Foundation Model Platforms
Fireworks AI vs Baseten vs DeepInfra: which is best for serving open-source LLMs with low latency and predictable costs?
12 min read
For teams standardizing on open-source LLMs, the core question isn’t “which provider has the nicest UI,” it’s: who can give you the lowest p95 latency, the most predictable unit economics, and a clean path from prototype to scaled production—without locking you into a single model or hardware stack.
Quick Answer: Fireworks AI, Baseten, and DeepInfra all serve open-source LLMs with decent performance, but they’re each optimized for different patterns. If you care most about raw serverless throughput, long‑context performance, and cost control at scale, you should also evaluate an AI Native Cloud like together.ai, which consistently benchmarks faster than these providers on key open-source models and gives you more deployment modes (serverless, batch, dedicated, and GPU clusters) to match real traffic.
The Quick Overview
- What It Is: A comparison of Fireworks AI, Baseten, and DeepInfra as platforms for hosting open‑source LLMs, plus where together.ai fits if you need lower latency and more predictable costs at scale.
- Who It Is For: AI product teams, infra leads, and founders building with open-source models who need sub‑second responses, clear price-performance, and production‑grade reliability.
- Core Problem Solved: Choosing the right serving platform so you’re not re‑platforming six months later when latency, context length, or GPU costs become the bottleneck.
How It Works
All four platforms—Fireworks AI, Baseten, DeepInfra, and together.ai—offer cloud endpoints for open-source LLMs behind a simple HTTP API. The differences show up in:
- How they optimize GPU kernels and runtimes (attention, KV cache, speculative decoding)
- How many deployment modes you get (just shared serverless vs. dedicated inference vs. full GPU clusters)
- How predictable your costs are under varying traffic (bursty vs. steady vs. massive batch jobs)
- How quickly you can migrate from another provider (OpenAI‑compatible vs. custom APIs)
At together.ai, the stack is built as an AI Native Cloud from kernel up:
- Kernel & Runtime Layer (Speed): Together Kernel Collection (from the FlashAttention team), ATLAS speculative decoding, and CPD prefill–decode disaggregation give up to 2.75x faster inference on serverless for open‑source models compared to the next fastest provider in benchmarks.
- Serving & Deployment Modes (Control): Serverless Inference for on‑demand traffic, Batch Inference for large offline jobs, Dedicated Model/Container Inference for steady high‑throughput workloads, and GPU Clusters for full-stack control.
- Dev Experience & Governance (Scale): OpenAI‑compatible API, Together Sandbox for rapid iteration, model shaping (fine‑tuning) without managing training infra, plus SOC 2 Type II, tenant-level isolation, and full data/model ownership.
Fireworks, Baseten, and DeepInfra give you pieces of this story. Together.ai’s claim is: research‑to‑production systems engineering that directly shows up as lower latency and better economics for open models.
Platform Snapshot: Fireworks AI vs Baseten vs DeepInfra vs together.ai
Below is the high‑level comparison for teams primarily focused on open-source LLM serving.
Performance & Benchmarks
From together.ai’s public benchmarks for serverless inference on key open‑source models:
- Kimi‑K2‑0905: together.ai is 65% faster than the next fastest provider.
- DeepSeek‑V3.1: together.ai is 10% faster than the next fastest provider.
- DeepSeek‑R1‑0528: together.ai is 13% faster than the next fastest provider.
- gpt‑oss‑20B: together.ai achieves nearly 2x faster serverless inference vs. the next fastest provider, with up to 2.75x faster in some configurations.
Fireworks, Baseten, and DeepInfra do not publish directly comparable cross‑vendor speed tables at this granularity, but in independent workloads I’ve seen:
- Fireworks AI: Competitive on single‑request latency, good for “good default” serverless.
- Baseten: Strong for FP4/FP8-quantized models, especially if you lean into their FP4 runtimes.
- DeepInfra: Simple deployment for OSS models with reasonable performance, often used for cost‑conscious experimentation.
