GoodLads vs Prime Agent: Features, Pricing & Which Is Better (2026)

A side-by-side comparison of GoodLads and Prime Agent — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.

GoodLads

Paid

AI growth manager for Google Ads that turns account performance into testable hypotheses and ships each one only on your approval.

Hypothesis Feed: Daily analysis of search terms, keyword quality, geography, and audiences produces a ranked list of ideas, each naming the campaign and the spend at risk.
One-Click Shipping with Approval Gate: Any proposed change is applied in a single click but never without explicit owner approval, and live ads are not edited directly.
Kanban Verdict Board: Hypotheses move through Proposed, Scheduled, Live, and Completed so every test ends with a measured verdict rather than being forgotten.
Account Treemap Overview: Campaign spend, conversions, and ROAS roll into one visual overview sized by spend and coloured against the account average.
Least-Risky Lever Selection: Recommendations favour reversible mechanisms such as 50/50 RSA experiments, stepped target CPA changes, and new paused assets.
Predicted vs Measured Reporting: Each completed experiment compares the predicted lift against the actual result, with budget shifting to the winner.
Claude Code and Codex Integration: The same workflows can be driven from Claude Code or Codex for teams that work from a coding agent.

Performance Review: Get a single overview of how every campaign is doing on spend, conversions, and ROAS without building reports by hand.
Wasted Spend Discovery: Surface negative keyword opportunities, poor keyword-ad combinations, and geography issues that are draining budget.
Budget-Capped Campaigns: Identify campaigns limited by budget and lower target CPA in reversible steps to buy cheaper conversions at the same spend.
Ad Copy Testing: Run benefit-led versus price-led headline experiments as 50/50 splits instead of editing live ads.
Seasonal Campaign Prep: Stage seasonal copy and sitelink assets in advance, ready for one-click approval when demand spikes.
Agency Account Management: Manage optimisation hypotheses across multiple client accounts from one board with a shared approval workflow.

Prime Intellect

Freemium

A self-improving RLM coding agent from Prime Intellect that can refine its own harness on a training-inference-compute stack you own.

Continual Harness: The agent can modify and refine its own scaffolding — tools, prompts, and evaluation criteria — during long-running work.
RLM Foundation: Built on Reasoning Language Models rather than plain chat models, so multi-step planning and self-critique are first-class.
One-Line Install: Bootstrap the agent locally with a single curl-piped shell script — no infra setup, no configuration.
Integrated Training Loop: Capture production traces, cluster failures, convert misses into RL environments, and train adapters that make the model cheaper and more reliable for your workflow.
2,500+ RL Environments: Train and evaluate against a community-curated environment hub (verifiers-based), including SWE, terminal, search, and science tasks.
Owned Inference Stack: Deploy the improved agent on dedicated GPUs, serverless APIs, or LoRA adapters served alongside base models with a 1-click flow.
Global GPU Access: On-demand H100/H200/B200/B300 or reserved clusters from 50+ datacenters, orchestrated with SLURM/K8s and Grafana monitoring.

Autonomous Coding: Run a self-improving harness over your repository that plans, edits, and validates changes over long sessions.
SWE-Bench Style Benchmarks: Iterate the agent against tasks like mini-swe-agent-plus and Verifiers-based SWE environments.
Training Custom Agents: Post-train your own domain-specific coding agent on captured traces (Ramp beat frontier models on spreadsheet search this way).
Enterprise Deployment: Serve the improved agent on private dedicated inference with LoRA adapters and OpenAI-compatible APIs.
Research on Continual Learning: Study how agents self-modify their harness while progress remains auditable and reversible.
Cost Reduction: Turn expensive frontier calls into cheaper fine-tuned adapters that specialize in your codebase and workflow.