together.ai vs DeepInfra: do they offer dedicated endpoints, and how does performance isolation work?

Most teams evaluating together.ai vs DeepInfra aren’t just asking “who’s cheaper per million tokens.” They want to know: can I get true dedicated endpoints, and how hard is it to keep one noisy workload from degrading everything else?

This explainer walks through how both providers handle dedicated endpoints and performance isolation, and when you’d reach for each deployment mode on the together.ai AI Native Cloud.

Quick Answer: Both together.ai and DeepInfra offer ways to get “dedicated” capacity, but together.ai formalizes this as Dedicated Model Inference and Dedicated Container Inference with tenant-level isolation, reserved GPUs, and latency SLOs. DeepInfra focuses more on shared serverless-style endpoints and reserved capacity per model; together.ai goes deeper on research-driven runtime isolation (ATLAS, CPD, TKC) and dedicated options that separate noisy neighbors at both the hardware and runtime layers.

The Quick Overview

What It Is: A comparison of how together.ai and DeepInfra provide dedicated endpoints for LLM and multimodal workloads, and how each platform enforces performance isolation under load.
Who It Is For: Engineering leaders, infra owners, and applied ML teams building latency-sensitive AI products who need to choose between shared serverless, reserved capacity, and fully dedicated endpoints.
Core Problem Solved: Avoiding noisy-neighbor issues, tail-latency spikes, and unpredictable throughput when traffic and models scale — without taking on GPU cluster management yourself.

How It Works

At a high level, both platforms follow a similar pattern:

Shared / Serverless Inference for variable or unpredictable traffic:
- You hit a public or account-scoped endpoint.
- Your requests are multiplexed with other tenants’ workloads.
- Autoscaling and queuing aim to keep latency “good enough,” but you share GPUs and runtime state.
Reserved / Dedicated Capacity for stable, high-throughput workloads:
- You reserve capacity for a specific model or set of models.
- The provider pins that capacity to your tenant or to a specific endpoint.
- You get more predictable latency and throughput, often at better unit economics.
Custom Runtime / Container for non-standard engines:
- Instead of the provider’s standard inference engine, you bring your own containerized runtime.
- The platform handles GPU orchestration, scaling, networking, and observability.
- This is essential for generative media, custom decoding stacks, or research-heavy pipelines.

On together.ai, these map directly to:

Serverless Inference (shared, OpenAI-compatible API)
Dedicated Model Inference (reserved, isolated endpoint running Together’s inference engine)
Dedicated Container Inference (your engine, your model, on fully-managed infrastructure)

DeepInfra offers a similar spectrum, but with less explicit separation between “shared” and “fully dedicated” runtimes and fewer documented details about kernel-level and runtime isolation strategies.

together.ai Dedicated Endpoints vs DeepInfra at a Glance

together.ai

together.ai is an AI Native Cloud optimized for running open-source and partner models with:

Serverless Inference:
- Best for variable or bursty traffic.
- OpenAI-compatible API for text, image, code, and more.
- Backed by systems like FlashAttention (via Together Kernel Collection), ATLAS (speculative decoding), and CPD (prefill–decode disaggregation) to deliver:
  - Up to 2.75x faster inference
  - 2x faster serverless inference for top open-source models
Batch Inference:
- For massive offline workloads up to 30 billion tokens, at up to 50% less cost, with asynchronous completion.
Dedicated Model Inference:
- An inference endpoint backed by reserved, isolated compute resources and the Together AI inference engine.
- Best for:
  - Predictable or steady traffic
  - Latency-sensitive applications
  - High-throughput production workloads
Dedicated Container Inference:
- Run your own engine and model on fully-managed, scalable infrastructure.
- Best for:
  - Generative media models
  - Non-standard runtimes (custom CUDA, Triton, Rust, etc.)
  - Custom inference or RAG pipelines
GPU Clusters + Together Sandbox:
- Self-serve GPU clusters that scale from 8 GPUs to 4,000+.
- Sandbox for fast iteration with 2.7s cold-starts (P95) and 500ms snapshot resumes (P95).