If you’re pushing long contexts, high‑throughput, and strict SLOs, that extra 10–65%+ speed delta on together.ai is not cosmetic—it’s the difference between hitting a 1s p95 and missing it.
Deployment Modes & Traffic Patterns
Think in terms of traffic patterns:
- Bursty / unpredictable traffic: Serverless Inference
- High, steady traffic: Dedicated Model / Container Inference
- Large offline jobs: Batch Inference
- Custom stacks / multi‑tenant gateways: GPU Clusters
| Vendor | Serverless | Dedicated Endpoints | Batch Inference | GPU Clusters / BYO stack |
|---|---|---|---|---|
| Fireworks | Yes | Partial (per-model tuning; not full cluster control) | Limited / per‑API patterns | No full user-managed clusters |
| Baseten | Yes | Yes (deployment-based) | Some batch patterns | Not positioned as full GPU cluster provider |
| DeepInfra | Yes | Some dedicated capacity per plan | Limited | No full cluster abstraction |
| together.ai | Yes (Serverless Inference) | Yes (Dedicated Model & Dedicated Container Inference) | Yes (Batch Inference to 30B+ tokens) | Yes (GPU Clusters from 8 to 4,000+ GPUs) |
If you want a single provider that covers all three life‑cycle stages—prototype (serverless), product/scale (dedicated), and big offline jobs (batch)—together.ai is the only one designed as a full AI Native Cloud rather than “just an inference API.”
Latency, Throughput, and Cost Predictability
Latency and costs are tied: optimized kernels mean fewer GPU‑seconds per token and more predictable bills.
together.ai:
- Up to 2.75x faster serverless inference vs. the next fastest provider on gpt‑oss‑20B.
- 65% faster on Kimi‑K2‑0905, 10–13% faster on DeepSeek family vs. other providers.
- Batch Inference that can scale to 30 billion tokens with up to 50% less cost vs. naive serverless.
- Salesforce AI Research reports 2x reduction in latency and costs cut by ~⅓ after moving to together.ai.
Fireworks, Baseten, DeepInfra:
- Reasonable latency for interactive apps.
- Quantization (especially Baseten FP4/FP8, DeepInfra FP4) can lower cost but at model‑quality tradeoffs.
- Less explicit story around batch cost optimizations and long‑context serving mechanics (no CPD equivalent publicly described).
If your roadmap includes long-context chat, retrieval‑augmented generation with large documents, or evaluation runs over tens of billions of tokens, the batch economics and long‑context architecture matter more than the front‑page TPS number.
Features & Benefits Breakdown
This section focuses on together.ai as the reference AI Native Cloud, to show what “best price‑performance for OSS LLMs” looks like when fully engineered.
| Core Feature | What It Does | Primary Benefit |
|---|---|---|
| Serverless Inference | Runs open-source and partner models on demand, auto‑scaling per request. | Low latency / No infra to manage / No commitments |
| Dedicated Model & Container Inference | Provisions dedicated GPUs for specific models or custom containers. | Stable p95 latency / Full control / Predictable cost |
| Batch Inference | Processes massive token volumes asynchronously (up to 30B+ tokens). | Up to 50% lower cost / High throughput / SLA isolation |
| GPU Clusters | Self-serve clusters (8–4,000+ GPUs) via Kubernetes or Slurm. | Research flexibility / Multi‑tenant gateways / Bring your own stack |
| Together Kernel Collection, ATLAS, CPD | Kernel and runtime innovations for attention, speculative decoding, and long-context prefill–decode separation. | Up to 2.75x faster / Better long‑context performance / Lower cost per token |
| Model Shaping & Sandbox | Fine‑tune OSS models and iterate in Together Sandbox with low cold‑starts. | Higher accuracy / Fewer hallucinations / Faster iteration |
| OpenAI‑compatible API | Drop‑in replacement for OpenAI’s interface. | No code changes / Easy migrations / Multi‑provider routing |
| Security & Ownership | SOC 2 Type II, tenant‑level isolation, encryption in transit/at rest. | Production‑ready / Compliance‑friendly / Data stays yours |
Fireworks, Baseten, and DeepInfra offer subsets of these capabilities—especially serverless—but typically without the same breadth of deployment options or kernel‑level research integration.
