Timbal helps teams turn AI prototypes into production systems. Build agents and workflows, connect them to your data, design interfaces, deploy, monitor, evaluate, and govern everything from one platform. Instead of assembling separate tools for retrieval, orchestration, UI, observability, and evals, Timbal gives you one core for shipping reliable AI applications.
How does pricing scale as you add more agents and team members, especially once you start hitting heavier eval runs on bigger workloads?
About Timbal AI on Product Hunt
“Build AI agents, workflows, and apps in one stack”
Timbal AI launched on Product Hunt on July 9th, 2026 and earned 299 upvotes and 76 comments, earning #1 Product of the Day. Timbal helps teams turn AI prototypes into production systems. Build agents and workflows, connect them to your data, design interfaces, deploy, monitor, evaluate, and govern everything from one platform. Instead of assembling separate tools for retrieval, orchestration, UI, observability, and evals, Timbal gives you one core for shipping reliable AI applications.
On the analytics side, Timbal AI competes within Productivity, SaaS and Artificial Intelligence — topics that collectively have 1.2M followers on Product Hunt. The dashboard above tracks how Timbal AI performed against the three products that launched closest to it on the same day.
Who hunted Timbal AI?
Timbal AI was hunted by Ben Lang. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
Ben Lang
How does pricing scale as you add more agents and team members, especially once you start hitting heavier eval runs on bigger workloads?