Isolation is enforced at multiple levels: tenant-level endpoint isolation, reserved GPUs, runtime-level scheduling, and storage boundaries, all under SOC 2 Type II with encryption in transit/at rest. Your data and models remain fully under your ownership.

DeepInfra

DeepInfra provides:

Hosted open-source models via an HTTP API.
Serverless-style shared endpoints with autoscaling.
Options for “reserved capacity” or dedicated GPUs for select workloads (details depend on their current product tier and roadmap).

DeepInfra does offer ways to get closer to dedicated capacity (e.g., provisioning dedicated GPUs per model or per tenant), but documentation tends to focus on:

Per-model performance and pricing.
Simple deployment story for open models.
Less emphasis on detailed kernel/runtime-level innovations or containerized custom runtimes.

Performance isolation is primarily shaped by:

GPU-level reservation (when enabled).
Queueing and autoscaling policies.
Model-level separation.

In practice, both platforms can isolate noisy neighbors by giving you reserved capacity, but together.ai goes further in exposing explicit Dedicated Model and Dedicated Container options, paired with research-backed runtime systems designed for long-context and high-throughput scenarios.

How Dedicated Model Inference Works on together.ai

Dedicated Model Inference on together.ai is designed to look like a familiar OpenAI-style endpoint but behave like your own private, production-grade model gateway.

1. Endpoint Creation

You choose the model (e.g., Llama 3.x, Mixtral, Qwen, or a fine-tuned variant).
together.ai provisions reserved GPU instances and wires them to a dedicated endpoint URL.
The endpoint sits behind the Together inference engine, which includes:
- Together Kernel Collection (TKC): custom CUDA kernels (including FlashAttention-4) optimized for high tokens/sec.
- ATLAS (AdapTive-LeArning Speculator System): speculative decoding to reduce time-to-first-token and total latency.
- CPD (prefill–decode disaggregation): disaggregated prefill and decode stages for long context, so large prompts don’t starve concurrent requests.

2. Traffic Routing & Isolation

Your traffic is never co-scheduled with other tenants’ traffic on that dedicated endpoint.
Isolation includes:
- Reserved, isolated compute: your GPUs are pinned to that endpoint.
- Tenant-level isolation: separate control plane, logs, and metrics.
- Per-endpoint QoS: request concurrency and rate limits are tuned for your SLOs, not the global public pool.

This prevents the classic noisy-neighbor issue where another tenant’s large context or long-running request blows up your tail latency.

3. Observability & Scaling

You get per-endpoint metrics: latency (including P95/P99), tokens/sec, and error rates.
You can adjust:
- Number/type of GPUs.
- Model variants or quantization settings (e.g., 4-bit vs 8-bit for throughput vs quality).
together.ai’s underlying research systems (ATLAS, CPD) continue to optimize your workload over time, effectively improving your SLOs without an app rewrite.

When to Use Dedicated Model Inference Instead of Serverless

Choose together.ai Dedicated Model Inference when:

You have predictable or steady traffic (e.g., 24/7 customer chat, B2B workflow automation).
You are latency-sensitive and need consistent P95/P99 times.
You need high-throughput generation at predictable cost (serverless is often cheaper for spiky traffic; dedicated shines when utilization is high).

Compared to DeepInfra’s reserved capacity, together.ai’s Dedicated Model Inference is more explicit about isolation guarantees (reserved, isolated compute + tenant-level separation) and is tightly integrated with the same kernel/runtime stack that powers their top-performing serverless endpoints.

How Dedicated Container Inference Works on together.ai

If you’ve built your own inference stack — e.g., custom scheduler, diffusion engine, or multi-model router — you often don’t want to replace that with a provider’s runtime. You just want someone else to manage GPUs and serving infrastructure.

