Open-source alternatives to Fireworks AI: 5 hosted LLM inference tools compared
Replacing Fireworks AI with software you host means the serving stack, the model catalogue and the behaviour of the endpoint all become your decisions, and the trade is that prompts and the documents inside them stop leaving your network. Most readers should look at vllm-project/vllm first, because it is the candidate here that exposes an OpenAI-compatible server over the widest set of model architectures.
What Fireworks AI does, and why people look elsewhere
Fireworks AI is a hosted inference service for open-weight models: it serves them over an OpenAI-compatible API and adds function calling, structured output and its own tuning options.
- the serving stack runs on the vendor's hardware and its own kernels
- prompts and the documents inside them are processed on the vendor's servers
- the model catalogue and the API's behaviour are the vendor's, so an endpoint that answers a certain way today can answer differently later
The candidates at a glance
| Project | Licence | Maintenance | Language | GitHub stars | Install |
|---|---|---|---|---|---|
| vllm-project/vllm | Apache-2.0Permissive: commercial use allowed | Commits in the last dayLast push October 23, 2026 | Python | 95,239 | Command documented |
| sgl-project/sglang | Apache-2.0Permissive: commercial use allowed | Commits in the last dayLast push October 23, 2026 | Python | 37,671 | Command documented |
| NVIDIA/TensorRT-LLM | Custom licenceCustom licence: read the LICENSE file | Commits in the last dayLast push October 23, 2026 | Python | 14,931 | See analysis |
| InternLM/lmdeploy | Apache-2.0Permissive: commercial use allowed | Commits in the last dayLast push October 23, 2026 | Python | 8,115 | See analysis |
| triton-inference-server/server | BSD-3-ClausePermissive: commercial use allowed | Commits in the last six monthsLast push October 4, 2026 | Python | 11,043 | Command documented |
Each alternative in detail
vllm-project/vllmA high-throughput and memory-efficient inference and serving engine for LLMs.
- Best for
- A team with one or more accelerators that wants an OpenAI-compatible server in front of a specific model and needs the widest set of supported architectures behind it.
- What you give up
- The install is the hard part. The build pins torch to an exact version, caps setuptools below a fixed release, and a source build needs cmake, ninja, setuptools-rust and a Rust toolchain. There is no tuner here either, so a model that needs fine-tuning still needs a separate tool.
- Moving from Fireworks AI
- vLLM exposes an OpenAI-compatible API server, plus Anthropic Messages API and gRPC support, so a client written against a hosted OpenAI-compatible endpoint can be repointed at the local process by changing the base URL.
- First install step
- Full setup guide
uv pip install vllm
sgl-project/sglangSGLang is a high-performance serving framework for large language models and multimodal models.
- Best for
- A team serving agentic traffic or running reinforcement learning rollouts across several nodes, where structured generation and multi-node throughput decide the workload.
- What you give up
- The launch command, the KV cache project and the RL trainer live in other repositories, so a working deployment takes more than the container image. There is no Windows install path, and the image tag in the install line pins nothing at all.
- First install step
- Full setup guide
docker pull lmsysorg/sglang:latest
NVIDIA/TensorRT-LLMTensorRT LLM provides users with an easy-to-use Python API to define Large Language Models (LLMs) and supports state-of-the-art optimizations to perform inference efficiently on NVIDIA GPUs. TensorRT LLM also contains components to create Python and C++ runtimes that orchestrate the inference execution in a performant way.
- Best for
- A team already committed to NVIDIA GPUs that wants a Python API over specialised kernels and an efficient runtime, and can pin CUDA 13.2.1 with PyTorch 2.12.0 in its environment.
- What you give up
- The newest tags are release candidates rather than stable releases, so you are adopting a version that may still move under you. GitHub cannot classify the licence here: the licence field reports NOASSERTION while the README badge says Apache 2, so the LICENSE file has to be read before use.
InternLM/lmdeployLMDeploy is a toolkit for compressing, deploying, and serving LLMs.
- Best for
- A team that wants compression, deployment and serving inside one toolkit, particularly on NVIDIA GPUs or Huawei Ascend, and wants a C++ engine and a pure Python engine to compare.
- What you give up
- The speed comparison in the project comes from a single benchmark rather than a general result, so throughput has to be measured on your own workload. The supported model list is fixed, and PyPI wheel uploads were paused for a period, which makes environment pinning harder than it should be.
triton-inference-server/serverThe Triton Inference Server provides an optimized cloud and edge inferencing solution.
