Together AI Alternatives 2026: 10 GPU Cloud Options for Inference and Fine-Tuning

Together AI built something genuinely useful. You get an OpenAI-compatible inference API, access to 200+ open-source models without provisioning any infrastructure, fast time-to-first-token on popular models, and predictable per-token billing. For prototyping, low-volume inference, or teams that want to access a large model catalog immediately, it is a reasonable starting point.

The problems show up at scale. Together AI charges $0.88 per 1M output tokens for Llama 3.3 70B. A single H100 running vLLM with continuous batching at moderate concurrency generates around 400 tokens per second, which is 34.56M tokens per day. At Together AI's output-only rate, that comes to $30.41/day. But you are also paying for the prompt tokens. At a 3:1 prompt-to-completion ratio, the effective daily spend quadruples to around $121/day. A Spheron H100 PCIe at $2.01/hr runs $48.24/day total, less than half that cost once prompt tokens are factored in. Add in fine-tuning limitations (Together AI runs training jobs on shared infrastructure with no raw checkpoint access, no multi-node LoRA, and no BYO-GPU option) and the constraints of shared tenancy at peak hours, and the reasons to look elsewhere become concrete. Together AI also launched Together Instant GPU Clusters, their on-demand GPU product, in 2025 at $3.49/hr for H100. That is 74% more than a bare-metal H100 PCIe on Spheron at $2.01/hr.

Three categories typically drive developers to evaluate alternatives: per-token billing that stops being efficient above moderate daily token volumes, fine-tuning requirements that need full infrastructure control, and production endpoints where shared-API rate limits and unpredictable latency spikes are not acceptable.

Why Developers Look Beyond Together AI

Per-token billing at scale

The math is straightforward once you put it in a table. For Llama 3.3 70B at $0.88 per 1M output tokens, a Spheron H100 PCIe at $2.01/hr ($48.24/day) is the comparison point:

This table covers output tokens only. At a typical 3:1 prompt-to-completion ratio, the crossover point drops to roughly 14M output tokens per day, which is around 162 tokens per second of sustained generation. Most production inference endpoints cross that threshold quickly once traffic picks up.

Fine-tuning control

Together AI's managed fine-tune API handles LoRA on their infrastructure. You submit a job, they run it, you get a fine-tuned model back. What you cannot do: run on a GPU you control, access intermediate checkpoints, run multi-node jobs, use GRPO or DPO on large models (which require at least 2x H100s for 70B), or guarantee your training data does not leave their environment. For teams building on proprietary data or running custom training loops with non-standard CUDA extensions, that is a hard ceiling. For a full breakdown of fine-tuning infrastructure options, see How to Fine-Tune LLMs in 2026.

Shared tenancy and throughput limits

Together AI's API has rate limits. Popular models, especially Llama 3.1 and 3.3 70B, queue at peak hours. The latency variance at p99 is meaningful for user-facing production APIs. You are sharing GPU capacity with every other Together AI customer, which means your throughput depends on their overall load. For batch processing or async workflows, this does not matter. For synchronous APIs where a p99 response time of under 500ms is a product requirement, shared serverless endpoints are a liability.

How to Evaluate Alternatives

Five dimensions drive most alternative decisions:

1. Pricing model. Serverless per-token, per-minute bare metal, or reserved capacity. The right choice depends on your daily token volume and utilization pattern. Variable traffic with long idle windows favors serverless; sustained high throughput favors dedicated.

2. Supported models and BYO model. Does the provider let you load arbitrary HuggingFace checkpoints, or only a curated list? Fine-tuned models and custom merges require full infrastructure access.

3. Fine-tune support. LoRA vs full fine-tune, multi-node support, checkpoint storage, and data residency requirements. Managed fine-tune APIs trade control for convenience.

4. Region coverage. EU/APAC capacity matters for latency and compliance. Not all providers have geographic diversity.

5. Throughput SLAs and cold starts. For production APIs, cold start latency on the first request to an idle container can be 10-60 seconds. Dedicated instances have zero cold start.

