Together AI Outage History: How to Find It, and Why It Is Incomplete
Everyone looking for "Together AI outage history" wants one of three things: to know whether today's failure is Together AI's fault, to justify a fallback provider, or to prove downtime to someone else. The published record answers only the first, and not reliably.
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Together AI publishes an incident history at status.together.ai. It is the right first stop and the wrong last one. A provider status page is a publishing decision — a record of incidents Together AI declared, after an internal threshold was crossed and a human confirmed it — not a measurement of what your requests experienced.
The short version
status.together.ai is Together AI's declared history. Your own probe log is the observed history. The gap between them is where most production incidents live, and it is the only one of the two you can use as evidence.
Where Together AI Incident History Actually Lives
There are four sources, and they surface different things. Checking only the first is the mistake almost everyone makes.
| Source | What it captures | What it misses |
|---|---|---|
| status.together.ai | Declared incidents with start, update and resolution timestamps, plus any postmortem Together AI chose to publish. | Sub-threshold degradations, single-model failures, and the window between impact starting and the incident being declared. |
| Community reports | Real-time confirmation from other developers on the Together AI community forum, Discord, or the relevant subreddit — usually minutes ahead of the status page. | Structure and durability. Useful for "is it just me" right now; useless as a historical record in three months. |
| Third-party outage trackers | Crowd-sourced spikes, which cluster around consumer-facing failures and catch some incidents earlier than the provider does. | API-specific failure. the Together AI API can be broken while nobody reports it, because API users file tickets rather than outage reports. |
| Your own probe log | Every failed request you sent, with timestamp, model, region, status code and latency. Complete, and admissible as evidence. | Nothing that matters to you — but it only covers the period since you started recording, which is why starting today beats starting after the next outage. |
Why the Published History Under-Counts
Four structural reasons, none of which imply bad faith on Together AI's part. Status pages are built for broad communication, not for your SLA math.
- Declaration lag. Impact starts before an incident is confirmed and published. That gap — often 10 to 40 minutes — is invisible in the history but fully visible to your users.
- Threshold effects. A degradation affecting a minority of requests may never justify a public incident, while still breaking every user who hit it.
- Aggregation. A platform-level green light can hide a single broken component. For Together AI specifically, the common shape is a single model in the shared catalog degrading or being pulled while the platform reports healthy, because catalog breadth means per-model availability varies more than platform availability.
- Retention and editing. Incident histories get pruned, and wording is revised after the fact. A record you do not control is not evidence.
None of this makes status.together.ai useless. It makes it a corroborating source for a record you keep yourself.
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Build Your Own Together AI Downtime Record
The goal is a log that answers "was Together AI down at 14:20 UTC on the 9th, and for how long?" without depending on anyone else's publishing decisions. Five things make it defensible.
- Probe the models you actually call. A check against Together AI's marketing site or a single cheap endpoint tells you nothing about the open-model catalog served on shared and dedicated endpoints under production conditions. Probe each model family you depend on separately.
- Record latency, not just status. A 200 that arrived after your user gave up is downtime. Store the response time on every probe so you can reconstruct degradations, which is where most of the real history is.
- Separate the error classes. 401 is your key, 429 is your quota, 400 is your payload. Only sustained 5xx and timeouts belong in a provider availability number — mixing them in produces a history you cannot defend.
- Probe from more than one region. Single-region monitoring turns your own network incidents into false Together AI outages, and misses regional degradation completely.
- Retain the raw records, in UTC. Aggregated dashboards are not evidence. Keep the per-request rows long enough to cover any credit-claim or contract window that applies to you.
This is precisely what API Status Check runs continuously against Together AI and the rest of your provider stack: independent probes, per-error-class breakdowns, latency retained alongside status, and a history you own rather than one the provider curates.
Reading an Incident Record Without Fooling Yourself
Once you have both histories, the temptation is to count incidents and call it a reliability score. Incident counts are the least meaningful number in the file.
Count these instead
- Total impacted minutes, weighted by the share of requests that failed — one four-hour partial outage matters more than eight brief blips.
- Time to detection, measured from your first failed probe to the provider's declaration. This number tells you how much independent monitoring is worth to you.
