Which Together AI fine-tuning jobs are still running, and did any fail?
Together AI connects Memtro to your Together AI project's operations. It reads fine-tuning jobs with status, progress and recent events, batch inference jobs, and dedicated endpoints with their deployments and replicas. It also reads GPU clusters, uploaded files by id and purpose, and evaluation jobs with headline results. When asked and granted, it can start a batch job, cancel a batch or a fine-tune, scale a dedicated deployment up, down or to zero, and delete a file or a GPU cluster. Memtro never runs inference with the key, and prices, file names and file contents are not returned.
Ask in plain English from chat, your editor, a scheduled job or your own agent. The model picks the right Together AI tool, reads what it needs, and answers with the source cited.
These tools change something in Together AI. The model is told to use them only when you ask for exactly that, and an administrator grants each one by name to the teams that may use it; a personal connection acts only after its owner allows it.
Once Together AI is connected, these are the kinds of things people ask. Plain questions, no syntax. If the answer lives partly in another system you have connected, Memtro looks there too.
Which fine-tuning jobs are running, and what stage has each reached?
Did any batch jobs fail this week?
How many replicas are ready on each dedicated endpoint?
What were the headline results of the latest evaluation?
Cancel the batch job that started this morning.
Sarah Chen, ML engineer at Acme Ltd, asked why her fine-tune had not finished. Memtro read the job and its recent events, which showed the stage it reached and the error it logged. She fixed the training file and started a new job.
Tom Reilly, platform lead at BigCo, asked Memtro to stop the test endpoint's deployment for the weekend. With scaling granted to his team, Memtro set its replicas to zero and the deployment stopped. Requests to it failed until he asked for it to be scaled back up on Monday.
The teams that lean on Together AI most, a question each of them asks, and what they connect beside it.
A record in Together AI is rarely the whole story. Connect the systems that hold the rest and Memtro checks them together, then remembers what it learned so the next question starts further along.
Using something else for the same job, or both? Each connector has its own tools; connect as many as you like.
UptimeRobot
Honeycomb
Cronitor
FireHydrant
StatusCake
GlitchTip
GLPI
Langfuse
LibreNMS
NetBox
Snipe-IT
Woodpecker CI
Zabbix
LangSmith
Helicone
GrowthBook
Drata
Intruder
GitGuardian
Secureframe
ilert
JumpCloud
ConfigCat
Split
Aikido Security
Astronomer
Comet Opik
CrowdStrike Falcon
Detectify
Domotz
LambdaTest
LangWatch
LlamaCloud
Logz.io
Lunary
NinjaOne
Northflank
Portkey
PromptLayer
PRTG Network Monitor
Raygun
Retell AI
Runpod
Shodan
Spike.sh
Squadcast
Tenable
UpGuard
Uptime.com
Vanta
Zenduty
Cortex
WorkOS
Kandji
Automox
Huntress
SimpleMDM
Healthchecks.io
Rapid7 InsightVM
ZenhubIn Together AI, open Project settings, then API keys, and choose Create API Key. Copy it straight away, as it is shown once.
In Memtro, open Connections → Together AI and paste the API key. Leave the project field blank for a project key, and fill it only for an organisation key.
A project key reaches billed compute, so an administrator grants the batch, scaling and delete actions by name only to the teams that may use them.
Ask which fine-tuning jobs are running and what stage each has reached.
Chat in the dashboard, the OpenAI-compatible API, scheduled jobs, and any MCP client: Claude Code, OpenCode, Cursor, Claude Desktop. The Together AI tools are the same everywhere.
Only when you ask, and only what it has been granted: start a batch inference job from an uploaded file (billed per token); cancel a batch job; cancel a fine-tuning job; scale a dedicated endpoint deployment, or stop it with 0 replicas; delete an uploaded file; delete a GPU cluster. Every other tool reads, searches or lists. The Connections page labels the tools that act, an administrator grants each one by name to the teams that may use it, and a personal connection acts only after its owner allows it.
A personal connection serves only your own requests. A shared connection serves the organisation: everyone, or only the teams an admin chooses. Admins can always add shared connections; other members need the permission.
Credentials are encrypted at rest. Tool results are cached briefly so agent loops do not repeat identical calls, and the facts the model extracts are saved to your organisation's memory with provenance (system, record and link) so they can be checked and superseded.
API key. In Together AI, open Project settings, then API keys, and choose Create API Key. Copy it straight away, as it is shown once.
Seven-day free trial, then one monthly price per organisation. Connectors, jobs and memory are never metered.
Sign in, add a provider key, create an API key and point your tools at Memtro. It takes about five minutes.
7-day free trial · card on file, nothing charged until it ends · cancel any time