- Best for
- A team that has to serve models from more than one framework, such as TensorRT, PyTorch, ONNX and OpenVINO, behind a single endpoint and a single queue.
- What you give up
- The engine expects a model repository rather than a model name on the command line, and every framework needs its own backend repository. Upgrades follow a monthly container cadence, so an unattended deployment has to be rebuilt on that rhythm rather than whenever a release lands.
- Moving from Fireworks AI
- Triton speaks the KServe predict API v2 over HTTP/REST and gRPC, so a client written against that protocol can point at a Triton-hosted model without changing the shape of the request.
- First install step
- Full setup guide
# Step 1: Create the example model repository git clone -b r26.09 https://github.com/triton-inference-server/server.git cd server/docs/examples ./fetch_models.sh
Five answers to one question: a general engine, two tuned stacks and a multi-framework front door
Fireworks AI serves open-weight models over an OpenAI-compatible API, adds function calling and structured output, and runs its own tuning options on top. The serving stack executes on the vendor's hardware and the vendor's kernels, the catalogue of available models is the vendor's choice, and the behaviour of the endpoint belongs to the vendor too, so an endpoint that answers a certain way today can answer differently later. Replacing it means choosing what plays that role on your own machines.
The five candidates divide cleanly. vllm-project/vllm is the general engine: a serving library built around paged attention, continuous batching, chunked prefill and prefix caching, with a model architecture list that runs past two hundred entries and a single command for the wheel path. sgl-project/sglang is the runtime aimed at agentic workloads and reinforcement learning rollouts, written in Python and Rust, with disaggregated prefill and decode and a cache-aware load balancer. NVIDIA/TensorRT-LLM is the tuned stack, a Python API over NVIDIA's own specialised kernels with a C++ runtime underneath, and InternLM/lmdeploy is the compression and deployment toolkit built around the TurboMind engine, including KV cache quantization and a pure Python engine for experiments.
triton-inference-server/server sits sideways to all four. It is a front door for many frameworks rather than a model server for one, with a Backend API, Python backends, ensembling and business logic scripting.
What none of them can replace is a catalogue. There is no hosted registry here, no vendor-managed model list, and no tuning console. A team that wants both a place to publish weights and a place to serve them keeps two of the five and adds something else.
Matching the hosted endpoint's behaviour is the part nobody hands you
Function calling, structured output, streaming and the exact shape of an error response are the properties a hosted API gives you as part of the package. When you run the endpoint yourself, you own all four, and the candidates help in different ways.
vllm-project/vllm comes closest to a drop-in shape. It serves tool calling and reasoning parsers, generates structured output through xgrammar or guidance, streams outputs, and supports multi-LoRA serving for dense and mixture-of-experts layers. NVIDIA/TensorRT-LLM pairs guided decoding with speculative decoding and has published work on trace replay and job-level metrics for agentic serving. sgl-project/sglang pushes on the same axis with its cache-aware load balancer, its DFlash and Spec V2 speculative decoding work, and day-0 support for new model releases.
triton-inference-server/server answers a different question: how several models share one endpoint. It does dynamic batching and sequence batching, keeps implicit state for stateful models, and can compose models into an ensemble or drive them with business logic scripting. InternLM/lmdeploy adds tool calling on top of its TurboMind engine and a proxy server for multi-model, multi-machine deployments.
The honest summary is that parity is per endpoint, not per project. Whatever you choose, write the same client against both the hosted service and your own process, then compare the failures: how a malformed tool call is reported, what happens when the model returns a truncated argument, whether a cancellation stops the token generation. That test finds more incompatibilities than any feature list.
What self-hosted serving costs: exact pins, unpinned images and a monthly container
The hosted service turns one thing into one call. Self-hosting splits it into a model on disk, a process on a GPU, a queue in front of it and a set of upgrade decisions. The candidates differ most in how much of that they hand you.
The wheel path of vllm-project/vllm is short: one uv command. The build path is not. Our analysis of vllm records that the build requirements pin torch to an exact version and cap setuptools below a fixed release, that the version string comes from a git describe filtered to v[0-9]* so a shallow or untagged checkout has nothing to derive, and that a source build needs cmake, ninja, setuptools-rust and a Rust toolchain. The image is your responsibility as a result, and the image is where the torch pin actually bites.
sgl-project/sglang installs by pulling a container image, which pins nothing, and its 0.5.x line shipped three tags in four weeks. Our analysis of sglang says to pin the exact version or image digest you intend to run, and notes that the KV cache project you run beside it is a separate decision.