1. Bring Your Own Runtime

Package your inference server as a container image.
together.ai runs it as Dedicated Container Inference:
- Fully-managed GPU infrastructure.
- Integrated networking, scaling, and health checks.
- Kubernetes/Slurm-like control without touching those systems directly.

2. Best For

Generative media models (image, video, audio) that rely on specialized runtimes.
Non-standard runtimes (Triton, custom CUDA, Rust, Go) that aren’t supported by off-the-shelf LLM servers.
Custom pipelines (e.g., retrieval-augmented generation with bespoke ranking, graph-based tools, or on-the-fly adapters).

DeepInfra can host custom models, but the explicit “Dedicated Container Inference” pattern — your runtime, your model, on managed GPUs — is a strength of together.ai and matches how many advanced teams want to operate.

Features & Benefits Breakdown

Core Feature	What It Does	Primary Benefit
Dedicated Model Inference	Provides an endpoint backed by reserved, isolated compute running the Together inference engine	Consistent low-latency, high-throughput performance without noisy neighbors
Dedicated Container Inference	Runs your own inference engine and model on fully-managed GPU infrastructure	Custom runtime flexibility with together.ai handling scaling, orchestration, and reliability
Research-Backed Runtime (ATLAS, CPD, TKC)	Uses kernel and runtime innovations for speculative decoding, efficient attention, and disaggregated prefill/decode	Up to 2.75x faster inference, 2x faster serverless for top OS models, and efficient long-context serving
Batch Inference up to 30B tokens	Processes massive offline workloads asynchronously at up to 50% less cost	Lower unit cost for large jobs without impacting real-time SLOs
OpenAI-Compatible API	Allows drop-in integration using familiar chat/completions semantics	Minimal code changes to switch or multi-home between providers

Ideal Use Cases

Best for steady SaaS workloads:
Use together.ai Dedicated Model Inference when you run an AI feature that sees stable, 24/7 traffic and where a 100–200 ms swing in P95 latency is noticeable to users. Reserved, isolated compute plus ATLAS/CPD give you consistent performance.
Best for heavy internal pipelines:
Use together.ai Batch Inference when you need to process millions of records — e.g., nightly dataset classification, offline summarization, or synthetic data generation — up to 30 billion tokens per job, at up to 50% less cost than real-time.
Best for custom media or non-standard engines:
Use Dedicated Container Inference if your workload is a diffusion model, custom ASR/TTS pipeline, or a bespoke router built around an internal library. together.ai manages GPUs; you keep your runtime.
Best for spiky traffic or early experiments:
Start with Serverless Inference (on both together.ai and DeepInfra). As your traffic stabilizes and latency SLOs tighten, you can migrate hot paths to Dedicated Model Inference on together.ai.

Limitations & Considerations

DeepInfra details may change quickly:
DeepInfra’s exact dedicated/reserved capacity features and API semantics evolve over time. Always check their latest docs for:
- Whether GPUs are fully tenant-dedicated vs shared.
- Any minimum commitment or lock-in for dedicated capacity.
- How they report latency SLOs and noisy-neighbor isolation.
Dedicated endpoints require volume to pay off:
On together.ai, Dedicated Model Inference is optimized for predictable, high-utilization workloads. If your traffic is low or very spiky, you’ll likely get better economics from Serverless Inference until usage ramps.
Container-based deployments require ops discipline:
With Dedicated Container Inference, you own your runtime’s correctness and efficiency. together.ai handles infrastructure, but:
- Poor batching or KV-cache handling inside your container can hurt latency.
- You still need to profile and tune your inference stack (batch size, quantization, etc.).