Quick Comparison: Together AI vs Top Alternatives

GPU rates fetched 30 Apr 2026 and fluctuate with availability. Check current Spheron pricing for live rates. Third-party rates are based on publicly listed on-demand prices as of 30 Apr 2026.

Now let's break down each one.

1. Spheron: Bare-Metal GPU at Lower Cost Than Together AI

H100 PCIe: $2.01/hr | B200 (spot): $2.06/hr | A100: $1.04/hr | Per-minute billing | No contracts

Pricing as of 30 Apr 2026. Rates fluctuate with GPU availability.

Spheron aggregates bare-metal GPU capacity from vetted data center partners across multiple regions. You get a dedicated GPU instance with root SSH access, full control over the software stack, and per-minute billing with no minimum commitment. Together Instant GPU Clusters offers H100 at $3.49/hr. H100 rental on Spheron starts at $2.01/hr for PCIe, which is 42% less than Together Instant GPU Clusters for the same hardware class.

For sustained inference workloads, the economics are clear. For fine-tuning, the difference is more than price: you control the hardware, the checkpoints, and the data environment. Run any model from HuggingFace, any custom CUDA extensions, and any multi-GPU configuration up to 8x H100 with InfiniBand interconnect. If your training data cannot leave your own infrastructure, bare-metal is the only viable option.

What Spheron does well

• Lowest published H100 on-demand rate in the market
• Full root access, no container abstraction overhead
• GPU availability across multiple data center partners reduces out-of-stock risk
• Per-minute billing: no rounding up to the hour
• No contracts or minimum usage requirements
• OpenAI-compatible endpoint setup with vLLM takes under 10 minutes (see migration guide below)

Where it falls short

• No serverless auto-scaling endpoint (pair with a load balancer or inference router for burst traffic)
• Smaller community and fewer third-party tutorials compared to RunPod

Who should choose Spheron over Together AI

Teams generating 14M+ output tokens per day on a single model, teams fine-tuning on proprietary data, and production endpoints where p99 latency must be controlled. At 50M daily output tokens with a 3:1 prompt:completion ratio, Together AI costs $176/day versus $48.24/day on Spheron H100 PCIe, a difference of $128/day, compounding to $3,800+ per month.

2. Fireworks AI: Serverless, OpenAI-Compatible, Competitive Token Rates

Fireworks AI is the serverless alternative closest to Together AI's product. Both offer OpenAI-compatible endpoints, a large open model catalog, and per-token billing. Fireworks AI generally offers lower per-token rates on popular models and has been investing in latency optimization for production inference.

If you want to stay serverless but reduce your token costs, Fireworks AI is the most direct drop-in. The migration is a URL change and an API key swap. Check current per-model token rates at fireworks.ai/pricing before committing, as they update frequently.

What Fireworks AI does well

• Competitive token pricing, often 20-40% below Together AI on popular models
• OpenAI-compatible API, zero code changes to migrate
• Fast time-to-first-token on optimized models
• Compound AI system support (function calling, structured outputs)

Where it falls short

• Fine-tuning has less flexibility than bare-metal options
• No dedicated GPU access: you are still on shared serverless infrastructure

Who should choose Fireworks AI over Together AI

Teams that want serverless convenience but are cost-sensitive, and where the migration path matters (minimal code change). If token costs are the primary driver but you do not want to manage GPU infrastructure, Fireworks AI is the strongest direct alternative.

3. RunPod: Bare Metal and Serverless in One Platform

H100 SXM: ~$2.69/hr | Per-second billing | Serverless available

RunPod offers both bare-metal GPU instances and a serverless endpoint product. The same platform handles training jobs on dedicated instances and bursty inference on their serverless layer. If you want a single vendor for both use cases, RunPod covers that. H100 pricing is higher than Spheron ($2.69 vs $2.01/hr), but the unified platform experience has value for teams that want to minimize vendor count. See our RunPod alternatives guide for a side-by-side comparison of RunPod against other dedicated providers.