NVIDIA/TensorRT-LLM constrains the environment rather than the image: the README badges show CUDA 13.2.1, PyTorch 2.12.0 and Python 3.10 or 3.12, so the machine has to line up with the versions the project compiles against. InternLM/lmdeploy is the least awkward of the five on packaging, and the project records that PyPI wheel uploads were paused and resumed in 2026-04, with a release on PyPI again.
triton-inference-server/server is the most predictable and the most demanding. The quickstart is three commands, the release is a container tag that changes monthly, and the README warns that the main branch tracks work towards the next release, so the branch you clone decides what you get.
Three permissive licences, one BSD, and one file you have to open yourself
Three of the five candidates carry Apache-2.0: vllm-project/vllm, sgl-project/sglang and InternLM/lmdeploy. triton-inference-server/server carries BSD-3-Clause, and the three-clause notice is reproduced in the repository header, which means a redistribution has to keep the copyright notice, the conditions and the disclaimer.
That matters for a hosted deployment in a specific way. A project under a copyleft licence with a network use clause, an AGPL-3.0 candidate for instance, obliges you to publish the changes to a modified version that you host for other people. None of the five on this page is under such a licence, so a patched vLLM or a modified Triton deployment can run behind your own firewall without an obligation to release the source. The Apache-2.0 grant also carries a patent licence and a requirement to keep the notice text with any redistribution, including inside a container image.
The exception is NVIDIA/TensorRT-LLM, where the metadata and the badge disagree. The GitHub licence field reports a custom licence that cannot be classified, while the README badge reads Apache 2. Our analysis of TensorRT-LLM says to read the LICENSE file and the version file in the repository before building anything on top of it. That is a real step, not a formality, because a company deciding how to distribute a modified build needs to know the terms rather than guess from a badge.
And every candidate inherits a licence problem it does not solve. The base model weights carry their own terms, the serving engine says nothing about them, and if you tune a model on data you collected, that data's provenance is your question to answer.
Choosing by scenario, and where DeepInfra and Nebius AI readers land
A developer with one GPU box starts at vllm-project/vllm, because it is the one candidate here where the install is a single command and the endpoint is an OpenAI-compatible server you can point an existing client at. A small team with a fixed model and predictable traffic makes the same choice and then fixes the image digest so the next machine comes up identical.
A company with compliance requirements should weigh the second half of the trade most carefully. The gain is that prompts and the documents inside them are processed on hardware you control. The cost is that the serving queue, the health checks and the upgrade calendar are now internal work, and the pinned torch version in the vLLM build is a dependency your own release process has to carry.
A team serving agentic workloads or running rollouts reads the SGLang section first and checks the KV cache project and the RL trainer, because those pieces live elsewhere. A team already standardised on NVIDIA GPUs and willing to own a CUDA and PyTorch pin reads the TensorRT-LLM section, and it should open the LICENSE file before anything else. A shop with Ascend hardware, or one that cares mainly about quantization, reads the LMDeploy section, and it should benchmark on its own workload rather than trust the single comparison the project publishes. A team running several frameworks behind one endpoint reads the Triton section, and it should plan for a monthly container rebuild.
The same shortlist answers the DeepInfra and Nebius AI questions, because the job is identical: serve open-weight models from hardware you control. Where each candidate is the weaker choice is specific. Triton is the wrong tool if you want a chat endpoint and nothing else. TensorRT-LLM is the wrong tool if you need more than one vendor of accelerator or a stable tagged release today. LMDeploy is the wrong tool if your model is not on its list. SGLang is the wrong tool for a single machine, and vLLM is the wrong tool when the missing piece is tuning.
Bottom line
Choose vllm-project/vllm when you want one OpenAI-compatible server over the largest catalogue of architectures, sgl-project/sglang when the workload is agentic or rollout heavy, NVIDIA/TensorRT-LLM when the fleet is NVIDIA and the licence file checks out, InternLM/lmdeploy when quantization is the goal, and triton-inference-server/server when several frameworks share one endpoint. Before committing, pin the exact torch version and image digest, read the licence file for the stack you picked, and compare the failure behaviour of your existing client against the local endpoint.
Sources
- Fireworks AI official site
- vllm-project/vllm repository
- vllm-project/vllm README
- sgl-project/sglang repository
- sgl-project/sglang README
- NVIDIA/TensorRT-LLM repository
- NVIDIA/TensorRT-LLM README
- InternLM/lmdeploy repository
- InternLM/lmdeploy README
- triton-inference-server/server repository
- triton-inference-server/server README
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