Pricing & Plans

together.ai follows a deployment-mode-centric pricing model rather than a one-size-fits-all:

Serverless Inference:
- Pay per token or per output unit.
- Best for variable or unpredictable traffic and teams testing multiple models.
- “No infrastructure to manage, no long-term commitments.”
Batch Inference:
- Optimized price for large offline workloads up to 30B tokens, with up to 50% less cost vs real-time.
- Best when throughput matters more than per-request latency.
Dedicated Model Inference:
- Reserved, isolated compute with tailored pricing for predictable, high-throughput workloads.
- Best for teams needing latency SLOs with steady traffic patterns.
Dedicated Container Inference:
- Custom pricing depending on GPU type, capacity, and expected throughput.
- Best for teams bringing their own engine and needing fully-managed GPU infrastructure.

DeepInfra’s pricing typically centers on:

Per-token or per-second charges on shared serverless endpoints.
Additional pricing for reserved GPUs or model-specific dedicated capacity.
Discounts at higher volume or with reserved usage.

For precise numbers, you should compare current public pricing pages or talk to sales for both providers, as GPU and model costs shift rapidly.

Frequently Asked Questions

Do both together.ai and DeepInfra offer fully dedicated endpoints?

Short Answer: Yes, both offer dedicated capacity, but together.ai exposes it as Dedicated Model Inference and Dedicated Container Inference with explicit reserved, isolated compute and tenant-level isolation.

Details:
DeepInfra supports reserved capacity or dedicated GPUs at a model level, but the exact semantics (how isolated, how scheduled, how noisy neighbors are handled) are less explicitly documented. together.ai defines:

Dedicated Model Inference: An endpoint backed by reserved, isolated compute resources and the Together AI inference engine, best for predictable, latency-sensitive, and high-throughput workloads.
Dedicated Container Inference: Your own engine and model on fully-managed infrastructure, ideal for generative media and custom pipelines.

In both cases, your workloads are isolated from other tenants at the GPU and endpoint level, with together.ai focusing on kernel/runtime-level optimizations (ATLAS, CPD, TKC) that turn that isolation into measurable SLO gains.

How does performance isolation actually work on together.ai compared to DeepInfra?

Short Answer: together.ai combines reserved GPUs, tenant-level isolation, and research-driven runtime systems (ATLAS, CPD, TKC) to keep latency stable under load. DeepInfra isolates primarily via GPU reservation and queueing policies; details of kernel/runtime strategies are less visible.

Details:
On together.ai:

Hardware isolation: Dedicated endpoints run on reserved GPU instances that are not shared with other tenants.
Runtime isolation: The Together inference engine uses:
- Together Kernel Collection (including FlashAttention-4) for high tokens/sec.
- ATLAS for speculative decoding, improving time-to-first-token.
- CPD to separate prefill and decode stages so long context prompts don’t block shorter requests.
Control plane isolation: Tenant-level separation in routing, rate limiting, logging, and API keys.
Compliance & security: SOC 2 Type II, encryption in transit/at rest, and guarantees that your data and models remain fully under your ownership.

DeepInfra can approximate some of this via dedicated GPUs and per-model queues. However, the depth of documented runtime innovations and isolation strategies is not on par with together.ai’s explicit research-to-production stack, especially for long-context and high-throughput workloads.

Summary

If your main question is “Do together.ai and DeepInfra both offer dedicated endpoints?” the answer is yes — but they don’t treat dedicated capacity the same way.

together.ai formalizes dedicated endpoints as Dedicated Model Inference and Dedicated Container Inference, built on a research-backed inference engine (ATLAS, CPD, TKC, FlashAttention) and reserved, isolated compute. It’s designed for teams that treat latency, tokens/sec, and cost per 1M tokens as core product levers, not just infra metrics.
DeepInfra offers reserved or dedicated capacity as an extension of its hosted open-source model service, with a simpler surface but less emphasis on multi-layer isolation and custom runtimes.

For most production workloads with real SLOs, together.ai’s AI Native Cloud gives you clearer deployment choices (Serverless vs Batch vs Dedicated Model vs Dedicated Container), better documented performance isolation, and more control over how research-grade kernels translate into your latency and throughput.

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