4. Modal: Python-Native Serverless

Modal solves the same problem as Together AI but for teams that write Python. You define a function, add a @app.function(gpu="H100") decorator, and Modal handles container scheduling, auto-scaling, and teardown. No Dockerfile, no Kubernetes. The developer experience is genuinely the best in the serverless GPU category.

The tradeoff: effective H100 rate under sustained load is around $3.95/hr (higher than Together AI's Together Instant GPU Clusters), Modal's SDK is the only way to run Modal-decorated functions (switching costs are real), and cold starts range from a few seconds to over a minute for large models. For burst inference with long idle periods, Modal's pay-per-second billing beats dedicated rentals. See our Modal alternatives guide for a detailed breakdown.

5. Lambda Labs: Research Lab Standard

H100 PCIe: from $2.49/hr | H100 SXM: from $3.78/hr

Lambda has the longest track record in managed GPU cloud and maintains strong relationships with academic institutions. GPU inventory is solid for H100s. Reserved pricing is available for multi-week runs. The tradeoff is cost: Lambda's on-demand rates run 1.2x to 1.9x Spheron's for the same hardware (H100 PCIe from $2.49/hr vs Spheron's $2.01/hr, H100 SXM from $3.78/hr), and month-to-month rates are significantly higher than reserved rates, so you are effectively paying a flexibility tax. For teams fine-tuning with Lambda, see our Lambda Labs alternatives guide for how it compares to more cost-efficient options.

6. Replicate: Simplest API, Highest Token Costs

Replicate abstracts GPU infrastructure behind a single API call. You reference a model by its HuggingFace ID, Replicate handles the rest. This is the fastest path from zero to a running model for prototyping. The cost is the highest on this list for sustained inference. Per-second billing on GPU time makes it difficult to predict monthly costs for variable workloads. Good for hackathons and MVP testing; not cost-efficient for production.

7. Cerebrium: Serverless Inference with Fine-Tune Support

Cerebrium positions itself as a serverless GPU platform with more fine-tuning flexibility than Together AI or Fireworks AI. You can deploy custom models, run LoRA fine-tunes, and use their serverless infrastructure for inference without managing containers. Cold start times are competitive with other serverless options. Per-second GPU billing with scale-to-zero makes it cost-efficient for bursty traffic.

The fine-tune story is more flexible than Together AI's but still managed: you do not get raw hardware access or multi-node training. For teams that want serverless convenience but need to use their own fine-tuned model in production, Cerebrium is worth evaluating.

8. Baseten: Model Serving with TensorRT and Triton

Baseten specializes in model serving with production-grade inference tooling: TensorRT-LLM optimization, Triton Inference Server, and auto-scaling inference endpoints. If you care about squeezing maximum throughput out of a given model (FP8 quantization, continuous batching, speculative decoding), Baseten's deployment tooling is more polished than the general-purpose serverless platforms. Teams that have already done model optimization work and want an inference serving layer should evaluate it seriously.

9. Lepton AI: Serverless with Dedicated GPU Options

Lepton AI offers both serverless inference endpoints and dedicated GPU instances, which makes it a closer feature match to the full Together AI product (including Together Instant GPU Clusters). The serverless layer supports OpenAI-compatible endpoints and scales to zero. The dedicated GPU option gives you more control without switching vendors. Pricing is competitive with the serverless tier; check lepton.ai/pricing for current GPU rates.

10. Hyperstack: Bare Metal with EU/GDPR Data Residency

Hyperstack operates data centers in the UK and EU, making it the clearest choice for teams with GDPR data residency requirements. H100 instances run around $2.99/hr on-demand. You get bare-metal access, full root SSH, and compute that stays within the EU. For US-based teams, Hyperstack is not the most cost-efficient option. For EU-based teams or US companies with EU customer data requirements, it solves a compliance problem that most other providers on this list cannot address. See our Hyperstack alternatives guide for more context on the European GPU cloud market.

