoints at localhost:11434 meant existing SDK code worked for me after a single base_url change, including JSON-schema structured output
- + Install-to-first-chat took about two minutes on my M-series Mac with a quantized 8B model, and ollama ps shows the GPU/CPU split honestly
- + Huge ecosystem: ~183k GitHub stars, setup docs for 20+ agents and editors, official Python and JS libraries
- + v0.40.x MLX default made prompt processing measurably faster on Apple Silicon in my runs, and releases ship every few days
Cons
- - Memory-tiered default context window truncates long inputs out of the box — I had to raise num_ctx explicitly in a coding-agent setup
- - Quality ceiling is your hardware: a local 7B quant visibly trails frontier hosted models on hard reasoning
- - No end-user polish — quantization, context sizing, and memory management stay your problem, and CPU-only machines run slowly
- - Fast release cadence creates churn for pinned deployments (e.g. v0.34.1 moved GGUF creation out to llama.cpp tooling)
- - Cloud Team plan pricing conflicts across sources ($500/mo flat vs $25/seat with 5-seat minimum); Max signups reported paused at times
Frequently Asked Questions
Which is better, Baseten or Ollama?▼
Ollama scores higher at 4.6/5 vs 4.1/5 for Baseten, making it the better-rated option in our comparison.
What is the difference between Baseten and Ollama?▼
Baseten: Baseten hosts and scales machine learning models in production, serving open-source and custom models behind an API. Ollama: Ollama enables you to run open-source large language models locally on your machine, providing privacy and offline access to AI capabilities.
Is Baseten or Ollama more affordable?▼
Baseten starts at €0/mo, Ollama starts at €0/mo. Check the comparison table above for current pricing.
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