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

Most teams evaluating GPU providers quickly converge on the same two questions: can I get a dedicated endpoint for my steady workloads, and how strong is the performance isolation when I do? This is exactly where together.ai and DeepInfra start to diverge in practice—especially once you care about tail latency, noisy neighbors, and cost per 1M tokens at scale.

Quick Answer: Both together.ai and DeepInfra offer ways to run models without sharing a generic serverless pool, but together.ai exposes a clearer, production-oriented split between Dedicated Model Inference (their dedicated endpoints) and Dedicated Container Inference (bring your own engine), with explicit tenant-level isolation and SOC 2 Type II controls. DeepInfra focuses more on shared, per‑request GPU billing with some reserved-capacity options, but its performance isolation model is less tightly coupled to a research-backed serving stack (ATLAS, CPD, TKC) and less explicitly documented around multi-tenant separation.

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The Quick Overview

• What It Is: A comparison of how together.ai and DeepInfra handle dedicated endpoints for generative workloads and what performance isolation you can expect in real deployments.
• Who It Is For: Engineering leads, infra owners, and ML platform teams deciding where to run production LLMs and multimodal models with strict SLOs (p50/p95 latency, throughput, and cost constraints).
• Core Problem Solved: Understanding whether each provider can give you predictable, isolated performance for steady traffic—without you managing GPUs and serving runtimes yourself.

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How “Dedicated Endpoints” Work On Each Platform

Both providers start from a similar premise: shared “serverless” pools for bursty traffic, and some notion of reserved capacity for steady workloads. The differences come down to:

• How explicit the dedicated endpoint concept is.
• How performance isolation is enforced (cluster-level vs tenant-level).
• How much of the serving stack is controlled by the provider vs by you.

together.ai

together.ai’s AI Native Cloud splits inference into clear modes:

1. Serverless Inference (shared pool)

   • On-demand, OpenAI‑compatible API.
   • Best for variable or unpredictable traffic, prototyping, and early stage workloads.
   • Backed by Together Kernel Collection, ATLAS speculative decoding, and CPD for long context, giving up to 2.75x faster inference and 2x faster serverless on top open-source models.

2. Dedicated Model Inference (dedicated endpoints)

   • Definition: 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
   • You hit a stable URL / model endpoint; underneath, GPUs are pinned to your workload, and ATLAS/CPD/TKC optimizations are applied globally.
   • Supports tenant-level isolation, encryption in transit/at rest, SOC 2 Type II.

3. Dedicated Container Inference (BYO engine)

   • You bring your own runtime and model (custom CUDA, custom vLLM/tgi build, non-standard runtimes).
   • together.ai manages the GPU cluster, autoscaling, and isolation, but you own the inference stack.
   • Best for generative media models, custom pipelines, or anything that doesn’t fit a standard LLM/embedding server.

4. Batch Inference

   • Asynchronous, high-throughput processing of up to 30 billion tokens per batch at up to 50% less cost.
   • Best for classification, offline summarization, and synthetic data generation where latency is less critical than cost.

Performance isolation mechanism at together.ai:

• Dedicated Model Inference:

  • Reserved GPUs and memory per customer or per endpoint.
  • Isolation via tenant-level resource partitioning—no noisy neighbors contending for the same GPU memory or KV cache.
  • Time-to-first-token and tokens/sec tuned via:
    • ATLAS (AdapTive-LeArning Speculator System) for speculative decoding.
    • CPD (cache-aware prefill–decode disaggregation) for long-context serving so large prompts don’t starve decodes.
    • Together Kernel Collection (TKC) with FlashAttention-4 and custom CUDA kernels.
  • Result: predictable P95 latency and stable throughput, even under sustained heavy load.

• Dedicated Container Inference:

  • Same isolation properties at the infrastructure level, but you control the inference engine.
  • together.ai’s scheduler, networking, and cluster management provide tenant-level isolation and 99.9% uptime, but model-level behavior (batch sizes, quantization, KV cache policies) is your domain.

DeepInfra

DeepInfra’s public positioning is mostly around:

• Running open-source models via a pay‑per‑token or pay‑per‑second interface.
• Exposing an API that looks similar to OpenAI’s for LLMs and embeddings.
• Offering some notion of dedicated GPU instances / reserved capacity in addition to shared serverless.

