Coding · Head-to-head

Ollama vs T3MP3ST

Ollama vs T3MP3ST: pricing, features, and which to pick in 2026.

The verdict

Pick Ollama if…

→overall capability matters more than price (AI Score 8.8 vs 7.8)
→you want our editor's pick for this category
→your primary use case is anyone who wants open-weight models running on their own hardware for privacy, offline work or zero marginal cost, with a cloud fallback for models that will not fit.
→you need: productivity

Try Ollama →

Pick T3MP3ST if…

→budget is the constraint
→your primary use case is security researchers and bug-bounty hunters who want to point ai coding agents they already use at authorized targets for autonomous vulnerability hunting.
→you need: research

Try T3MP3ST →

Spec	Ollama	T3MP3ST
Category	Coding	Coding
Pricing model	freemium	free
Headline pricing	Free, with Pro at $20/mo and Team at $25/seat/mo for cloud usage	Free, open source (AGPL-3.0) — you pay for the underlying AI agent's API usage
Free tier	Yes, and it is the main event. Local inference is free forever on your own hardware. The paid tiers exist to buy cloud capacity, and their allowances are published only as multipliers of an unstated base.	Entire project is free and open source under AGPL-3.0; the only real cost is the token/subscription spend of whatever AI agent you plug in.
AI Score	8.8/10	7.8/10
Best for	Anyone who wants open-weight models running on their own hardware for privacy, offline work or zero marginal cost, with a cloud fallback for models that will not fit.	Security researchers and bug-bounty hunters who want to point AI coding agents they already use at authorized targets for autonomous vulnerability hunting.
Editor's pick	✓ Yes	—
Use cases	development agents productivity	development agents research
Date added	2026-08-02	2026-07-05

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Coding · freemium

✓Local inference costs nothing per token and keeps prompts and files on the machine
✓Works offline, which Ollama calls out specifically for mission-critical work
✓Acts as the runtime under other agents, including ones named on its own front page, so it slots into existing stacks
✓Cloud fallback covers models too large for a laptop without switching to a different tool or vendor
✓States that prompt and response data is never logged or trained on, with zero data retention on the Team plan

×Cloud allowances are published only as multipliers ("50x more than Free", "5x more than Pro") against a base quantity Ollama never states
×The $100 Max tier is currently listed as paused for new sign-ups, so the top individual plan may not be available
×Local model quality is capped by your RAM and GPU, and open weights still trail frontier hosted models on the hardest tasks
×Comfort with a terminal, model files and quantisation choices is assumed, which puts it out of reach for non-technical users

Coding · free

✓Free and fully open source (AGPL-3.0) — the orchestration layer is inspectable, which matters for a tool aimed at sensitive targets
✓Agent-agnostic: runs on coding agents you may already pay for (Claude Code, Codex) instead of a weaker bundled model
✓Multi-agent approach can parallelize recon and exploitation work that would be tedious to drive by hand in a single session
✓Comes from a well-known red-teaming researcher, so the workflow is designed by someone fluent in offensive security

×Early-stage GitHub framework, not a polished product — expect setup friction, thin docs, and self-reported benchmarks rather than independent validation
×Real cost is hidden: fanning out multiple agents can burn through API tokens or agent subscription limits quickly
×AGPL-3.0 copyleft complicates embedding it in closed-source or commercial security products
×Dual-use by nature — only appropriate for authorized testing, and autonomous runs still surface false positives that need expert triage