- Failure concentration. Repeated incidents on one model or one region is an architecture signal, not bad luck.
- Correlation with your fallback. If your backup provider fails in the same windows, you do not have a fallback — you have a shared dependency.
A provider with more declared incidents and faster, more honest declarations is usually a better bet than a quiet one. Silence is not uptime.
What to Do With the History Once You Have It
An outage history is only worth keeping if it changes a decision. Three it should inform directly:
- Whether to add a fallback. If measured impacted minutes exceed what your own uptime commitment allows, a second provider stops being optional. See the Together AI failover guide for the wiring.
- What you can promise downstream. Your composite availability is capped by your weakest single-provider dependency. Run the numbers in the Together AI SLA and uptime guide before signing anything.
- How you write the postmortem. Customer trust survives an outage you can explain with timestamps far better than one you attribute vaguely to "an upstream provider".
Providers That Fail Independently of Together AI
A fallback only helps if its outage history does not overlap Together AI's. A second key on the same provider shares the same control plane and the same bad day.
Groq
Separate control plane and capacity pool, so its incident history is largely uncorrelated with Together AI's. That independence is what makes it useful as a fallback.
Check Groq status →Mistral
Separate control plane and capacity pool, so its incident history is largely uncorrelated with Together AI's. That independence is what makes it useful as a fallback.
Check Mistral status →OpenAI
Separate control plane and capacity pool, so its incident history is largely uncorrelated with Together AI's. That independence is what makes it useful as a fallback.
Check OpenAI status →Keep the fallback warm. A failover path that has not served a real request in three months is a hypothesis, not a mitigation.
Frequently Asked Questions
Where can I find Together AI's outage history?
Together AI's own record lives at status.together.ai, which keeps a rolling history of incidents Together AI has declared. That page is the starting point, not the answer. It shows incidents that crossed Together AI's internal declaration threshold and were confirmed by a human before publication, which means brief degradations, single-model failures and regional problems frequently never appear. If you need a complete record of when the Together AI API failed for you, the only reliable source is your own probe log.
How often does Together AI actually go down?
There is no honest single number, and any blog post quoting one is guessing. Published incident counts measure Together AI's publishing behaviour, not its availability. What is consistent across LLM inference providers is the shape of failure: a single model in the shared catalog degrading or being pulled while the platform reports healthy, because catalog breadth means per-model availability varies more than platform availability. Counted properly, those partial degradations outnumber declared outages by a wide margin, which is why teams that only read the status page believe their provider is more reliable than their own error logs say it is.
Why does Together AI's status page say everything is fine when my requests are failing?
Because you are measuring different things. A provider status page is an aggregate, publisher-controlled view; your requests are a specific model, from a specific region, on a specific key, at a specific concurrency. All of those can fail while the aggregate stays green. Before assuming a provider outage, split your errors by class: 401 is your key, 429 is your quota, 400 is your payload, and only sustained 5xx and timeouts across multiple keys and regions point at Together AI.
How do I prove Together AI downtime to a customer or for a credit claim?
With timestamped raw request records, not screenshots. A defensible downtime claim needs the request time in UTC, the endpoint and model, the HTTP status or timeout, the region you called from, and enough volume to show the failure was sustained rather than a single unlucky request. A screenshot of status.together.ai proves only that Together AI published something. Service credits, where a contract creates them, are almost always denied for lack of claimant-side evidence within the filing window.
Should I build my own Together AI outage history?
If the Together AI API is on your critical path, yes — and it is less work than it sounds. A synthetic request every minute against the models you actually call, with status code and latency retained, gives you a complete availability record inside a month. That record is what your SLA math, your postmortems and your customer comms all need, and unlike the provider's history, nobody can edit it after the fact.
Related Guides
- Is Together AI Down? Outage Checking Guide
- Together AI API SLA and Uptime: What Is Actually Committed
- Together AI API Incident Postmortem Guide
- Together AI API Failover: Keep Serving When Together AI Goes Down
- Together AI API Monitoring Guide
- Together AI API Status Alerts
- Uptime vs Availability: Why the Numbers Disagree
- LLM Inference API Uptime Comparison
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