From what’s publicly documented as of this writing:

1. Shared Serverless Inference

   • Multi-tenant GPU pools.
   • Limited control over scheduling; you pay per token or request.
   • Best for experimentation, low-volume workloads, and non-critical traffic.

2. Reserved / Dedicated GPU Options

   • DeepInfra offers ways to provision your own GPU-backed instances for models; in practice this looks like:
     • Dedicated instances where your model occupies specific GPUs.
     • Fewer noisy neighbors compared to shared serverless.
   • Details on tenant-level isolation (e.g., strict separation of memory, KV cache, and scheduling queues) are less explicit than together.ai’s “reserved, isolated compute resources” language and SOC 2 Type II program.

3. Bring-Your-Own Model / Runtime

   • DeepInfra also supports hosting your own models.
   • Platform control over performance tuning (like speculative decoding or long-context pipeline) is less clearly tied to named research systems like ATLAS/CPD/TKC.

Performance isolation mechanism at DeepInfra (inferred from public info):

• Shared serverless pools are multi-tenant; performance can depend on global demand and internal scheduling, with higher risk of tail latency and noisy neighbors.
• Dedicated or reserved GPUs will reduce contention, but:
  • Public docs are less precise on how isolation is enforced in the scheduler.
  • There’s less explicit framing around p95/p99 latency guarantees or specific throughput benchmarks.
  • Security posture is documented, but you see fewer explicit claims like “tenant-level isolation,” “SOC 2 Type II,” and “your data and models remain fully under your ownership” integrated into the dedicated endpoint story.

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Side-by-Side: Dedicated Endpoints & Isolation

From a practitioner’s perspective, the main questions are:

1. Can I get a stable, dedicated endpoint for my production workloads?
2. Will my latency and throughput be isolated from other tenants?
3. Do I retain data and model ownership?

Dedicated Endpoint Availability

Capability	together.ai	DeepInfra (based on public info)
Shared serverless pool	Yes – Serverless Inference	Yes – Shared / on-demand inference
Named “Dedicated Endpoint” product	Yes – Dedicated Model Inference	Similar in spirit via reserved GPUs; less formalized naming
BYO engine, managed infra	Yes – Dedicated Container Inference	Yes – BYO model/runtime hosting
Batch / async workloads	Yes – Batch Inference (up to 30B tokens)	Limited/less emphasized; focus is online inference
OpenAI-compatible API	Yes – across serverless & dedicated	Yes – for main LLM API

Performance Isolation & SLOs

Isolation / SLO Aspect	together.ai	DeepInfra (based on public info)
Tenant-level GPU isolation	Explicit in Dedicated Model Inference	Implied with reserved instances; details less explicit
Isolated KV cache and memory	Yes, per-tenant / per-endpoint on dedicated inference	Not clearly documented
Model-level isolation option	Yes – Dedicated Container Inference with your runtime	Supported but less tightly integrated into a named product line
Long-context performance	Optimized via CPD (prefill–decode disaggregation)	Generic runtime optimizations; no named CPD equivalent
Speculative decoding	ATLAS runtime-learning speculator	Undocumented or generic (if present)
Kernel-level speedups	Together Kernel Collection, FlashAttention-4	Uses optimized kernels, but no public TKC equivalent
Published customer benchmarks	Yes – e.g., Salesforce AI Research: 2x lower latency, ~33% lower cost	Limited public benchmarks with concrete SLOs
Uptime commitment	99.9% uptime	Standard cloud SLA language; specifics may vary

Security, Ownership, and Compliance

Aspect	together.ai	DeepInfra (based on public info)
Data ownership guarantee	“Your data and models remain fully under your ownership.”	Data control is present but less emphasized
Compliance	AICPA SOC 2 Type II	Compliance present; details differ by provider
Encryption	Encryption in transit/at rest as baseline	Common TLS and storage encryption
Tenant-level isolation language	Explicit in dedicated products	Not as central in branding

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How It Works: A Typical Migration To Dedicated Endpoints

Assume you’re running a high-traffic AI product today on a patchwork of hosted providers, all hitting shared pools. Here’s how this typically plays out on together.ai vs DeepInfra.

together.ai: Serverless → Dedicated Model → Container

1. Phase 1 – Serverless Inference for prototypes

   • Start with the OpenAI-compatible serverless API.
   • Swap your openai base URL to together’s endpoint; in many cases no code changes beyond base URL and API key.
   • Validate:
     • Quality on your prompts.
     • Latency profile (see ATLAS/CPD in action).
     • Unit economics at your current traffic level.

