together.ai vs Fireworks AI: how do they compare for batch inference/backfills (throughput limits, queueing, and total cost)?

Batch inference and backfills are where infrastructure choices show their real economics. When you’re pushing tens of billions of tokens through a model to rebuild embeddings, re-score a catalog, or refresh a knowledge index, small differences in throughput, queueing behavior, and pricing multiply quickly.

This walkthrough compares together.ai and Fireworks AI specifically for batch inference and backfill-style workloads, with a focus on three questions:

How much throughput can you realistically get?
What happens when you dump a very large job on the system?
What’s the total cost to clear a big backlog?

The Quick Overview

What It Is: A comparison of together.ai’s Batch Inference on the AI Native Cloud vs Fireworks AI’s batch / high-throughput inference for large, asynchronous workloads.
Who It Is For: Teams running large backfills (embeddings, rerank, content generation, evaluations) that care about tokens/sec, queueing guarantees, and cost per 1M tokens.
Core Problem Solved: Choosing the right platform so large jobs (10–30B+ tokens) finish predictably, stay within cost targets, and don’t disrupt real-time traffic.

How It Works

At a high level, both together.ai and Fireworks AI give you ways to run non-interactive jobs at scale. The differences are in:

Execution model: How you submit work (batch API vs DIY job queues vs dedicated endpoints).
Throughput & scheduling: How much parallelism you get and how jobs get queued.
Pricing & unit economics: How much you pay per 1M tokens once you’re saturating GPUs.

On together.ai, large backfills are a first-class concept via Batch Inference:

Job Definition & Submission:
You create a batch job targeting any serverless model or a private deployment (Dedicated Model Inference or Dedicated Container Inference), upload your inputs, and submit via API or CLI.
Parallel Execution & Scaling:
Together’s scheduler fans your job out across the AI Native Cloud, scaling aggressively to process up to 30 billion tokens per model. Under the hood, systems like Together Kernel Collection, ATLAS (speculative decoding), and CPD (prefill–decode disaggregation) are tuned for long, asynchronous runs.
Results & Cost Control:
Results are written to AI-native storage; you can stream progress or pull outputs when complete. Batch is priced for offline work—up to 50% less cost than real-time for most serverless models—so you can run large backfills without blowing your budget.

On Fireworks AI, you typically approximate batch via:

Their batch-style APIs (where available), or
Manually orchestrated parallel calls against high-throughput endpoints.

You can get strong performance, but you shoulder more of the scheduling, queueing, and cost-management logic yourself.

Features & Benefits Breakdown

Core Feature	What It Does	Primary Benefit
Batch Inference (together.ai)	Processes up to 30B tokens per model asynchronously via batch jobs.	Clear scale envelope and predictable completion.
Batch Pricing (together.ai)	Up to 50% cheaper than real-time serverless for most models.	Lower cost per backfill; better budget control.
AI Native Cloud Optimizations	Uses TKC, ATLAS, CPD, and custom kernels for throughput.	Higher tokens/sec and better GPU utilization.
Serverless + Dedicated Modes	Mix serverless batch with dedicated endpoints/clusters.	Match cost/latency to workload shape.
OpenAI-Compatible API	Drop-in replacement for existing completion/chat API calls.	Minimal code changes to move batch workloads.
Security & Ownership (together.ai)	Tenant-level isolation, encryption in transit/at rest, SOC 2 Type II.	Safe for sensitive, large-scale data backfills.

Throughput: How Fast Can You Clear a Backfill?

Throughput is a combination of:

Raw tokens/sec per GPU (kernel/runtime efficiency).
How many GPUs you can access in parallel (scaling limits).
How well the provider handles long-context / long-output runs.

together.ai

Together is engineered for high-throughput inference:

2.75x faster serverless inference for gpt-oss-20B vs the next fastest provider.
65% faster for Kimi-K2-0905 vs the next fastest provider.
10% faster for DeepSeek-V3.1 vs the next fastest provider.
13% faster throughput vs leading providers on DeepSeek R1 0528 and similar workloads.

These numbers reflect serverless inference, but the same kernel and runtime stack powers Batch Inference and private deployments:

Together Kernel Collection and FlashAttention-4 optimize attention, memory bandwidth, and KV cache usage.
ATLAS improves tokens/sec with speculative decoding.
CPD (prefill–decode disaggregation) keeps long-context runs from collapsing throughput when prompts are large.

For batch jobs, the key throughput property is:

Scale to 30 billion tokens per model with any serverless model or private deployment.

That’s a concrete ceiling you can plan against: if you know your average tokens/request, you can estimate job completion time by back-of-the-envelope math and usually get close to reality.

Fireworks AI

Fireworks AI is also positioned around high-throughput, especially for open models. They provide:

Fast serverless-style endpoints for leading OSS models.
Various optimizations (e.g., quantization and kernel tuning) to increase tokens/sec.

