Baseten Guide

---
name: baseten-guide
description: Deploy ML models to production with Baseten — high-performance inference with autoscaling and custom runtimes.
category: ai-research
---

## Overview

Baseten is a model-deployment platform focused on production inference
performance: deploy open models (or your own) behind autoscaled, low-latency APIs
with strong performance engineering (custom inference engines, optimized
serving stacks). The pitch is "self-hosted performance without self-hosting" —
you get serious inference optimization managed for you, with dedicated capacity
options for strict SLOs.

Where serverless GPU platforms optimize for convenience and scale-to-zero,
Baseten optimizes for the production middle: sustained traffic, latency SLOs,
and cost-per-token at volume. If your workload has graduated from "run this
model sometimes" to "serve this model reliably at scale," Baseten is built for
that transition.

The evaluation lens: measure p99 latency and cost per million tokens at your
actual traffic shape. Baseten's value shows up in those numbers, not in feature
lists.

## When to use

- Production LLM serving with latency SLOs (p99 matters, not just averages).
- High-volume open-model inference where cost-per-token at scale is the metric.
- Deploying fine-tuned models behind production-grade endpoints.
- Multi-modal model serving (the platform covers more than text).
- Teams that need inference performance engineering without hiring for it.
- Dedicated capacity for isolation and predictable performance.

## Core concepts

- **Model deployments**: versioned deployments of models behind API endpoints.
  Deploy from popular open models or your own weights/fine-tunes. Versioning
  lets you roll forward and back safely.
- **Performance-optimized serving**: the platform applies inference
  optimizations (efficient attention kernels, batching, quantization options) so
  you don't have to. Understand what's applied to your deployment — it affects
  both latency and output behavior.
- **Autoscaling**: capacity scales with traffic within configured bounds.
  Configure min/max replicas deliberately: min for baseline latency (warm
  capacity), max for cost protection.
- **Dedicated deployments**: reserved GPUs for your workload — no noisy
  neighbors, predictable latency. The right choice when shared capacity variance
  violates SLOs.
- **Custom models and fine-tunes**: bring your own weights or train on the
  platform; serve the result on the same optimized stack. Keeps the
  customize→serve loop tight.
- **Development vs. production tiers**: lighter/cheaper options for dev and
  staging, performance tiers for production. Don't benchmark dev-tier latency and
  extrapolate to production.
- **Observability**: request logging, latency metrics, and GPU utilization.
  Wire these into your monitoring — inference issues show up here before users
  report them.
- **Cost model**: pay for the capacity you reserve plus usage. Model your
  expected cost at baseline and peak traffic before committing.

## Practical workflow

1. **Define SLOs first.** Target p50/p99 latency, throughput, and availability.
   Everything — deployment tier, replica counts, dedicated vs. shared — follows
   from these numbers.
2. **Deploy and benchmark.** Deploy the model, then load-test at realistic
   traffic shape (not just steady-state — include bursts). Measure p99 latency
   and tokens/sec; tune replica bounds and instance types.
3. **Validate output quality.** Optimized serving stacks (quantization, custom
   kernels) can subtly change outputs. Run your eval set against the deployed
   endpoint, not just the reference model.
4. **Configure autoscaling deliberately.** Set min replicas for warm baseline
   capacity (cold scaling hurts p99), max replicas as a cost circuit breaker.
   Test scale-up behavior under a traffic spike.
5. **Set up staging.** Mirror production config in staging for safe rollout
   testing. Canary new model versions through staging before production.
6. **Version and roll back safely.** Deploy new versions alongside old; shift
   traffic gradually; keep the previous version deployable for instant rollback.
7. **Monitor continuously.** Track latency percentiles, error rates, GPU
   utilization, and cost. Alert on SLO breaches and cost anomalies.

Checklist for a Baseten production deployment:
- SLOs defined; load test passed at realistic traffic shape.
- Output quality validated on the deployed (optimized) stack.
- Autoscaling min/max set and spike-tested.
- Staging mirrors production; canary process defined.
- Latency, error, utilization, and cost monitoring in place.

## Common pitfalls

- **Benchmarking the wrong tier.** Dev-tier numbers don't predict production
  latency. Benchmark the tier you'll actually run.
- **Skipping output validation.** Assuming the optimized stack is
  behavior-identical to the reference model. Quantization and custom kernels can
  shift outputs — verify.
- **Min replicas at zero for latency-sensitive APIs.** Scale-from-zero saves
  money and destroys p99. Warm baseline capacity is the cost of latency SLOs.
- **No max-replica cost guard.** Autoscaling without an upper bound during a
  traffic anomaly or retry storm. Set the max; alert approaching it.
- **Traffic shape mismatch.** Load-testing steady state when production has
  bursts. Test bursts, cold traffic patterns, and sustained peaks.
- **Big-bang version upgrades.** Swapping model versions without canarying.
  Inference behavior changes are user-facing — roll out gradually.
- **Ignoring GPU utilization.** Low utilization means you're overprovisioned
  (wasting money); saturated means no headroom (latency risk). Right-size from
  the metrics.
- **No rollback plan.** A bad deploy with no quick revert path. Keep the previous
  version warm and the rollback procedure rehearsed.