2. Phase 2 – Lift steady workloads to Dedicated Model Inference

   • Identify traffic that is:
     • Predictable (daily/weekly patterns).
     • Latency-sensitive (user-facing chat, agents, voice).
   • Move those models to Dedicated Model Inference:
     • together.ai reserves GPU capacity for your endpoint.
     • You get isolation from global traffic, stable P95s, and often lower cost at volume.
     • Same OpenAI-compatible interface, so migrating is usually a matter of updating model/endpoint in config.
   • You now have:
     • Serverless for bursty workloads.
     • Dedicated Model for steady traffic with reserved, isolated compute resources.

3. Phase 3 – Custom pipelines on Dedicated Container Inference

   • As your requirements get more complex:
     • Custom quantization (e.g., 4-bit or 8-bit for better price-performance).
     • Custom runtime (vLLM fork, custom KV cache, multimodal pipelines).
   • Move these to Dedicated Container Inference:
     • together.ai manages GPU clusters and isolation.
     • You own the container image and inference logic.
     • Still benefit from GPU Clusters, autoscaling, and the broader AI Native Cloud stack.

Throughout this progression, you can also move large offline workloads (training data labeling, large document summarization) to Batch Inference, getting up to 50% lower cost for up to 30B tokens per job.

DeepInfra: Shared Pool → Reserved GPUs

On DeepInfra, the typical evolution is:

1. Start with shared on-demand API for experimentation.
2. For steady workloads, request or configure dedicated / reserved GPU instances.
3. Run your own model or a hosted open-source model on those GPUs.

You do get less noisy neighbors versus the shared pool, but:

• The performance isolation story is more tied to “you own these GPUs” than to a named, research-backed serving stack like ATLAS/CPD/TKC.
• The migration path from shared → dedicated is less explicitly framed as a first-class product (no “Dedicated Model Inference” vs “Dedicated Container Inference” distinction).

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Features & Benefits Breakdown

together.ai’s Dedicated Inference Lineup

Core Feature	What It Does	Primary Benefit
Dedicated Model Inference	Reserves GPUs for a single tenant/model on together.ai’s inference engine	Predictable latency and throughput for production traffic
Dedicated Container Inference	Runs your containerized engine on managed, isolated GPU infrastructure	Full runtime control with no infra management
Batch Inference (up to 30B tokens)	Processes large offline workloads asynchronously at up to 50% less cost	Best unit economics for non-interactive jobs

DeepInfra’s Relevant Offerings (High-Level)

Core Feature	What It Does	Primary Benefit
Shared On-Demand Inference	Multi-tenant LLM hosting with pay-per-request billing	Fast to start, no capacity management
Reserved/Dedicated GPUs	Allocates GPU capacity primarily for your use	Reduced contention, more stable performance
Hosted Custom Models	Run your own models on DeepInfra’s infra	Offloads GPU provisioning and base infra work

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Ideal Use Cases

together.ai

• Best for latency-critical production endpoints:
  Because Dedicated Model Inference gives you reserved, isolated compute with ATLAS/CPD/TKC-backed performance, making p95 latency a feature, not a variable.

• Best for teams with custom runtimes or media models:
  Because Dedicated Container Inference lets you ship your own engine (e.g., vLLM fork, diffusion pipeline) while together.ai handles GPU clusters, autoscaling, and tenant-level isolation.

• Best for large offline workloads:
  Because Batch Inference scales up to 30 billion tokens per job at up to 50% less cost, and you can keep the interactive paths on dedicated real-time inference.

DeepInfra

• Best for simple open-source model hosting with minimal setup:
  Because the shared API and optional reserved GPUs are straightforward for teams that don’t need granular control over isolation mechanisms.

• Best for small to mid-size workloads where SLOs are less strict:
  Because you can stay on the shared pool longer before committing to reserved capacity, at the cost of more variable tail latency.