However, as of the available public information:

You don’t get an explicit “scale to X billion tokens per job” statement.
Batch is more of a pattern you build (parallelizing many calls) rather than a named Batch Inference product with a clear token envelope.

For small to medium backfills, Fireworks’ high-throughput endpoints can perform well. For very large jobs (10–30B+ tokens), the lack of explicit scale guarantees means you’ll often have to:

Over-provision concurrency yourself.
Implement your own queuing / backoff logic.
Monitor and mitigate rate limiting.

Practical takeaway on throughput

If your backfills are routine and large (multi‑billion tokens) and you want published scaling behavior, together.ai’s Batch Inference and 30B-token target give you clearer planning.
If your workloads are smaller or more bursty, both platforms can be fast; together.ai’s benchmark data (2.75x faster, 65% faster, 10% faster, 13% faster) suggests stronger performance at the top end, especially on modern OSS models.

Queueing & Scheduling: What Happens When You Drop a Huge Job?

together.ai: Batch as a First-Class Job

Batch Inference is a dedicated execution path:

Asynchronous jobs: You submit once; together.ai manages splitting, queuing, and distributing work.
Model-agnostic: Works with any serverless model or private deployment (Dedicated Model Inference / Dedicated Container Inference).
Traffic isolation: Batch runs on a separate lane from your latency-sensitive serverless traffic, maintaining SLOs for your live app.
Predictable limits: Up to 30 billion tokens per model per batch-style workload gives you a transparent envelope for how much you can queue.

Operationally, this means:

You don’t manage per-request concurrency manually.
You don’t need your own queuing system for basic backfills.
You can treat batch jobs like “set it and monitor it” instead of “manually babysit concurrency vs rate limits.”

If you need even tighter control, you can:

Spin up Dedicated Model Inference or Dedicated Container Inference endpoints.
Run your batch on that private capacity, with tenant-level isolation and guaranteed GPU reservation.
Scale further with GPU Clusters for one-off mega backfills or recurring offline jobs.

Fireworks AI: Queueing via Concurrency + Rate Limits

Fireworks AI typically expects you to:

Control job distribution using your own queues/workers.
Respect per-model or per-account rate limits.
Tune concurrency dynamically based on observed errors (429s, timeouts, etc.).

You can get good performance, but the queueing behavior lives in your code, not in a named batch product:

For small workloads, this is fine.
For very large backfills, you’ll involve additional infrastructure—message queues, worker pools, status tracking—to get the same level of predictability and isolation that together.ai’s Batch Inference gives you out of the box.

Queueing summary

together.ai: Queueing and scaling are encapsulated in Batch Inference. You get a clear token envelope and an operationally simple model: submit → monitor → download.
Fireworks AI: You build queueing and parallelization yourself. This can be powerful, but it’s more operational overhead, especially as token volume grows.

Total Cost: How Do Prices Behave at Backfill Scale?

For backfills, you care about:

Cost per 1M tokens (input + output where applicable).
Discounts or specialized pricing for asynchronous work.
Overhead cost from retries, queueing inefficiency, or fragmented infrastructure.

together.ai: Explicit Batch Discounting

Together’s AI Native Cloud is especially aggressive on batch economics:

Up to 50% cost savings versus real-time API for most serverless models when run via Batch Inference.
Same high-performance kernel/runtime stack as serverless, so you’re not trading away throughput for cost.
Ability to mix and match:
- Serverless Inference for on-demand tasks.
- Batch Inference for large backfills.
- Dedicated Model Inference / Dedicated Container Inference or GPU Clusters when you want fixed capacity and long-running jobs at even more predictable unit economics.

In practice, for a large job (say 20–30B tokens):

Moving it from serverless real-time to Batch can almost halve the bill.
For recurring workflows, you can push it further by binding it to dedicated capacity and running at high utilization.

Fireworks AI: Strong Performance, Less Explicit Batch Economics

Fireworks AI positions itself around strong price-performance for open models. For batch:

You still pay per token (and any model-specific multipliers) as with normal inference.
There may be volume or enterprise discounts, but there’s no widely advertised, named batch discount on public materials comparable to “up to 50% cheaper than real-time.”

The net effect:

For smaller jobs, Fireworks can be competitive.
For multi‑billion token backfills, the absence of a dedicated batch pricing tier means you’re often paying “real-time-style” unit prices for offline work unless you negotiate something custom.

Fireworks vs together.ai cost behavior

When you scale up to tens of billions of tokens, together.ai’s economics are shaped by:

Batch discounting (up to 50% cheaper).
Throughput gains (2.75x, 65%, 10%, 13% faster vs leading providers), which indirectly reduce cost when you run dedicated capacity or clusters because you get more tokens per GPU-hour.

