Together AI API Best Practices 2026
Together AI hosts hundreds of open models behind one OpenAI-compatible API, which makes the production question less "which model" and more "serverless or dedicated". This guide covers that decision, rate limits, error handling, batch discounts, and the monitoring that catches a model swap before your users do.
Quick Reference
- →Status page:
status.together.ai - →The API is OpenAI-compatible — migration is usually a base-URL and key change, which also makes Together a cheap fallback target
- →Serverless is per-token; dedicated endpoints are per-GPU-minute. Above a sustained throughput threshold, dedicated is dramatically cheaper
- →429 = rate limit on serverless; dedicated endpoints do not rate-limit you, they queue you
- →The Batch API discounts roughly 50% for delay-tolerant jobs
- →Model availability on serverless changes as the catalogue rotates — pin ids and monitor for 404s
Together AI Model Selection Guide
Model choice is the largest single lever on both cost and latency. Start at the bottom of this table and move up only when an eval on your own data shows the smaller option failing.
Llama 3.3 70B classBest for
General-purpose production chat and RAG at a fraction of closed-model pricing
Avoid when
Frontier reasoning tasks where a closed model still measurably wins
Small instruct models (8B class)Best for
Classification, routing, extraction, and any high-QPS path
Avoid when
Long-form generation and multi-hop reasoning
Mixture-of-experts / DeepSeek classBest for
Reasoning and code where you want frontier-adjacent quality on open weights
Avoid when
Latency-sensitive paths — reasoning tokens inflate time to completion
Dedicated endpoint (any model)Best for
Sustained high-throughput workloads and predictable p99 latency
Avoid when
Spiky or low-volume traffic — you pay for idle GPU time
Serverless or dedicated: run the arithmetic once
This is the decision that determines your Together bill. Serverless charges per token and scales to zero, which is right for spiky or early-stage traffic. Dedicated endpoints charge per GPU-minute regardless of utilisation, which is right once your throughput is high and steady enough that the hourly rate divides down below the per-token equivalent.
Compute the crossover from your own numbers: take tokens per hour at peak, price them at the serverless rate, and compare against the hourly dedicated rate for the same model. Teams routinely leave money on the table in both directions — running dedicated GPUs at 15% utilisation, or paying serverless rates for a pipeline that saturates a GPU all day.
Rate limits and queueing behave differently per mode
On serverless, you get standard 429s when you exceed requests or tokens per minute, and the fix is backoff plus a client-side token bucket. On dedicated endpoints you generally are not rate-limited at all — instead, requests queue behind the capacity you have provisioned, and the symptom of overload is latency growth rather than an error code.
That difference matters for alerting. A serverless alert on 429 rate is meaningful; on dedicated it will never fire, and you need a p95 latency alert plus a queue-depth signal instead. Teams that migrate from serverless to dedicated and keep the old alerts effectively run unmonitored.
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Error handling on an OpenAI-compatible surface
Because the API mirrors OpenAI's shape, most SDK error handling ports directly — 429, 500, 503 all behave as expected and respond to exponential backoff with jitter. The one place compatibility leaks is model-specific behaviour: tool calling, JSON mode, and context handling vary between open models even though the request envelope is identical.
Treat a model swap as a code change, not a config change. A 422 or a malformed tool call after switching model ids is almost always a capability difference, not a platform fault, and it needs an eval run rather than a retry.
Cost control and monitoring
Beyond the serverless-versus-dedicated decision, the levers are the familiar ones: right-size the model, cap max_tokens, and push delay-tolerant work into the Batch API for roughly 50% off. Because Together's catalogue spans a wide price range for similar-quality open models, an eval sweep across three candidates often finds a materially cheaper model at equal quality — a test that pays for itself once and keeps paying.
For monitoring, watch model-id availability. Serverless catalogues rotate, and a retired id surfaces as a 404 on a path that worked yesterday. A daily synthetic request per id in production turns that into a ticket instead of an outage.
Production Checklist
Calculate your serverless/dedicated crossover
Peak tokens per hour at the serverless rate versus the hourly dedicated rate. Both over- and under-provisioning are common and expensive.
Alert on latency for dedicated endpoints
Dedicated endpoints queue rather than 429. A 429-rate alert inherited from serverless will never fire.
Pin model ids and synthetically test them daily
Serverless catalogues rotate. A retired id surfaces as a 404 in production.
Treat model swaps as code changes
Tool calling and JSON mode vary between open models even on an identical request envelope. Re-run evals.
Use the Batch API for offline work
Roughly 50% off for anything that tolerates a delayed completion window.
Run a three-model eval sweep
Together's catalogue spans a wide price range at similar quality. The cheaper equal-quality model is usually there.
Keep Together as a fallback target
OpenAI-compatible surface means a base-URL swap. Cheapest resilience you can buy.
Cap max_tokens per route
Open models are chattier by default than tuned closed models. Unbounded generation costs more here, not less.
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FAQ
Should I use Together AI serverless or dedicated endpoints?
Run the arithmetic on your own traffic. Serverless charges per token and scales to zero, which suits spiky or low-volume workloads. Dedicated endpoints charge per GPU-minute regardless of utilisation, which becomes cheaper once throughput is high and steady. Take your peak tokens per hour, price them at the serverless rate, and compare with the hourly dedicated rate for the same model. Running dedicated GPUs at low utilisation and paying serverless rates for a saturating pipeline are equally common mistakes.
What does Together AI error 429 mean?
A 429 means you exceeded requests or tokens per minute on a serverless endpoint. Back off exponentially with jitter (1s, 2s, 4s, 8s, ±20%) and add a client-side token bucket so you shape traffic before the server rejects it. Note that dedicated endpoints generally do not return 429 at all — they queue requests behind provisioned capacity, so overload shows up as latency growth rather than an error code.
Is the Together AI API compatible with the OpenAI SDK?
Yes. Together exposes an OpenAI-compatible API, so in most cases migration is a base-URL and API-key change with existing SDK error handling intact. The compatibility covers the request envelope, not model capabilities: tool calling, JSON mode, and context handling differ between open models. Treat any model id change as a code change requiring an eval run, not a config tweak.
How do I reduce Together AI costs?
Four levers. First, get the serverless-versus-dedicated decision right — it dominates everything else at scale. Second, run an eval sweep across three candidate models; the catalogue spans a wide price range at comparable quality and a cheaper equal-quality model is usually available. Third, move delay-tolerant work to the Batch API for roughly 50% off. Fourth, cap max_tokens per route, since open models tend to be chattier by default than tuned closed models.
Why did my Together AI model start returning 404?
The model id was almost certainly retired from the serverless catalogue. Together rotates available models as open-weight releases supersede one another, and a retired id fails as a 404 on a code path that worked the previous day. Pin explicit model ids, run a daily synthetic request against every id in production, and alert on 404 so the deprecation reaches you as a ticket rather than as a user-facing outage.