Check out my comparison spreadsheet: Tabnine features vs price per seat

I'm a platform lead at a 300-person fintech. We run Tabnine Enterprise on our own K8s cluster for about 150 devs, alongside our standard GitLab CI and GitHub for source.

1. Actual Codebase Context: Tabnine's "Repository Aware" feature needs explicit project root indexing per IDE. It's not automatic like GitHub Copilot's pull request suggestions. You get good single-repo context when set up, but cross-repo is manual.
2. Enterprise Pricing Reality: The published "contact us" price is a starting point. Our final negotiated cost was about $12/user/month for 150 seats, committed annually. That's far below their list, but you must push.
3. On-Prem Deployment Load: You'll need a dedicated infra team. We allocate three ~8-core nodes with 32GB RAM each for our cluster. Sync with GitHub/GitLab via service account is straightforward, but model updates require a maintenance window.
4. Where It Breaks: For large monorepos (our core is 8GB), the initial index can take hours and sometimes times out. Support had us tweak JVM heap size. It's solid for daily work after that, but the first-time experience is rough.

My pick is Tabnine Enterprise, but only if you have a hard requirement for air-gapped or private-cloud AI and the internal staff to run it. If you're cloud-first and your team lives in GitHub already, Copilot for Business is less friction. Tell us your team's top need: is it privacy-at-all-costs, or is it deep PR integration?

> **Local vs. Cloud Models:** Privacy and offline capability.

Your column is right, but misses the data transfer cost nuance. With Tabnine Enterprise's local models, you still pay for egress when the IDE client fetches the initial model from your on-prem server to the developer's machine. At scale, this can add up.

You might add a column for "Initial Model Download Size". For the large code model, it's ~4GB per user. With 150 devs, that's 600GB of internal network transfer just for the first sync.

That's a solid starting framework for a comparison. I'd suggest adding a column for audit logging and data retention, especially for the Enterprise/on-prem tiers.

You're tracking where the model runs, but not necessarily *what* it logs. For compliance (SOX, HIPAA, even internal IP protection), you need to know if the system logs prompts, completions, and which user generated what code snippet. Can you export those logs to your SIEM? What's the default retention period?

The privacy claim for local models is strong, but if there's no audit trail of usage, you've just traded one risk for another.

You're absolutely right to push on audit logging. The sales pitch always focuses on "your data never leaves," but that says nothing about accountability inside the perimeter.

I'd add that the log export capability is often a checkbox in the enterprise contract, not a standard feature. In our evaluation, Tabnine's SIEM integration required setting up a separate log forwarder container in the K8s deployment, and the default retention was a shockingly low 30 days. We had to specify a longer retention period and hook it into our Splunk instance ourselves.

It creates an interesting trade-off: with a cloud service like Copilot, you get detailed, searchable usage analytics out of the box, but your data is exposed to the vendor. With a local model, you own the data but also own the entire burden of making it auditable.

That's a great practical point. It's the kind of hidden infra detail that only shows up when you actually deploy.

I'd extend it a bit: that initial 4GB pull per dev is also a real drain on the local SSD in their laptop, especially if you're also running other local tools like Docker. We've had devs on older MacBooks hit storage warnings after loading a couple of these large models.

On the flip side, once it's cached locally, the offline benefit is legit. Our team in a manufacturing plant with spotty VPN connectivity can still get completions. But you're right, you pay the network and storage tax upfront.

That's a good point about the local storage hit. It makes me wonder if there's any way to manage that download centrally, like pushing the model as a standard package through IT, instead of having each dev's IDE pull it individually. Would that even work with their licensing?