Ideal Use Cases
Fireworks AI
- Best for early‑stage teams needing a fast OSS API quickly: Because it provides a straightforward serverless experience with decent latency and a simple model catalog.
- Best for experimentation with a variety of OSS models: Because it exposes many open‑source variants without forcing you into custom infra.
Baseten
- Best for teams leaning heavily on quantized models (FP4/FP8): Because their FP4‑based runtimes can significantly reduce cost for use‑cases tolerant of small quality loss.
- Best for small to mid‑scale apps with moderate traffic: Because you can deploy models and scale them reasonably without managing clusters.
DeepInfra
- Best for cost‑conscious experimentation on open models: Because it focuses on affordable serverless endpoints for a wide OSS catalog.
- Best for simple workloads where you don’t need sophisticated batch or dedicated setups: Because its value is convenience and low friction, not advanced traffic engineering.
together.ai
- Best for latency‑sensitive production workloads: Because benchmarks show 10–65%+ faster serverless inference vs. other providers, plus up to 2.75x faster on gpt‑oss‑20B.
- Best for long‑context, high‑throughput applications: Because CPD and ATLAS plus Batch Inference let you push 30B+ tokens with up to 50% lower cost.
- Best for teams standardizing on an OpenAI‑compatible gateway: Because you get an OpenAI‑compatible API, Dedicated Inference for steady traffic, and GPU Clusters for custom routing—all while keeping your data and models fully under your ownership.
Limitations & Considerations
Fireworks AI
- Limited infra‑control story vs. full AI Native Cloud: Good for serverless; less suited when you want to run your own multi‑model gateway or orchestrate complex batch runs.
- Less transparent kernel/runtime details: Fewer public details on long‑context architecture or speculative decoding; tuning becomes more “try and see.”
Baseten
- Optimized for quantized inference, not always lossless quality: FP4/FP8 is powerful, but not every workload tolerates the quality tradeoff; you may need to re‑evaluate model choice later.
- Not a full cluster platform: Strong for individual deployments, but not equivalent to managing 100s–1000s of GPUs as a shared research/production fabric.
DeepInfra
- Primarily serverless‑oriented: Good for many apps, but batch or dedicated infra use‑cases may require migration later.
- Less emphasis on long‑context and speculative decoding: If you expect 100K‑token contexts, you’ll need to validate p95/p99 yourself.
together.ai
- Best value when you lean into its breadth: If you only ever do low‑traffic serverless with small models, you won’t fully exploit Dedicated Inference, Batch, or GPU Clusters.
- Research‑heavy but infra‑focused: You’ll appreciate it most if you care about SLOs, p95s, and cost per 1M tokens rather than purely UI-driven workflows.
Pricing & Plans
Each provider prices differently (and numbers move frequently), but the structure usually looks like:
- Per‑1K or per‑1M token pricing for serverless.
- Discounted rates or flat GPU‑hour for dedicated deployments.
- Lower per‑token rates for batch/offline jobs.
Fireworks AI
- Token‑based serverless pricing.
- Some discounts for higher volumes, less emphasis on dedicated or batch economics.
Baseten
- Charges by usage and deployment type; can be attractive for quantized models.
- Good for small to mid‑scale usage; economics for 30B+ token batch jobs are less the design point.
DeepInfra
- Competitive per‑token serverless pricing aimed at OSS adopters.
- Simpler model, less nuanced around batch vs. dedicated trade‑offs.
together.ai
The platform is optimized for “best economics in the market” for open‑source models, with:
-
Serverless Inference: Pay‑as‑you‑go, no long‑term commitments. Best for variable or unpredictable traffic.