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Limitations & Considerations

• together.ai – More knobs, more decisions:
  You have to choose between Serverless, Dedicated Model, Dedicated Container, Batch, and GPU Clusters. The upside is fine-grained economics and SLO tuning; the tradeoff is slightly higher planning overhead (which serverless for what, which dedicated for what).

• DeepInfra – Less explicit isolation & research-to-production story:
  You do get reserved GPUs, but there is less public detail on:

  • How isolation interacts with scheduling and KV cache.
  • What research systems back the runtime (no explicit ATLAS/CPD/TKC equivalents).
  • How batch vs real-time workload separation is handled for cost and SLOs.

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Pricing & Plans (Conceptual, Not Quoted)

Both providers use a mix of per‑token (or per‑second) pricing for serverless and capacity-based pricing for dedicated / reserved GPUs.

On together.ai, you can think of it as:

• Serverless Inference:
  Best for teams testing models, running low-volume workloads, or handling bursty traffic where you don’t want to reserve capacity.

• Dedicated Model / Container Inference:
  Best for teams with:

  • Predictable QPS and token volumes.
  • Strict SLOs on latency.
  • A desire to lower cost per 1M tokens by running on reserved, isolated capacity.

On DeepInfra, the rough parallel is:

• Shared API:
  Best for experimentation and variable workloads.
• Reserved GPU Instances:
  Best for higher, steadier traffic volumes where cost predictability and reduced contention matter more.

For exact numbers, you’ll need to check each provider’s current pricing pages and, for larger deployments, talk to sales about committed-use or custom discounts.

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Frequently Asked Questions

Do both together.ai and DeepInfra offer dedicated endpoints?

Short Answer: Yes, both offer a form of dedicated capacity, but together.ai formalizes this as Dedicated Model Inference and Dedicated Container Inference with explicit tenant-level isolation and research-backed serving optimizations.

Details:
On together.ai, Dedicated Model Inference is explicitly defined as “an inference endpoint backed by reserved, isolated compute resources and the Together AI inference engine,” making the dedicated endpoint concept a first-class product. DeepInfra offers reserved / dedicated GPU instances for models, which function similarly at the capacity level, but the concept is not framed as a distinct, research-backed serving product line in the same way, and isolation/SLO guarantees are less exhaustively documented in public materials.

How does performance isolation differ between together.ai and DeepInfra?

Short Answer: together.ai emphasizes tenant-level isolation and long-context/throughput optimizations via ATLAS, CPD, and TKC, whereas DeepInfra focuses on the practical ability to reserve GPUs, with fewer public details on the underlying scheduling and isolation mechanisms.

Details:
With together.ai, you can rely on:

• Dedicated Model Inference: reserved GPUs and memory per tenant, separate scheduling queues, and integrated ATLAS/CPD/TKC optimizations for low latency and high throughput.
• Dedicated Container Inference: your runtime, but still on isolated GPU capacity with tenant-level isolation and SOC 2 Type II assurances.
• A clear separation between interactive (serverless/dedicated) and offline (batch) workloads, optimizing both SLOs and cost.

On DeepInfra, performance isolation primarily means reserving GPUs for your use rather than sharing them in the public pool. That does mitigate noisy neighbors, but there is less public detail about how KV cache, context length, speculative decoding, and scheduling are designed to guarantee tail latency and fairness across tenants. For stringent SLOs, you’ll likely need to run your own benchmarks on both platforms.

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Summary

If you just need to host an open-source model quickly, both together.ai and DeepInfra can work. The real difference emerges when you care about:

• Dedicated endpoints with explicit, reserved, isolated compute resources.
• Performance isolation that extends beyond “I have my own GPU” into how prefill, decode, KV cache, and speculative decoding are architected.
• A clear progression from serverless → dedicated model → dedicated container → batch, all under one OpenAI-compatible API and with SOC 2 Type II, tenant-level isolation, and strong ownership guarantees.

together.ai is built as an AI Native Cloud with research-to-production systems like ATLAS, CPD, and Together Kernel Collection explicitly wired into the dedicated inference stack. DeepInfra is a solid option for straightforward open-source hosting and reserved GPUs, but with a less formalized, less research-documented approach to performance isolation.

If your product’s differentiation relies on latency as a feature and unit economics as a moat, the more structured dedicated endpoint model and isolation story at together.ai tends to give you clearer, more predictable knobs to hit your SLOs.

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