With Fireworks, cost control is more about:

Driving utilization via concurrency.
Potentially negotiating volume discounts.
But you don’t get a “flip a switch and pay half” batch tier baked into the product surface.

Ideal Use Cases

Best for routine, large-scale backfills (10–30B tokens):
together.ai, because it:
- Exposes Batch Inference with an explicit 30B-token scale target.
- Offers up to 50% lower cost than real-time.
- Keeps batch and latency-sensitive traffic isolated by design.
Best for opportunistic or smaller batch jobs (<1–2B tokens):
Either platform, but together.ai’s performance benchmarks and batch pricing make it more attractive when:
- You rely on open models like DeepSeek, Kimi, or gpt-oss-20B, and
- You want room to grow into much larger offline workloads without re-architecting.

Limitations & Considerations

together.ai:
- Model coverage per region: Check that your required models are available in the region you need for compliance and latency.
- Batch job shape: While “up to 30B tokens per model” is generous, extremely large, heterogeneous jobs may still benefit from being broken into multiple batches for monitoring and failure isolation.
Fireworks AI:
- DIY queueing: You are responsible for building robust job orchestration—queues, backoff, retries, idempotency—for very large workloads.
- Lack of named batch tier: Without a dedicated batch product/pricing tier, you may pay closer to real-time prices for offline work unless negotiated otherwise.

Pricing & Plans (Conceptual Comparison)

Exact SKUs and numbers vary over time, but the decision pattern is consistent.

On together.ai, think in two main modes for batch/backfills:

Batch Inference (Serverless Batch):
Best for teams needing:
- Asynchronous processing of large token volumes.
- Up to 50% lower cost vs real-time.
- No need to manage GPUs or capacity planning.
Dedicated Capacity (Dedicated Model Inference / Dedicated Container Inference / GPU Clusters):
Best for teams needing:
- Guaranteed throughput and isolation for recurring backfills.
- Control over runtime and environment (e.g., custom containers).
- Ability to scale from 8 GPUs to 4,000+ for large, time-bounded jobs.

On Fireworks AI, you typically choose between:

High-throughput serverless-style endpoints:
Best for:
- Medium-size backfills.
- Teams comfortable handling concurrency and queueing themselves.
Enterprise / custom capacity arrangements:
Best for:
- Organizations ready to negotiate dedicated capacity and pricing, and to build their own batch orchestration on top.

Frequently Asked Questions

Is together.ai actually cheaper than Fireworks for large backfills?

Short Answer: For multi‑billion token jobs, together.ai is generally cheaper because Batch Inference is priced up to 50% below real-time for most serverless models, and its higher throughput lets you do more per GPU-hour.

Details:
Backfills are about both unit price and throughput. Together.ai’s Batch Inference explicitly discounts offline workloads vs real-time API usage, and the same research-to-production stack that powers its benchmarks (2.75x faster, 65% faster, 10% faster, 13% faster vs top providers on major models) means you process more tokens per unit time. Fireworks AI can perform well on a per-request basis, but without a clearly separated batch pricing tier, you typically pay something closer to real-time pricing for large offline runs unless you negotiate custom terms.

How do I avoid large batch jobs hurting my real-time latency?

Short Answer: On together.ai, use Batch Inference or Dedicated endpoints/Clusters for backfills; they run on separate capacity from your real-time serverless traffic.

Details:
Together.ai’s Batch Inference is designed to keep offline work away from latency-sensitive paths. You submit batch jobs against serverless models (in a batch lane) or your own private deployments. Under the hood, tenant-level isolation and a separate scheduler ensure that large asynchronous jobs don’t steal capacity from real-time calls. If you’re latency-obsessed, you can further isolate by running all backfills on Dedicated Model Inference or GPU Clusters, and keep serverless purely for user-facing workloads. With Fireworks AI, you typically implement this separation by managing concurrency and capacity yourself—e.g., reserving some workers for real-time traffic and others for batch—rather than relying on a dedicated batch product.

Summary

For batch inference and backfills, the core differences between together.ai and Fireworks AI are about how explicit the platform is about offline workloads and how the economics scale with volume:

together.ai gives you Batch Inference with:
- Scale up to 30 billion tokens per model.
- Up to 50% lower cost versus real-time serverless.
- Strong performance advantages on open models (2.75x faster, 65% faster, 10% faster, 13% faster vs top providers).
- Clean separation between batch and real-time paths, plus options for Dedicated endpoints and GPU Clusters.
Fireworks AI offers solid high-throughput endpoints but expects you to build your own batch orchestration and queueing, and doesn’t foreground batch-specific pricing in the same way.

If your backfills are small and occasional, either platform can work. If you’re planning recurrent, multi‑billion token offline jobs and want predictable throughput, clear queueing behavior, and strong unit economics, together.ai’s AI Native Cloud—especially Batch Inference plus Dedicated capacity—is built for exactly that.

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