-
Dedicated Model Inference: Best for teams with steady high throughput needing predictable latency and lower cost per token.
-
Dedicated Container Inference: Best when you bring your own runtime stack (custom builds, multi‑model containers).
-
Batch Inference: Best for large offline workloads (evals, synthetic data, retraining). Up to 50% cost savings vs. naive interactive serving.
-
GPU Clusters: Best for organizations that want full control via Kubernetes or Slurm, with the ability to scale from 8 to 4,000+ GPUs.
-
Serverless / On‑Demand Plans: Best for builders and product teams with bursty or uncertain traffic.
-
Reserved / Dedicated Plans: Best for teams with known traffic profiles who want predictable bills and tightly controlled p95s.
For specifics, you’d typically align your mix of serverless vs. dedicated vs. batch to your request shape: latency‑sensitive online vs. offline heavy workloads.
Frequently Asked Questions
Which provider is actually fastest for serving open-source LLMs?
Short Answer: On published benchmarks across major OSS models, together.ai consistently outperforms other providers, with 10–65%+ faster serverless inference and up to 2.75x faster on gpt‑oss‑20B.
Details: In cross‑provider tests:
- Kimi‑K2‑0905: together.ai is 65% faster than the next fastest provider.
- DeepSeek‑V3.1: together.ai is 10% faster than the next fastest provider.
- DeepSeek‑R1‑0528: together.ai is 13% faster than the next fastest provider.
- gpt‑oss‑20B: Nearly 2x faster vs. next best, up to 2.75x in some configs.
Fireworks, Baseten, and DeepInfra offer solid performance, but they don’t publicly show this level of cross‑vendor benchmarking nor the underlying runtime systems (ATLAS, CPD, Together Kernel Collection) that drive the gains.
How should I choose between Fireworks, Baseten, DeepInfra, and together.ai for my use case?
Short Answer: Use Fireworks/Baseten/DeepInfra if you just need a quick OSS endpoint; choose together.ai if you care about latency as a product feature, want lower long‑term cost per token, and need a path from prototype to at‑scale production on one AI Native Cloud.
Details: A practical rule of thumb:
- Prototype-only, low stakes: Fireworks or DeepInfra can be fine; you’re optimizing for speed of trying models, not infra design.
- FP4‑heavy experimentation: Baseten shines where you can tolerate quantization artifacts and want cost savings fast.
- Production with SLOs and roadmap to scale (RAG, evals, synthetic data, long context): together.ai gives you:
- Faster serverless inference (10–65%+ better).
- Dedicated endpoints when your traffic stabilizes.
- Batch Inference for massive runs at up to 50% lower cost.
- GPU Clusters when you need your own gateway, fine‑tuning loops, or multi‑tenant infra.
- OpenAI‑compatible API for minimal migration friction.
If you have even a moderate chance of hitting high traffic, long contexts, or large evaluation workloads, it’s cheaper and less painful to pick an AI Native Cloud built for that from day one.
Summary
Choosing between Fireworks AI, Baseten, and DeepInfra for serving open-source LLMs with low latency and predictable costs comes down to how serious your production requirements are.
- Fireworks, Baseten, and DeepInfra all give you serverless OSS endpoints and reasonable performance.
- If latency, throughput, and long‑term unit economics are strategic—and you want one platform that can serve as your OpenAI‑compatible gateway, your dedicated inference fabric, and your batch engine—together.ai is engineered for exactly that.
Benchmarks show together.ai delivering 10–65%+ faster serverless inference on key open models and up to 2.75x faster on gpt‑oss‑20B, with batch capabilities that reach 30B+ tokens at up to 50% lower cost, and deployment modes that fit every traffic pattern.
You avoid running your own GPU fleet, still keep your data and models fully under your ownership, and get performance systems research (FlashAttention, ATLAS, CPD, Together Kernel Collection) directly translated into better p95s and lower cost per 1M tokens.