Bare Metal vs Serverless: When Each Wins

When Together AI's serverless wins:

• Daily output token volume under roughly 14M output tokens per day (accounting for 3:1 prompt:completion ratios; the output-token-only crossover is ~55M tokens/day)
• Variable traffic with long idle windows: pay only when you call the API
• Teams that need access to 200+ models without hosting each one
• Rapid prototyping with no infrastructure budget or operational expertise
• Workloads where model variety matters more than per-token cost

When dedicated H100 on Spheron wins:

• Sustained throughput above 14M output tokens per day on a single model (output-only crossover is ~55M tokens/day)
• Fine-tuning with data that cannot leave your own environment
• Production SLAs where cold starts cause user-facing latency or where p99 latency must be controlled
• Running 2-3 smaller models co-hosted on a single instance (L40S or A100 for 7B-13B models)
• Workloads already paying for continuous GPU time regardless of utilization

The cost math for the crossover point:

A single H100 PCIe running vLLM FP8 with Llama 3.3 70B:
  ~400 tokens/sec at moderate concurrency
  = 1.44M tokens/hour
  = 34.56M tokens/day (output only)

Spheron rate: $2.01/hr x 24 = $48.24/day

At Together AI's output rate ($0.88/1M tokens):
  34.56M x $0.88/1M = $30.41/day (Together AI cheaper on output-only at this volume)

Output-token-only crossover: ~55M tokens/day (~636 tokens/sec sustained)

With 3:1 prompt:completion ratio:
  Effective cost: ~$3.52/1M completion tokens
  Crossover drops to ~14M output tokens/day (~162 tokens/sec)

For a deeper look at the serverless vs dedicated decision framework, see Serverless GPU vs On-Demand vs Reserved.

Migration Guide: Together AI to Spheron with vLLM

This guide covers the steps to move from Together AI's API to a self-hosted vLLM endpoint. If you are currently running Ollama instead of a serverless API, see Ollama vs vLLM first to understand the performance and setup differences.

Step 1: Provision an H100 instance

Go to app.spheron.ai, select H100 PCIe 80GB or H100 SXM5 80GB, and deploy Ubuntu 22.04. SSH access is available immediately after provisioning. For SSH setup, see the Spheron SSH connection guide.

Step 2: Install vLLM

pip install vllm

Step 3: Launch your model with an OpenAI-compatible server

vllm serve meta-llama/Meta-Llama-3.3-70B-Instruct \
  --dtype fp8 \
  --max-model-len 32768 \
  --host 0.0.0.0 \
  --port 8000 \
  --api-key your-secret-key

Authentication warning: always set --api-key with a strong secret when binding to 0.0.0.0. Without it, vLLM requires no authentication at all. Any request reaching port 8000, with or without an Authorization header, will be served.

For production setup including systemd service configuration, tensor parallelism flags, and authentication, see the Spheron vLLM server guide.

Step 4: Change two lines in your application code

from openai import OpenAI

# Before (Together AI)
client = OpenAI(
    base_url="https://api.together.xyz/v1",
    api_key="your-together-api-key"
)

# After (Spheron + vLLM) - only 2 values change
# Warning: http:// is only safe on localhost or a private VPC network.
# For public internet access, terminate TLS with NGINX and use https:// instead.
# Use the same secret key you passed to --api-key when starting vLLM.
client = OpenAI(
    base_url="http://YOUR_H100_IP:8000/v1",
    api_key="your-secret-key"
)

# All other API calls stay identical
response = client.chat.completions.create(
    model="meta-llama/Meta-Llama-3.3-70B-Instruct",
    messages=[{"role": "user", "content": "Hello"}]
)

For production hardening of this setup (NGINX reverse proxy, systemd service, and monitoring), see Build a Self-Hosted OpenAI-Compatible API with vLLM. For multi-GPU tensor parallelism and FP8 configuration, see vLLM Multi-GPU Production Deployment 2026.

Decision Matrix: Which Alternative for Which Use Case

Together AI's serverless API is convenient for prototyping, but at sustained throughput the per-token math points toward dedicated hardware. Spheron's H100 PCIe starts at $2.01/hr with per-minute billing, no contracts, and full root access. Run any model, including ones you have fine-tuned yourself.

Rent H100 → | Rent B200 → | View all GPU pricing →

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